WO2020075793A1 - Distinguishing device, information processing device, program, teacher data, and distinguishing method - Google Patents

Distinguishing device, information processing device, program, teacher data, and distinguishing method Download PDF

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Publication number
WO2020075793A1
WO2020075793A1 PCT/JP2019/039960 JP2019039960W WO2020075793A1 WO 2020075793 A1 WO2020075793 A1 WO 2020075793A1 JP 2019039960 W JP2019039960 W JP 2019039960W WO 2020075793 A1 WO2020075793 A1 WO 2020075793A1
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Prior art keywords
seeds
seed
double
color
value
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PCT/JP2019/039960
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French (fr)
Japanese (ja)
Inventor
美奈 時安
福島　啓吾
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広島県
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Priority to JP2020502241A priority Critical patent/JP6731683B1/en
Publication of WO2020075793A1 publication Critical patent/WO2020075793A1/en

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    • A—HUMAN NECESSITIES
    • A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
    • A01C—PLANTING; SOWING; FERTILISING
    • A01C1/00—Apparatus, or methods of use thereof, for testing or treating seed, roots, or the like, prior to sowing or planting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00—General purpose image data processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics

Definitions

  • the present invention relates to a discrimination device, an information processing device, a program, teacher data, and a discrimination method.
  • Stock as a cut flower is one of the flowers that has been cultivated actively and has been favored by consumers.
  • the stock is a cruciferous flower that blooms from October to March and is an annual plant that grows to a height of about 20 cm to about 80 cm.
  • Patent Document 1 discloses a method of discriminating between single-flowering and double-flowering by delaying the growth of single-flowering seeds with respect to double-flowering seeds by coating stock seeds.
  • One aspect of the present invention has been made in view of the above problems, and an object thereof is to enable double identification in a seed state for various stock varieties.
  • the distinguishing apparatus is a distinguishing apparatus for distinguishing the double-flowered seeds from a plurality of stock seeds before distinguishing the double-flowered seeds, and the plurality of the From the light information of the reflected light from the stock seeds, for each of the plurality of stock seeds, an information acquisition unit that acquires at least color tone information indicating a color tone value of a predetermined color component included in the seed color, and The tone value of the predetermined color component of the seeds of the plurality of stocks, each of which is ranked in descending order of the value, for each of the seeds of the plurality of stock, at least the order of the tone value of the predetermined color component is predetermined.
  • a determining unit that determines whether or not the seed of the specific stock is the double-flowered seed by determining whether or not it is included in the first range.
  • the teacher data is an information acquisition unit that acquires at least the characteristic information indicating the characteristics of the seed from the image data of the seed for each of the seeds of the plurality of stocks before distinguishing the double-flowered seeds.
  • a discrimination device that machine-learns to discriminate double-flowered seeds from the seeds of the plurality of stocks, is teacher data for building or updating a learning model, and features of the seeds of the plurality of stocks.
  • Information, the information acquisition unit of the identification device is used for at least the process of acquiring, image data of the seeds of the plurality of stock, and morphological data showing the morphology of the flower blooming after the growth of the seeds contained in the image data. It has a structure including ,.
  • the discrimination method is a discrimination method for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, and reflected light from the seeds of the plurality of stocks.
  • an information acquisition step of acquiring at least color tone information indicating a color tone value of a predetermined color component contained in the color of the seed, and the seeds of the plurality of stocks described above.
  • the tone values of the predetermined color components are ranked in descending order of the values, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range.
  • FIG. 1 It is a block diagram showing functional composition of an imaging device and an information processor concerning Embodiments 1 and 2 of the present invention. It is a flowchart which shows the discrimination method which concerns on Embodiment 1 of this invention. It is a flow chart which shows the modification of the discriminating method concerning Embodiment 2 of the present invention. It is a figure which shows the example of the picked-up image of a double-flowering seed and a single-flowering seed regarding Sayuri.
  • Graph 101 is a histogram showing the calculation results of a * values of seeds for Saury, and Table 102 shows that a * values of specific target seeds for Saury are among a * values of all target seeds.
  • Table 105 shows the relationship between the seed selection rate and the correct answer rate regarding whether or not the a * value of a specific target seed for iron marine is included in the lower x% of the a * values of all target seeds. It is a table, and a graph 106 is a graph showing the relationship between the seed selectivity and the correct answer rate of each of the spoilage and iron marine.
  • Table 5 is a histogram showing the calculation results of the area of seed, b * value, R value, and roundness of iron marine.
  • Table 111 is a table showing an example of the determination conditions in the determining unit for iron marine
  • Table 112 is a table showing the relationship between the seed selection rate and the correct answer rate when the determination conditions shown in Table 111 are applied.
  • Table 113 is a table showing another example of the determination condition in the determination unit for Sayuri
  • Table 114 is a table showing the relationship between the seed selection rate and the correct answer rate when the determination condition shown in Table 113 is applied. is there. It is a graph which shows the relationship between each seed selection rate and the correct answer rate of Saury and iron marine in the case of performing the double discrimination using a single or multiple characteristic amount.
  • Table 115 is a table showing the relationship between the seed selectivity and the correct answer rate of each of the vast and iron marine when the occultation was performed using the yellow hue value, and Table 116 is the occultation using the blue hue value.
  • FIG. 6 is a table showing the relationship between the seed selectivity and the correct answer rate for each of Saury and iron marine in the case of performing.
  • Table 117 relates to whether or not the specific target seed a * value for each of Saury, Iron Marine, Iron Cherry and Iron White is included in the lower X% of the a * values of all target seeds.
  • Table 121 is a table showing the seed selection rate and the correct answer rate of octet discrimination by the combination of a plurality of feature amounts for Saury
  • Table 122 is the seed selection rate of the octet discrimination by a combination of a plurality of feature amounts for iron marine. It is a table showing the correct answer rate
  • Table 123 is a table showing the seed selection rate and the correct answer rate of the double identification by the combination of a plurality of feature amounts for iron cherry
  • Table 124 is a combination of a plurality of feature amounts for Iron White. It is a table which shows the seed selection rate and the correct answer rate of the double discrimination by.
  • a graph 125 is a histogram showing the measurement result of the seed a * value with respect to spoilage
  • a graph 126 is a histogram showing the measurement result of the seed b * value with respect to spoilage.
  • the information processing device 100 examples include a PC, a tablet terminal, a seed selection robot, and the like. As shown in FIG. 1, the information processing apparatus 100 includes a display unit 1, an operation input unit 2, a storage unit 3 and a control unit 4. The information processing device 100 may not include the display unit 1. For example, when the information processing apparatus 100 is a seed selection robot that does not include a display unit, a mode in which the hand of the seed selection robot selects the identified seed and moves it to a predetermined place can be exemplified.
  • the display unit 1 displays a discrimination result by the discrimination device 10 to be described later, an image resulting from execution of various functions (application software) installed in the information processing device 100, and the like.
  • the operation input unit 2 acquires an input user operation, and if the information processing apparatus 100 is a tablet terminal, for example, a touch panel integrated with the display unit 1 is used.
  • the storage unit 3 stores various data generated by the identification device 10 described later, various control programs executed by the control unit 4, and the like, and is configured by a nonvolatile storage device such as a hard disk or a flash memory. To be done.
  • the storage unit 3 may be provided outside the information processing device 100, for example.
  • the control unit 4 controls the information processing device 100 in a centralized manner, and includes a discrimination device 10.
  • the discrimination device 10 is a device for discriminating double-flowered seeds from a plurality of stock seeds, and includes an image analysis unit 11 (information acquisition unit) and a determination unit 12 as illustrated in FIG. 1.
  • the information acquisition unit from the light information of the reflected light from the seeds of the plurality of stocks, for each of the seeds of the plurality of stocks, the color tone of a predetermined color component included in the color of the seeds. At least color tone information indicating a value is acquired.
  • the optical information is image information acquired by the imaging device 500 and the information acquisition unit is the image analysis unit 11, but the mode of the information acquisition unit is not limited to this. (See Form 3).
  • the information acquisition unit is the image analysis unit 11, it is possible to acquire information regarding the size and shape of the seed, such as information indicating the area of the seed and the degree of roundness of the cross-sectional shape of the seed, in addition to the color tone information.
  • the imaging device 500 acquires image information by receiving the reflected light that the light emitted from the white light source or the like hits the seed of the stock and is reflected.
  • control unit 4 is built in the information processing device 100, but the present invention is not limited to this case.
  • control unit 4 may be a network server used via an external device attached to the information processing device 100 or a communication unit (not shown).
  • identification device 10 may be an external device attached to the information processing device 100.
  • the seeds in the state where the seeds of the stock before distinguishing the double-flowered seeds are placed on the stage one by one are the seeds of the stock that is the target of the double identification by the distinguishing device 10.
  • target seed the seeds of the stock before distinguishing the double-flowered seeds
  • the seed of the stock before distinguishing the double-flowered seeds specifies that the seeds of the stock for which the color characteristic values and the like are ranked are a group of unselected seeds.
  • the case where the seed is photographed without placing the stock seed on the stage for example, the case where the seed is photographed in the air can be considered.
  • a method for photographing seeds in the air for example, a multipurpose shoot type optical sorter manufactured by Satake Co., Ltd. is used, and seeds are flown into the chute, and the seeds that flow through the chute and fly out into the air are photographed. Can be mentioned.
  • the image analysis unit 11 analyzes the images of the plurality of target seeds placed on the stage as a preprocessing of the double discrimination. Specifically, first, a plurality of target seeds to be image-analyzed are imaged in the same field of view from above using an imaging device 500 such as a digital camera. In addition, in the present embodiment, a mode in which a plurality of target seeds are collectively imaged will be described, but each of the plurality of target seeds may be individually imaged.
  • initial images are developed in the TIF format from the state of the RAW image by the image editing software of the image capturing apparatus 500, and are transmitted to the image analysis unit 11.
  • the image analysis unit 11 that has received the initial image in TIF format (hereinafter abbreviated as “TIF image”) performs binarization processing on the TIF image in the RGB color space to generate a binarized image.
  • the image format of the initial image is not limited to this, and for example, the JPEG format or the GIF format may be used.
  • the image analysis unit 11 calculates and acquires values (hereinafter, abbreviated as “threshold”) indicating the hue, saturation, and lightness of the boundary between the target seed and the background in the image.
  • the seed area s of the seed may be calculated and acquired during the binarization process. Then, by converting the TIF images in CIELAB color space L *, after dividing into a * and b * each image, the from the binarized image L *, designates a * and b * each image.
  • the target seed and the background in the image are segmented by the thresholds of three indexes (hue, saturation, and brightness), but the present invention is not limited to this case.
  • the present invention is not limited to this case.
  • only one index may be used for segmentation, or an index other than “hue, saturation, and lightness” may be used for segmentation.
  • the CIELAB color space is a nearly complete color space established by the International Commission on Illumination (CIE). It can represent all colors visible to the human eye and can be used as a reference for a device-specific model related to image processing.
  • CIE International Commission on Illumination
  • the CIELAB color space is composed of three coordinates L * , a * and b * .
  • L * represents the lightness of the color
  • a * represents a position between red / magenta and green, where a negative value is closer to green and a positive value is closer to red / magenta.
  • b * represents a position between yellow and blue, where a negative value is closer to blue and a positive value is closer to yellow.
  • the image analysis unit 11 transmits the calculated and acquired a * value of the seed to the determination unit 12.
  • the a * value of the seed may be transmitted to and stored in the storage unit 3.
  • the determination unit 12 determines whether or not each of the plurality of target seeds is a double-flowered seed, based on the color tone value of the predetermined color component of the seed received from the image analysis unit 11. Specifically, the determining unit 12 ranks the tone values of the predetermined color components of the seeds of the plurality of stocks in descending order of the values, and determines at least the predetermined color component of each of the seeds of the plurality of stocks. It is determined whether or not the seed of the specific stock is the double-flowered seed by determining whether or not the rank of the color tone value of is included in the predetermined first range.
  • the determination unit 12 of the present embodiment determines whether or not each of the plurality of target seeds is a double-flowered seed, based on the a * value of the seed received from the image analysis unit 11. Specifically, it is determined whether or not the a * value of the seed related to the specific target seed is included in the lower x% (first range) of the a * values of the seed related to all the target seeds. .
  • the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed. On the other hand, when it is not included in the lower x%, the determining unit 12 determines that the specific target seed is not a double flower seed, in other words, a single flower seed.
  • the correct answer rate of the double identification of quick rebirth is about 80% or more, and the correct answer rate is generally considered to be almost successful in the double identification of seeds.
  • the determining unit 12 determines that the color tone value (a * value) of the red component related to the seed of the specific stock is the lower 50% of the color value of the red component related to the seed of all the stocks as the first range. It may be determined whether or not it is included in the range. Thereby, for example, when the stock type is early, it is possible to perform double identification with a high accuracy of 80% or more.
  • the value of x may be stored in advance in the storage unit 3 or a memory (not shown) in the determination unit 12 or the like.
  • a user may input a desired numerical value according to the type of stock or the like by inputting it through the operation input unit 2.
  • the discrimination device 10 discriminates the specific target seed as a double-flowered seed, and displays the discrimination result on the display unit 1.
  • the identification device 10 identifies the specific target seed as the single-flowered seed, and causes the display unit 1 to display the identification result. .
  • the determining unit 12 may be able to change the first range according to the stock type. For example, Saury and iron marine differ in the first range in which the double discrimination can be performed properly. Therefore, by changing the first range to an appropriate range according to the type of stock, it becomes possible to improve the accuracy of double identification.
  • step 101 a TIF image is generated by the image capturing apparatus 500, and the image analysis unit 11 receives the TIF image from the image capturing apparatus 500, so that S102 is performed. Proceed to.
  • the image analysis unit 11 converts the TIF image into the CIELAB color space and divides it into L * , a *, and b * images, and proceeds to S103.
  • the image analysis unit 11 performs binarization processing on the TIF image converted into the CIELAB color space, and then the L * value, a * value, and b for each of the plurality of target seeds.
  • the * value is calculated, the a * values of the seeds related to all the target seeds are transmitted to the determination unit 12, and the process proceeds to S104.
  • the determination unit 12 determines whether the a * value of the seed is contained in the lower x% of the a * value of seed according to any of the target seeds To judge.
  • Y YES
  • the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed, and proceeds to S105.
  • the identification device 10 finally identifies the specific target seed, which has been determined to be a double-flowered seed by the determination unit 12, as a double-flowered seed.
  • the discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
  • the determination unit 12 determines that the specific target seed that is the determination target is a single-flowered seed, and proceeds to S106.
  • the identification device 10 finally identifies the specific target seed, which is determined to be a single-flowering seed by the determination unit 12, as a single-flowering seed.
  • the discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
  • the double discrimination by the discrimination device 10 is completed by finishing the processing of either S105 or S106 for all the target seeds.
  • the mode in which the a * value, which is the color tone value of the red component, is used as the color tone value of the predetermined color component as the first index for performing the double discrimination is used as the color tone value of the predetermined color component as the first index for performing the double discrimination.
  • the tone value of the predetermined color component is not limited to the a * value.
  • the tone value b * of the yellow component or the tone value B of the blue component is used, the intended effect of the present invention can be obtained. It is known (see FIG. 13).
  • the color of the stock seeds tends to be reddish more in the single-flowered seeds than in the double-flowered seeds, regardless of the variety. Therefore, by using the red color tone value (a * value) as an index showing the redness of the color of seeds, it is possible to reduce the seedling cost and at the same time, perform double identification in the seed state for various varieties of stock. Can be enabled.
  • the discrimination device 10 makes it possible to select double-flowered seeds and single-flowered seeds.
  • the parameter indicating the size or shape of the seed may be used to perform the double discrimination with high discrimination accuracy.
  • the case where the roundness r of the seed is used as a parameter representing the shape of the seed can be considered.
  • “Circularity” is an index indicating the roundness (roundness) of a shape, and it is considered that single-flowered seeds have a large circularity (roundness) and double-flowered seeds have a small circularity (long and slenderness). The circularity is calculated using Equation 1 below.
  • the image analysis unit 11 weights the a * value and further acquires area information indicating the area of the seed (seed area s). For each, it may be further determined whether or not the above area is included in the predetermined second range. Accordingly, by combining the red color tone value and the area of the seed, it is possible to further improve the accuracy of the double identification.
  • the image analysis unit 11 weights the a * value and further acquires roundness information indicating the degree of roundness (roundness r, roundness) of the cross-sectional shape of the seed. For each stock seed, it may be further determined whether or not the degree of roundness is included in the predetermined third range. Accordingly, by combining the red color tone value and the roundness information of the seed, it is possible to further improve the accuracy of the double identification.
  • the image analysis unit 11 weights the a * value and further acquires yellow color tone value information representing the color tone value of the yellow component included in the seed color. For each of the above, it may be further determined whether or not the tone value of the yellow component is included in the predetermined fourth range.
  • the b * value in the CIELAB color space can be listed . Accordingly, by combining the red color tone value and the yellow color tone value, it is possible to further improve the accuracy of the occultation.
  • the image analysis unit 11 weights the a * value and further acquires blue color tone value information indicating the color tone value of the blue component included in the seed color
  • the determination unit 12 determines the seeds of a plurality of stocks. For each, it may be further determined whether or not the tone value of the blue component is included in the predetermined fifth range.
  • the tone value of the blue component the B value in the RGB color space can be listed. Accordingly, by combining the red color tone value and the blue color tone value, it is possible to further improve the accuracy of the double identification.
  • the image analysis unit 11 weights the a * value and further obtains saturation information indicating the saturation C * of the seed, and the determination unit 12 further determines, for each of the seeds of the plurality of stocks, It may be determined whether or not the saturation C * is included in the predetermined sixth range. Accordingly, by combining the red color tone value and the saturation, it is possible to further improve the accuracy of the double identification.
  • the image analysis unit 11 of the discrimination apparatus 10 calculates and acquires the circularity r of the seed in addition to the a * value of the seed. Different from the information processing apparatus 200 also has a point that the determination unit 12 of the discrimination apparatus 10 determines whether or not each of the seed a * value and the seed circularity r falls within a predetermined numerical range. Different from 100.
  • each processing of S201, S203, and S204 is the same as each processing of S101 to S103 in the flowchart of FIG. 2, so the description thereof will be omitted.
  • the image analysis unit 11 calculates and acquires the roundness r of the seed for each of the plurality of target seeds, and proceeds to S203.
  • the image analysis unit 11 calculates and acquires the a * value of the seed for each of the plurality of target seeds, and proceeds to S205.
  • the image analysis unit 11 transmits to the determination unit 12 the seed circularity r and a * value of all target seeds.
  • the determination unit 12 determines whether or not the (I) a * value related to the specific target seed is included in the lower x% of all the a * values, and (II) circle. It is determined whether or not the degree r is included in the lower y% of all the circular degrees r.
  • the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed, and the identification device 10 determines the specific target seed that is determined to be a double-flowered seed by the determination unit 12. The target seed is finally identified as a double-flowered seed.
  • the discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
  • N NO (hereinafter abbreviated as “N”) in S205, in other words, both the a * value of the specific target seed and the circularity r fall within the numerical ranges of (I) and (II) above. If not included, the determination unit 12 determines that the specific target seed that is the determination target is a single-flowering seed, and proceeds to S207.
  • the discrimination device 10 finally discriminates the specific target seed determined to be the single-blooming seed by the determination unit 12 as the single-blooming seed.
  • the discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
  • the double identification by the identification device 10 is completed by completing the processing of either S206 or S207 for all the target seeds.
  • parameters other than circularity r include seed area s, R value in RGB color space, b * value in CIELAB color space, circularity, and saturation C * .
  • the seed area s is the area of the region surrounded by the outer circumference of the target seed when the target seed is viewed in a plan view.
  • Saturation C * is a measure of color vividness.
  • the saturation C * is calculated using the following equation 2.
  • Equation 2 [(a *) 2 + (b *) 2 ] 1/2
  • Roundness is the amount of deviation from the geometrically correct circle of a circular feature. The roundness is calculated using the following Equation 3.
  • the image analysis unit 11 calculates one of the seed area s, the R value and the B value of the RGB color space, the b * value of the CIELAB color space, the roundness, and the saturation C * instead of the circularity r. • Obtained, and the determination unit 12 determines whether the seed a * value, the seed area s, the R value or B value in the RGB color space, the b * value in the CIELAB color space, the roundness, or the saturation C * . Whether or not it is a double-flowered seed may be determined based on.
  • the octo-double discrimination is performed using two kinds of parameters, the seed a * value and the circularity r, but the present invention is not limited to this example, and three or more kinds of parameters including the seed a * value are used.
  • Fig. 4 shows an example of a picked-up image of double-flowered seeds and single-flowered seeds related to the stock of Sorei. It is difficult to understand because the captured image shown in FIG. 4 is displayed in black and white, but single-flowered seeds tend to be more reddish than double-flowered seeds.
  • the graph 101 in FIG. 5 is a histogram showing the calculation result of the a * value of seeds regarding the spoilage.
  • the histogram of the graph 101 in FIG. 5 is generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double bloom seeds and the broken line graph indicates single bloom seeds.
  • the frequency of double-flowered seeds tends to be higher than the frequency of single-flowered seeds in the range where the a * value is small (5 to 10).
  • the table 102 of FIG. 5 shows the seed selection rate and the correct answer rate regarding whether or not the a * value of the specific target seed for spoilage is included in the lower X% of the a * values of all the target seeds. It is a table which shows a relationship. As shown in the figure, it can be seen that the smaller the seed selection rate X%, the higher the correct answer rate of the double discrimination.
  • the seed selection rate is a value calculated by dividing the number of target seeds that have been subjected to october discrimination by the method of the present embodiment by the total number of target seeds, and then multiplying by 100.
  • the correct answer rate of the double identification shown in Table 102 of FIG. 5 is (1) the identification result for 350 grains taken on April 9, 2018, and (2) taken on April 13, 2018. It is the average of the discrimination results for 300 grains.
  • FIG. 6 shows an example of imaged images of double-flowered seeds and single-flowered seeds related to iron marine.
  • the captured image shown in FIG. 6 is displayed in black and white, it is difficult to understand, but single-flowered seeds tend to be more reddish than double-flowered seeds.
  • the tendency is weaker than that in the case of iris, and in the case of iron marine, the reddish seed color tends to make it difficult to distinguish between double-flowered seeds and single-flowered seeds.
  • Graphs 103 and 104 in FIG. 7 are histograms showing the results of calculating the a * value of seeds for iron marine.
  • the graph 103 in FIG. 7 is a histogram in the case of targeting 224 particles taken on April 9, 2018. Further, the graph 104 in FIG. 7 is a histogram in the case where 284 particles photographed on April 12, 2018 are targeted.
  • the histograms of the graph 103 and the graph 104 of FIG. 7 are generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double bloom seeds and the broken line graph indicates single bloom seeds. As shown in these figures, it can be seen that in the range where the a * value is extremely small (1 to 4), the frequency of double-flowered seeds and the frequency of single-flowered seeds overlap, and the frequency level is reversed.
  • Table 105 in FIG. 8 shows the seed selection rate and the correct answer regarding whether or not the a * value of the specific target seed for iron marine is included in the lower X% of the a * values of all the target seeds. It is a table which shows the relationship with a rate. As shown in the figure, in the iron marine, the correct answer rate of the double discrimination is maintained between 66% and 69% regardless of the seed selection rate X% (10 to 50).
  • the correct answer rate of the double identification in the case of "Early” is higher than the correct answer rate of the double identification in the case of iron marine, and there is a large difference in the correct answer rate of the double identification between these varieties. I understand that there is. Therefore, it is preferable to perform the double discrimination using parameters such as the circularity r in addition to the a * value as in the second embodiment. That is, it is possible to improve the discrimination accuracy by performing the double discrimination by utilizing the characteristic quantities of a plurality of seeds.
  • the histogram in FIG. 9 is generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double flower seeds and the broken line graph indicates single flower seeds.
  • the result of performing discrimination using only the blue color tone value (B value of the RGB color space) as an index used for octo-identification is shown.
  • Yae was selected based on whether the B value of a specific target seed is included in the top Z% of all the target B values of the target.
  • the seed selectivity Y was in the range of 10-50.
  • the correct answer rate of 61% to 63% was obtained in the double identification of spectacular rice.
  • the results of the correct answer rates of double discrimination were 59% and 62%, respectively.
  • Table 117 in FIG. 14 shows that the a * value of a specific target seed for each of Soryo, iron marine, iron cherry, and iron white is in the lower X% of the a * values of all target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is contained. As shown in the figure, the smaller the seed selection rate X% is, the higher the correct answer rate of the double discrimination is.
  • the correct answer rate of the double identification for Sauryi shown in Table 117 of FIG. 14 is (1) the discrimination result for 350 grains taken on April 9, 2018, and (2) April 13, 2018. Identification result for 300 tablets taken on the day, (3) Identification result for 200 tablets taken on September 3, 2018, and (4) 200 tablets taken on September 5, 2018 It is the average of the discrimination result for.
  • the correct answer rate of the double identification for iron marine shown in Table 117 of FIG. 14 is (1) the identification result of 250 tablets taken on April 9, 2018, and (2) April 2018. It is the average of the discrimination results of 300 tablets taken on the 12th.
  • the correct answer rate of the double identification for each of the iron cherry and the iron white shown in Table 117 of FIG. 14 is (1) the discrimination result of 250 grains taken on September 3, 2018, and (2) It is the average of the discrimination results of 250 tablets taken on September 5, 2018.
  • a graph 118 in FIG. 14 is a graph showing a variety comparison between the seed selection rate and the correct answer rate in the double discrimination by the a * value.
  • the correct answer rate for double discrimination tends to increase as the seed selection rate decreases.
  • the rate of correct answers in double discrimination tends not to change so much even if the seed selection rate is changed.
  • Table 119 of FIG. 15 shows that the b * value of a specific target seed for each of Saury, Iron Marine, Iron Cherry, and Iron White is in the lower Y% of the b * values of all the target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is contained. According to the figure, when the seed selection rate is 20% or less, the correct answer rate of the double discrimination of iron cherry and iron white is higher than that of Saurya and iron marine.
  • the B value of a particular target seed for each of Saury, iron marine, iron cherry, and iron white is included in the lower Z% of the B values of all the target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is. According to the figure, when the seed selection rate is 20% or less, the correct answer rate of the double identification of iron white is higher than that of Sauryo iron marine and iron cherry.
  • any of the conditions (1) [area: upper 10%, R value: lower 20%, a * value: lower 20%] is satisfied, and condition (2) [B value: lower 10%].
  • a * value: top 40%] was selected as a double layer.
  • the seed selection rate was 30%, and the correct answer rate in the double discrimination was 90%.
  • any of the conditions (1) [area: upper 5%, R value: lower 5 to 10%, a * value: lower 55%, circularity: lower 5%]
  • condition (2) [area: lower 10%, R value: upper 10 to 30%, a * value: lower 3%, b * value: upper 35%, saturation: upper 35% or average value x 0.8
  • seeds that do not correspond to any of the roundness: lower 3%] were selected as double layers.
  • the seed selection rate was 24%, and the correct answer rate in the double discrimination was 91%, which was a high value.
  • the iron cherry it corresponds to any one of the condition (1) [color difference: lower 10%, saturation: lower 20%], and the condition (2) [area: lower 15% or upper 10%, R]. Values: top 3%, a * value: top 5%, b * value: top 15%] Seeds that did not correspond to any of the above were selected as double layers. As a result, as shown in Table 123 in FIG. 16, the seed selection rate was 26%, and the correct answer rate in the double discrimination was 88%.
  • the condition (1) [a * value: lower 35%] is satisfied, and the condition (2) [area: lower 15% or upper 3%, R value: upper 3%, B value] : Lower 2%, b * value: lower 3%, aspect ratio: lower 5%], seeds that did not correspond to any of the above were selected as double layers.
  • the aspect ratio is a value obtained by dividing the length of the major axis by fitting the target seed to an ellipse by the minor axis.
  • FIG. 17 is a graph showing the relationship between the seed selection rate and the correct answer rate of each of Saury, iron marine, iron cherry and iron white in the case of performing double discrimination using a single or a plurality of feature amounts. is there.
  • Iron White Single shows the result of performing double discrimination with a single feature amount on the Iron White stock.
  • Iron white compound indicates the result of double discrimination of iron white stock with a plurality of characteristic amounts.
  • FIG. 18 is a block diagram showing the functional configurations of the color sensor 600 and the information processing apparatus 300 according to the third embodiment of the present invention.
  • the present embodiment is different from the above-described embodiment in that a color sensor 600 is used instead of the imaging device 500 of the first and second embodiments. That is, in this embodiment, the light information of the reflected light from the seeds of the plurality of stocks is the detection information detected by the color sensor 600.
  • the control unit 4 includes a data analysis unit 11a instead of the image analysis unit 11.
  • the color sensor 600 includes a light source and a light receiving unit, and the light receiving unit detects the reflected light, which is the light emitted from the light source hitting the seed and reflected.
  • the color sensor 600 may output RGB values, or may directly output a * values or b * values.
  • the data analysis unit 11a calculates a * values and b * values based on the RGB values.
  • a colorimeter (CR-200 manufactured by Konica Minolta Co., Ltd.) can be exemplified.
  • the method of acquiring color tone information by this color difference meter is as follows.
  • the light source of a color difference meter is set to either C or D 65.
  • All target seeds are measured under the same settings and calibration conditions.
  • the seeds are placed on a white plate, and the a * value and the b * value are measured one by one using a colorimeter that has been set and calibrated.
  • the a * value and the b * value of the early seeds have distributions as shown by a graph 125 and a graph 126 in FIG. 19, respectively.
  • the method of acquiring red color tone information and the like (feature information) by the image analysis unit 11 (or the data analysis unit 11a) and the method of determining whether or not double-flowered seeds are determined by the determination unit 12 are not limited to the above-described embodiments.
  • machine learning for example, machine learning, or any of the following machine learning methods or a combination thereof can be used.
  • CNN Convolutional Neural Network
  • RNN Recurrent Neural Network
  • a convolutional layer that performs a convolutional operation is provided as one or a plurality of layers included in the neural network, and a filter operation (product) is performed on input data input to the layer. It may be configured to perform a sum operation).
  • processing such as padding may be used in combination, or an appropriately set stride width may be employed.
  • the neural network a multi-layer type or super multi-layer type neural network having tens to thousands of layers may be used.
  • the machine learning used by the image analysis unit 11 to acquire red color tone information and the like and the determination unit 12 to determine whether or not double-flowered seeds are used may be supervised learning or unsupervised learning. Good.
  • the teacher data used for this machine learning has a structure as shown below. is assumed.
  • the “teacher data” refers to data for constructing or updating a learning model, which is applied when the identification device 10 performs machine learning to identify double-flowered seeds for a plurality of stock seeds.
  • the image analysis unit 11 uses the image data of the seeds of the plurality of stocks used in the process of acquiring the red color tone information of the seeds of the plurality of stocks, and (ii) the seeds of the seeds included in the image data.
  • Teacher data having a structure including morphological data indicating the morphology of flowers blooming after growth is assumed.
  • control block (particularly the image analysis unit 11 and the determination unit 12) of the identification device 10 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software. Good.
  • the discrimination device 10 includes a computer that executes the instructions of a program that is software that realizes each function.
  • the computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. Then, in the computer, the processor reads the program from the recording medium and executes the program to achieve the object of the present invention.
  • the processor for example, a CPU (Central Processing Unit) can be used.
  • the recording medium include “temporary tangible media” such as ROM (Read Only Memory), tapes, disks, cards, semiconductor memories, and programmable logic circuits. Further, a RAM (Random Access Memory) for expanding the program may be further provided.
  • the program may be supplied to the computer via an arbitrary transmission medium (a communication network, a broadcast wave, or the like) capable of transmitting the program.
  • a transmission medium a communication network, a broadcast wave, or the like
  • one embodiment of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.
  • the discrimination apparatus is a discrimination apparatus for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, and the light of reflected light from the seeds of the plurality of stocks. From the information, for each of the seeds of the plurality of stock, an information acquisition unit that acquires at least color tone information indicating the color tone value of a predetermined color component included in the color of the seed, and the predetermined number of seeds of the plurality of stocks. Color tone values of the color components are ranked in descending order, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range.
  • a determining unit By determining whether or not the particular seed of the stock is the double-flowered seed, a determining unit is provided. According to the above configuration, by utilizing the tone value of the predetermined color component contained in the color of the seed of the stock, it is possible to perform the double discrimination in the seed state for various varieties of the stock.
  • the optical information may be image information acquired by the imaging device. According to the above configuration, in addition to the color tone information, it is possible to acquire information relating to the size and shape of the seed, such as information indicating the area of the seed and the degree of roundness of the cross-sectional shape of the seed.
  • the optical information may be detection information detected by a color sensor. According to the above configuration, as compared with the case where the optical information is image information obtained by the image pickup apparatus, the process of obtaining the color tone information is simplified and the processing cost can be reduced.
  • the discrimination apparatus is the discrimination apparatus according to any one of aspects 1 to 3, wherein the color tone value of the predetermined color component is the color tone value of the red component, and the color tone information is the color tone value of the red component. It may be red color tone information indicating. Regarding the color of the stock seeds, the single-flowered seeds tend to be more reddish than the double-flowered seeds, regardless of the variety. Therefore, according to the above configuration, by using the red color tone value as an index indicating the redness of the seed color, it is possible to perform double discrimination in the seed state for various varieties of stock. it can.
  • the determining unit can change the first range according to the stock type. According to the above configuration, by changing the first range to an appropriate range according to the type of stock, it is possible to improve the accuracy of double identification.
  • the discrimination device is, in the aspect 4, in the determination unit, the tone value of the red component related to the seed of the specific stock is the seed of all the stocks as the first range. It is determined whether or not the color tone value of the red component is included in the lower 50% range. According to the above configuration, for example, when the variety of stock is early, it is possible to perform double identification with high accuracy of 80% or more.
  • the information acquisition unit further acquires area information indicating the area of the seed, the determination unit, for each of the seeds of the plurality of stock, Further, it is determined whether or not the above area is included in the predetermined second range.
  • the discrimination apparatus is the Aspect 2 according to Aspect 2, wherein the information acquisition unit further acquires roundness information indicating the degree of roundness of the cross-sectional shape of the seed, and the determination unit is configured to store the plurality of stocks. For each seed, it is further determined whether or not the degree of roundness is included in the predetermined third range. According to the above configuration, by combining the tone value of the predetermined color component and the roundness information of the seed, it is possible to further improve the accuracy of the double identification.
  • the information acquisition unit further acquires yellow color tone value information indicating a color tone value of a yellow component included in the seed color.
  • the determining unit further determines whether or not the color tone value of the yellow component is included in the predetermined fourth range for each of the seeds of the plurality of stocks.
  • the information acquisition unit further acquires blue color tone value information representing a color tone value of a blue component included in the seed color.
  • the determination unit further determines, for each of the plurality of stock seeds, whether the color tone value of the blue component is included in a predetermined fifth range.
  • the discrimination apparatus is the discrimination apparatus according to any one of aspects 1 to 10, wherein the information acquisition unit further acquires saturation information indicating the saturation of the seed, and the determination unit includes the plurality of units. For each of the seeds of the stock, it is further determined whether or not the saturation is included in the predetermined sixth range. According to the above configuration, by combining the tone value and the saturation of the predetermined color component, it is possible to further improve the accuracy of the octo-identification.
  • the tone value of the red component is the a * value in the Lab color space. According to the above configuration, by using the a * value of the Lab color space as the tone value of the predetermined color component, it is possible to further improve the accuracy of the double identification.
  • the information processing apparatus includes the discrimination apparatus according to any of the first to twelfth aspects. According to the above configuration, it is possible to obtain the same effect as that of the first aspect.
  • the teacher data according to aspect 14 of the present invention includes an information acquisition unit that acquires at least the characteristic information indicating the characteristics of the seed from the image data of the seed for each of the plurality of stock seeds before the double-flowered seeds are distinguished.
  • Image information of the seeds of the plurality of stocks which is used in the process of obtaining at least the information acquisition unit of the discrimination device, and morphological data indicating the morphology of flowers blooming after the growth of seeds included in the image data, It has a structure including. According to the above configuration, by making the discrimination device learn using the teacher data, the discrimination accuracy of the discrimination device can be easily and dramatically improved.
  • the discrimination method according to aspect 15 of the present invention is a discrimination method for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, wherein the light of reflected light from the seeds of the plurality of stocks is used. From the information, for each of the seeds of the plurality of stock, an information acquisition step of obtaining at least color tone information indicating the color tone value of a predetermined color component included in the color of the seed, and the predetermined of the seeds of the plurality of stocks. Color tone values of the color components are ranked in descending order, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range. By determining whether or not the particular stock seed is the double-flowered seed. According to the above method, it is possible to obtain the same effect as that of the first aspect.
  • the discrimination apparatus may be realized by a computer, and in this case, the discrimination apparatus is realized by the computer by operating the computer as each unit (software element) included in the discrimination apparatus.
  • the program of the identification device and the computer-readable recording medium recording the program are also included in the scope of the present invention.
  • Identification device 11 Image analysis unit (information acquisition unit) 11a Data analysis unit (information acquisition unit) 12 Determining unit 100, 200, 300 Information processing device 500 Imaging device 600 Color sensor a * Seed color tone value (red component color tone value) b * Seed tone value (yellow component tone value) B Seed color value (blue component color value) r Roundness of seed (roundness) s Seed area C * Seed saturation

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Abstract

The present invention makes it possible to distinguish double-flowered stocks based on the condition of the seeds for various stock varieties. A distinguishing device (10) comprises: an image analysis unit (11) that acquires color tone information indicating a color tone value for a prescribed color component included in the color of at least the seed for each seed of a plurality of stock, based on light information about the light reflected from the seeds of the plurality of stocks; and a determination unit (12) that assesses whether the rank of the color tone value of the prescribed color component is included within a first range, and thereby determines whether the seed of a specific stock is a double-flowered seed.

Description

鑑別装置、情報処理装置、プログラム、教師データおよび鑑別方法Identification device, information processing device, program, teacher data, and identification method
　本発明は、鑑別装置、情報処理装置、プログラム、教師データおよび鑑別方法に関する。 The present invention relates to a discrimination device, an information processing device, a program, teacher data, and a discrimination method.
　切り花としてのストックは従来から盛んに栽培され、消費者に好まれている花の一つである。ストックは、１０月から３月にかけて咲くアブラナ科の花で、約２０ｃｍから約８０ｃｍの高さまで成長する一年草である。このストックには一重咲きと八重咲きとがあり、約半数ずつ出現するが、八重咲きの方が一重咲きよりもボリューム感があり、商品価値が高い。そのため、高い収益を上げるためには、ストックが開花する前の段階で、正確に一重咲きと八重咲きとを見分ける（八重鑑別）ことが重要になる。 Stock as a cut flower is one of the flowers that has been cultivated actively and has been favored by consumers. The stock is a cruciferous flower that blooms from October to March and is an annual plant that grows to a height of about 20 cm to about 80 cm. There are single and double blooms in this stock, and about half of them appear, but the double bloom has a larger volume than the single bloom and has a high commercial value. Therefore, in order to increase profits, it is important to accurately distinguish between single and double blooms (double discrimination) before the stock blooms.
　現在、生産者はストックが苗の状態のときに八重鑑別を行っているが、間引く苗にかける育苗コスト（種苗費、資材費、スペース、および労力）が無駄になっている。これを解消するには、種子の状態で八重鑑別を行う必要がある。 Currently, producers perform double identification when stock is in seedling state, but the seedling raising cost (seed seedling cost, material cost, space, and labor) for thinning seedlings is wasted. In order to eliminate this, it is necessary to perform double identification in the seed state.
　特許文献１には、ストックの種子をコーティングすることで、八重咲き種子に対して一重咲き種子の生育を遅らせることによって、一重咲きと八重咲きとを見分ける方法が開示されている。 Patent Document 1 discloses a method of discriminating between single-flowering and double-flowering by delaying the growth of single-flowering seeds with respect to double-flowering seeds by coating stock seeds.
日本国公開特許公報「特開２００３‐５２２２０６号公報（２００３年２月２５日公開）」Japanese Patent Laid-Open Publication "Japanese Patent Laid-Open No. 2003-522206 (Published February 25, 2003)"
　しかしながら、上記従来の方法では、品種によらず八重鑑別を行うことができるものの、種子の状態で八重鑑別を行うことができないため、八重鑑別のためには種子を播種して鑑別できる段階まで育苗する必要があり、間引く苗にかける育苗コスト（種苗費、資材費、スペース、および労力）が無駄になっているという問題点がある。 However, in the above-mentioned conventional method, although it is possible to perform double discrimination regardless of the variety, since it is not possible to perform double discrimination in the state of seeds, for double discrimination up to the stage where seeds can be sown and differentiated Therefore, there is a problem that the seedling raising cost (seedling cost, material cost, space, and labor) for thinning seedlings is wasted.
　本発明の一態様は、上記の問題点に鑑みてなされたものであり、その目的は、ストックの様々な品種について種子の状態での八重鑑別を可能にすることにある。 One aspect of the present invention has been made in view of the above problems, and an object thereof is to enable double identification in a seed state for various stock varieties.
　上記の課題を解決するために、本発明の一態様に係る鑑別装置は、八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別装置であって、上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得部と、上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定部と、を備えている。 In order to solve the above problems, the distinguishing apparatus according to one aspect of the present invention is a distinguishing apparatus for distinguishing the double-flowered seeds from a plurality of stock seeds before distinguishing the double-flowered seeds, and the plurality of the From the light information of the reflected light from the stock seeds, for each of the plurality of stock seeds, an information acquisition unit that acquires at least color tone information indicating a color tone value of a predetermined color component included in the seed color, and The tone value of the predetermined color component of the seeds of the plurality of stocks, each of which is ranked in descending order of the value, for each of the seeds of the plurality of stock, at least the order of the tone value of the predetermined color component is predetermined. A determining unit that determines whether or not the seed of the specific stock is the double-flowered seed by determining whether or not it is included in the first range.
　また、本発明の一態様に係る教師データは、八重咲き種子を鑑別する前の複数のストックの種子のそれぞれについて、当該種子の画像データから上記種子の特徴を示す特徴情報を少なくとも取得する情報取得部を備え、上記複数のストックの種子について八重咲き種子を鑑別することを機械学習する鑑別装置に適用される、学習モデルを構築ないし更新するための教師データであって、上記複数のストックの種子の特徴情報を、上記鑑別装置の上記情報取得部が少なくとも取得する処理に用いられる、上記複数のストックの種子の画像データと、上記画像データに含まれる種子の成育後に咲く花の形態を示す形態データと、を含む構造を有している。 Further, the teacher data according to one aspect of the present invention is an information acquisition unit that acquires at least the characteristic information indicating the characteristics of the seed from the image data of the seed for each of the seeds of the plurality of stocks before distinguishing the double-flowered seeds. Which is applied to a discrimination device that machine-learns to discriminate double-flowered seeds from the seeds of the plurality of stocks, is teacher data for building or updating a learning model, and features of the seeds of the plurality of stocks. Information, the information acquisition unit of the identification device is used for at least the process of acquiring, image data of the seeds of the plurality of stock, and morphological data showing the morphology of the flower blooming after the growth of the seeds contained in the image data. It has a structure including ,.
　また、本発明の一態様に係る鑑別方法は、八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別方法であって、上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得ステップと、上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定ステップと、を含んでいる。 Further, the discrimination method according to an aspect of the present invention is a discrimination method for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, and reflected light from the seeds of the plurality of stocks. From the light information, for each of the seeds of the plurality of stock, an information acquisition step of acquiring at least color tone information indicating a color tone value of a predetermined color component contained in the color of the seed, and the seeds of the plurality of stocks described above. The tone values of the predetermined color components are ranked in descending order of the values, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range. A determination step of determining whether or not the seed of the particular stock is the double-flowered seed by determining whether or not the particular stock is the double-flowered seed.
　本発明の一態様によれば、ストックの様々な品種について種子の状態での八重鑑別を可能にすることができるという効果を奏する。 According to one aspect of the present invention, it is possible to perform double identification in the seed state for various stock varieties.
本発明の実施形態１および２に係る撮像装置および情報処理装置の機能的構成を示すブロック図である。It is a block diagram showing functional composition of an imaging device and an information processor concerning Embodiments 1 and 2 of the present invention. 本発明の実施形態１に係る鑑別方法を示すフローチャートである。It is a flowchart which shows the discrimination method which concerns on Embodiment 1 of this invention. 本発明の実施形態２に係る鑑別方法の変形例を示すフローチャートである。It is a flow chart which shows the modification of the discriminating method concerning Embodiment 2 of the present invention. 早麗に関し、八重咲き種子および一重咲き種子の撮像画像の例を示す図である。It is a figure which shows the example of the picked-up image of a double-flowering seed and a single-flowering seed regarding Sayuri. グラフ１０１は、早麗に関し、種子のａ＊値の算出結果を示すヒストグラムであり、表１０２は、早麗について特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。Graph 101 is a histogram showing the calculation results of a * values of seeds for Saury, and Table 102 shows that a * values of specific target seeds for Saury are among a * values of all target seeds. Is a table showing the relationship between the seed selection rate and the correct answer rate regarding whether or not it is included in the lower x%. アイアンマリンに関し、八重咲き種子および一重咲き種子の撮像画像の例を示す図である。It is a figure which shows the example of the picked-up image of a double bloom seed and a single bloom seed regarding iron marine. アイアンマリンに関し、種子のａ＊値の算出結果を示すヒストグラムである。It is a histogram which shows the calculation result of a * value of a seed regarding iron marine. 表１０５は、アイアンマリンについて特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表であり、グラフ１０６は、早麗およびアイアンマリンのそれぞれの種子選択率と正答率との関係を示すグラフである。Table 105 shows the relationship between the seed selection rate and the correct answer rate regarding whether or not the a * value of a specific target seed for iron marine is included in the lower x% of the a * values of all target seeds. It is a table, and a graph 106 is a graph showing the relationship between the seed selectivity and the correct answer rate of each of the spoilage and iron marine. アイアンマリンに関し、種子の面積、ｂ＊値、Ｒ値および真円度の算出結果を示すヒストグラムである。5 is a histogram showing the calculation results of the area of seed, b * value, R value, and roundness of iron marine. 表１１１は、アイアンマリンについて決定部における判定条件の例を示す表であり、表１１２は、表１１１に示す判定条件を適用した場合における種子選択率と正答率との関係を示す表である。Table 111 is a table showing an example of the determination conditions in the determining unit for iron marine, and Table 112 is a table showing the relationship between the seed selection rate and the correct answer rate when the determination conditions shown in Table 111 are applied. 表１１３は、早麗について決定部における判定条件の別の例を示す表であり、表１１４は、表１１３に示す判定条件を適用した場合における種子選択率と正答率との関係を示す表である。Table 113 is a table showing another example of the determination condition in the determination unit for Sayuri, and Table 114 is a table showing the relationship between the seed selection rate and the correct answer rate when the determination condition shown in Table 113 is applied. is there. 単一または複数の特徴量を用い八重鑑別を行った場合における早麗およびアイアンマリンのそれぞれの種子選択率と正答率との関係を示すグラフである。It is a graph which shows the relationship between each seed selection rate and the correct answer rate of Saury and iron marine in the case of performing the double discrimination using a single or multiple characteristic amount. 表１１５は、黄色色調値を用い八重鑑別を行った場合における早麗およびアイアンマリンのそれぞれの種子選択率と正答率との関係を示す表であり、表１１６は、青色色調値を用い八重鑑別を行った場合における早麗およびアイアンマリンのそれぞれの種子選択率と正答率との関係を示す表である。Table 115 is a table showing the relationship between the seed selectivity and the correct answer rate of each of the splendid and iron marine when the occultation was performed using the yellow hue value, and Table 116 is the occultation using the blue hue value. FIG. 6 is a table showing the relationship between the seed selectivity and the correct answer rate for each of Saury and iron marine in the case of performing. 表１１７は、早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれについて特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位Ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表であり、グラフ１１８は、ａ＊値による八重鑑別の種子選択率と正答率との品種比較を示すグラフである。Table 117 relates to whether or not the specific target seed a * value for each of Saury, Iron Marine, Iron Cherry and Iron White is included in the lower X% of the a * values of all target seeds. It is a table | surface which shows the relationship between a seed selection rate and a correct answer rate, and the graph 118 is a graph which shows the variety comparison of the seed selection rate and the correct answer rate of the double discrimination by a * value. 表１１９は、早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれについて特定の対象種子のｂ＊値が、対象の全ての種子のｂ＊値のうち、下位Ｙ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表であり、表１２０は、特定の対象種子のＢ値が、対象の全ての種子のＢ値のうち、下位Ｚ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。Table 119 relates to whether the b * value of a particular target seed for each of Saury, Iron Marine, Iron Cherry and Iron White is included in the bottom Y% of the b * values of all target seeds. It is a table showing the relationship between the seed selection rate and the correct answer rate, and Table 120 shows whether the B value of a particular target seed is included in the lower Z% of the B values of all target seeds. It is a table which shows the relationship between a selection rate and a correct answer rate. 表１２１は、早麗について複数の特徴量の組合せによる八重鑑別の種子選択率と正答率を示す表であり、表１２２は、アイアンマリンについて複数の特徴量の組合せによる八重鑑別の種子選択率と正答率を示す表であり、表１２３は、アイアンチェリーについて複数の特徴量の組合せによる八重鑑別の種子選択率と正答率を示す表であり、表１２４は、アイアンホワイトについて複数の特徴量の組合せによる八重鑑別の種子選択率と正答率を示す表である。Table 121 is a table showing the seed selection rate and the correct answer rate of octet discrimination by the combination of a plurality of feature amounts for Saury, and Table 122 is the seed selection rate of the octet discrimination by a combination of a plurality of feature amounts for iron marine. It is a table showing the correct answer rate, Table 123 is a table showing the seed selection rate and the correct answer rate of the double identification by the combination of a plurality of feature amounts for iron cherry, and Table 124 is a combination of a plurality of feature amounts for Iron White. It is a table which shows the seed selection rate and the correct answer rate of the double discrimination by. 単一または複数の特徴量を用い八重鑑別を行った場合における早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれの種子選択率と正答率との関係を示すグラフである。It is a graph which shows the relationship between each seed selection rate and correct answer rate of Sayuri, iron marine, iron cherry, and iron white in the case of performing the double discrimination using a single or multiple characteristic amount. 本発明の実施形態３に係るカラーセンサおよび情報処理装置の機能的構成を示すブロック図である。It is a block diagram which shows the functional structure of the color sensor and information processing apparatus which concern on Embodiment 3 of this invention. グラフ１２５は、早麗に関し、種子のａ＊値の測定結果を示すヒストグラムであり、グラフ１２６は、早麗に関し、種子のｂ＊値の測定結果を示すヒストグラムである。A graph 125 is a histogram showing the measurement result of the seed a * value with respect to spoilage, and a graph 126 is a histogram showing the measurement result of the seed b * value with respect to spoilage.
　〔実施形態１〕
　説明の便宜上、特定の項目について説明した構成と同一の機能を有する構成については、同一の符号を付記し、その説明を省略する。実施形態２以下の各実施形態についても同様である。 [Embodiment 1]
For the sake of convenience of explanation, components having the same functions as those described for specific items are denoted by the same reference numerals, and description thereof is omitted. The same applies to the second and subsequent embodiments.
　＜情報処理装置の機能的構成＞
　情報処理装置１００としては、ＰＣ、タブレット端末および種子選別ロボット等を例示することができる。図１に示すように、情報処理装置１００は、表示部１、操作入力部２、記憶部３よび制御部４を備えている。なお、情報処理装置１００は、表示部１を備えていなくても良い。例えば、情報処理装置１００が表示部を備えない種子選別ロボットであるような場合、鑑別した種子を種子選別ロボットの手が選択して所定の場所へ移動させる形態などを例示することができる。 <Functional configuration of information processing device>
Examples of the information processing device 100 include a PC, a tablet terminal, a seed selection robot, and the like. As shown in FIG. 1, the information processing apparatus 100 includes a display unit 1, an operation input unit 2, a storage unit 3 and a control unit 4. The information processing device 100 may not include the display unit 1. For example, when the information processing apparatus 100 is a seed selection robot that does not include a display unit, a mode in which the hand of the seed selection robot selects the identified seed and moves it to a predetermined place can be exemplified.
　表示部１は、後述の鑑別装置１０による鑑別結果、および情報処理装置１００に装備されている各種機能（アプリケーションソフト）が実行されることに起因する画像等を表示する。操作入力部２は、入力されたユーザ操作を取得するものであり、例えば情報処理装置１００がタブレット端末の場合であれば、表示部１と一体となったタッチパネルが用いられる。 The display unit 1 displays a discrimination result by the discrimination device 10 to be described later, an image resulting from execution of various functions (application software) installed in the information processing device 100, and the like. The operation input unit 2 acquires an input user operation, and if the information processing apparatus 100 is a tablet terminal, for example, a touch panel integrated with the display unit 1 is used.
　記憶部３は、後述の鑑別装置１０によって生成された各種データ、および制御部４が実行する各種の制御プログラム等を記憶するものであり、例えばハードディスク、フラッシュメモリなどの不揮発性の記憶装置によって構成される。記憶部３は、例えば情報処理装置１００の外部に備えられてもよい。 The storage unit 3 stores various data generated by the identification device 10 described later, various control programs executed by the control unit 4, and the like, and is configured by a nonvolatile storage device such as a hard disk or a flash memory. To be done. The storage unit 3 may be provided outside the information processing device 100, for example.
　制御部４は、情報処理装置１００を統括的に制御するものであり、鑑別装置１０を備えている。鑑別装置１０は、複数のストックの種子から八重咲き種子を鑑別するための装置であり、図１に示すように画像解析部１１（情報取得部）および決定部１２を備えている。 The control unit 4 controls the information processing device 100 in a centralized manner, and includes a discrimination device 10. The discrimination device 10 is a device for discriminating double-flowered seeds from a plurality of stock seeds, and includes an image analysis unit 11 (information acquisition unit) and a determination unit 12 as illustrated in FIG. 1.
　本発明の実施形態に係る情報取得部は、上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得するものである。本実施形態では、上記光情報が、撮像装置５００によって取得される画像情報であり、情報取得部が画像解析部１１である場合について説明するが、情報取得部の態様はこれに限定されない（実施形態３参照）。なお、情報取得部を画像解析部１１とした場合、色調情報の他、種子の面積や種子の断面形状の丸みの程度を示す情報など、種子の大きさや形に係る情報を取得することが可能になる（実施形態２参照）。撮像装置５００は、白色光源等から出射された光がストックの種子に当たって反射した反射光を受けて画像情報を取得する。 The information acquisition unit according to the embodiment of the present invention, from the light information of the reflected light from the seeds of the plurality of stocks, for each of the seeds of the plurality of stocks, the color tone of a predetermined color component included in the color of the seeds. At least color tone information indicating a value is acquired. In the present embodiment, a case will be described in which the optical information is image information acquired by the imaging device 500 and the information acquisition unit is the image analysis unit 11, but the mode of the information acquisition unit is not limited to this. (See Form 3). When the information acquisition unit is the image analysis unit 11, it is possible to acquire information regarding the size and shape of the seed, such as information indicating the area of the seed and the degree of roundness of the cross-sectional shape of the seed, in addition to the color tone information. (See Embodiment 2). The imaging device 500 acquires image information by receiving the reflected light that the light emitted from the white light source or the like hits the seed of the stock and is reflected.
　図１では、制御部４は情報処理装置１００に内蔵されているが、この場合に限定されない。例えば、制御部４は、情報処理装置１００に取付けられた外部装置や通信部（不図示）を介して利用するネットワークサーバーであっても構わない。あるいは、鑑別装置１０が、情報処理装置１００に取付けられた外部装置であってもよい。 In FIG. 1, the control unit 4 is built in the information processing device 100, but the present invention is not limited to this case. For example, the control unit 4 may be a network server used via an external device attached to the information processing device 100 or a communication unit (not shown). Alternatively, the identification device 10 may be an external device attached to the information processing device 100.
　なお、本実施形態以下の各実施形態においては、八重咲き種子を鑑別する前のストックの種をステージに１粒ずつ載置した状態の種子を、鑑別装置１０による八重鑑別の対象となるストックの種子（以下、「対象種子」と略記）とする。ここで、「八重咲き種子を鑑別する前のストックの種」は、色特性値等の順位付けが行われるストックの種子が、未選別の種子の集団であることを特定したものである。 In addition, in each of the following embodiments, the seeds in the state where the seeds of the stock before distinguishing the double-flowered seeds are placed on the stage one by one are the seeds of the stock that is the target of the double identification by the distinguishing device 10. (Hereinafter, abbreviated as “target seed”). Here, "the seed of the stock before distinguishing the double-flowered seeds" specifies that the seeds of the stock for which the color characteristic values and the like are ranked are a group of unselected seeds.
　なお、種子の撮影ができれば必ずしもストックの種をステージに載置しなくてもよい。ストックの種をステージに載置しないで種子の撮影を行う場合としては、例えば、空中で種子の撮影を行う場合が考えられる。空中で種子の撮影を行うための手法として、例えば、（株）サタケ製の多用途シュート式光選別機を用い、シュートに種子を流し、該シュートを流れて空中に飛び出した種子を撮影する手法を挙げることができる。 Note that it is not necessary to put the stock seeds on the stage if the seeds can be photographed. As a case where the seed is photographed without placing the stock seed on the stage, for example, the case where the seed is photographed in the air can be considered. As a method for photographing seeds in the air, for example, a multipurpose shoot type optical sorter manufactured by Satake Co., Ltd. is used, and seeds are flown into the chute, and the seeds that flow through the chute and fly out into the air are photographed. Can be mentioned.
　＜鑑別装置の機能的構成＞
　（画像解析部）
　画像解析部１１は、八重鑑別の前処理として、上記ステージに載置した複数の対象種子の画像を解析する。具体的には、まず、デジタルカメラなどの撮像装置５００を用いて、画像解析の対象となる複数の対象種子をそれらの上方から同一視野で撮像する。なお、本実施形態では、複数の対象種子を一括して撮影する形態について説明するが、複数の対象種子のそれぞれを１つずつ個別に撮影するようにしても良い。 <Functional configuration of the discrimination device>
(Image analysis section)
The image analysis unit 11 analyzes the images of the plurality of target seeds placed on the stage as a preprocessing of the double discrimination. Specifically, first, a plurality of target seeds to be image-analyzed are imaged in the same field of view from above using an imaging device 500 such as a digital camera. In addition, in the present embodiment, a mode in which a plurality of target seeds are collectively imaged will be described, but each of the plurality of target seeds may be individually imaged.
　撮像場所について特に制限はないものの、暗室内で撮像するのが好ましい。複数の対象種子の撮像画像（以下、「初期画像」と略記）は、ＲＡＷ画像の状態から撮像装置５００の画像編集ソフトによってＴＩＦ形式に現像されて、画像解析部１１に送信される。 Although there are no particular restrictions on the imaging location, it is preferable to image in a dark room. The captured images of a plurality of target seeds (hereinafter abbreviated as “initial images”) are developed in the TIF format from the state of the RAW image by the image editing software of the image capturing apparatus 500, and are transmitted to the image analysis unit 11.
　ＴＩＦ形式の初期画像（以下、「ＴＩＦ画像」と略記）を受信した画像解析部１１は、ＲＧＢ色空間となっているＴＩＦ画像に二値化処理を施して二値化画像を生成する。 The image analysis unit 11 that has received the initial image in TIF format (hereinafter abbreviated as “TIF image”) performs binarization processing on the TIF image in the RGB color space to generate a binarized image.
　なお、本実施形態では、初期画像の画像形式としてＴＩＦ形式を用いる場合について説明するが、初期画像の画像形式としてはこれに限定されず、例えば、ＪＰＥＧ形式やＧＩＦ形式などを用いても良い。 In the present embodiment, the case where the TIF format is used as the image format of the initial image will be described, but the image format of the initial image is not limited to this, and for example, the JPEG format or the GIF format may be used.
　上記の二値化処理の際、画像解析部１１は、画像内の対象種子と背景との境界の色相、彩度および明度を示す値（以下、「閾値」と略記）を算出・取得する。場合によっては、二値化処理の際に種子の種子面積ｓを算出・取得してもよい。そして、ＴＩＦ画像をＣＩＥＬＡＢ色空間に変換してＬ＊、ａ＊およびｂ＊の各画像に分割した後、当該二値化画像から上記Ｌ＊、ａ＊およびｂ＊の各画像を指定する。 During the binarization process described above, the image analysis unit 11 calculates and acquires values (hereinafter, abbreviated as “threshold”) indicating the hue, saturation, and lightness of the boundary between the target seed and the background in the image. In some cases, the seed area s of the seed may be calculated and acquired during the binarization process. Then, by converting the TIF images in CIELAB color space L *, after dividing into a * and b * each image, the from the binarized image L *, designates a * and b * each image.
　なお、本実施形態以下の各実施形態では、画像内の対象種子と背景とを３つの指標（色相、彩度および明度）の閾値で分節したが、この場合に限定されない。例えば１つの指標のみで分節してもよいし、「色相、彩度および明度」以外の指標で分節してもよい。 In each of the following embodiments, the target seed and the background in the image are segmented by the thresholds of three indexes (hue, saturation, and brightness), but the present invention is not limited to this case. For example, only one index may be used for segmentation, or an index other than “hue, saturation, and lightness” may be used for segmentation.
　ＣＩＥＬＡＢ色空間は、国際照明委員会（ＣＩＥ）が策定したほぼ完全な色空間である。人間の目で見ることができるすべての色を表すことができ、画像処理に係る機器固有モデルの基準として利用することができる。 The CIELAB color space is a nearly complete color space established by the International Commission on Illumination (CIE). It can represent all colors visible to the human eye and can be used as a reference for a device-specific model related to image processing.
　ＣＩＥＬＡＢ色空間は、Ｌ＊、ａ＊およびｂ＊の３つの座標で構成される。Ｌ＊は色の明度を表し、Ｌ＊＝０が黒、Ｌ＊＝１００が白の拡散色となる。白の反射色は、Ｌ＊＝１００よりもさらに値が高くなる。ａ＊は、赤色／マゼンタ色と緑色との間の位置を表し、負の値が緑色寄りとなり、正の値が赤色／マゼンタ色寄りとなる。ｂ＊は、黄色と青色との間の位置を表し、負の値が青色寄りとなり、正の値が黄色寄りとなる。 The CIELAB color space is composed of three coordinates L * , a * and b * . L * represents the lightness of the color, and L * = 0 is black and L * = 100 is white diffused color. The reflected color of white has a value higher than that of L * = 100. a * represents a position between red / magenta and green, where a negative value is closer to green and a positive value is closer to red / magenta. b * represents a position between yellow and blue, where a negative value is closer to blue and a positive value is closer to yellow.
　次に、複数の対象種子のそれぞれについて、種子のＬ＊値、ａ＊値およびｂ＊値の３つの値を算出し、取得する。なお、この３つの値は、Ｌ＊、ａ＊およびｂ＊の各画像における全画素の平均値となる。 Next, for each of the plurality of target seeds, three L * values, a * values, and b * values of the seeds are calculated and acquired. Note that these three values are average values of all pixels in each image of L * , a *, and b * .
　画像解析部１１は、算出・取得した種子のａ＊値を決定部１２に送信する。なお、種子のａ＊値は、記憶部３に送信されて記憶されてもよい。このことは、後述する種子の彩度Ｃ＊、Ｒ値、Ｂ値、ｂ＊値、種子面積ｓ、円形度ｒ、および真円度などについても同様である（実施形態２参照）。 The image analysis unit 11 transmits the calculated and acquired a * value of the seed to the determination unit 12. The a * value of the seed may be transmitted to and stored in the storage unit 3. The same applies to the seed saturation C * , R value, B value, b * value, seed area s, circularity r, and roundness described later (see Embodiment 2).
　（決定部）
　決定部１２は、画像解析部１１から受信した種子の所定の色成分の色調値に基づいて、複数の対象種子のそれぞれについて八重咲き種子か否かを決定する。具体的には、決定部１２は、複数のストックの種子の所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する。 (Decision part)
The determination unit 12 determines whether or not each of the plurality of target seeds is a double-flowered seed, based on the color tone value of the predetermined color component of the seed received from the image analysis unit 11. Specifically, the determining unit 12 ranks the tone values of the predetermined color components of the seeds of the plurality of stocks in descending order of the values, and determines at least the predetermined color component of each of the seeds of the plurality of stocks. It is determined whether or not the seed of the specific stock is the double-flowered seed by determining whether or not the rank of the color tone value of is included in the predetermined first range.
　なお、本実施形態の決定部１２は、画像解析部１１から受信した種子のａ＊値に基づいて、複数の対象種子のそれぞれについて八重咲き種子か否かを決定する。具体的には、特定の対象種子に係る種子のａ＊値が、すべての対象種子に係る種子のａ＊値のうちの下位ｘ％（第１範囲）に含まれているか否かを判定する。 In addition, the determination unit 12 of the present embodiment determines whether or not each of the plurality of target seeds is a double-flowered seed, based on the a * value of the seed received from the image analysis unit 11. Specifically, it is determined whether or not the a * value of the seed related to the specific target seed is included in the lower x% (first range) of the a * values of the seed related to all the target seeds. .
　下位ｘ％に含まれていた場合、決定部１２は、判定対象となった特定の対象種子が八重咲き種子であることを決定する。一方、下位ｘ％に含まれていなかった場合、決定部１２は、上記特定の対象種子が八重咲き種子でない、言い換えれば一重咲き種子であることを決定する。 When included in the lower x%, the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed. On the other hand, when it is not included in the lower x%, the determining unit 12 determines that the specific target seed is not a double flower seed, in other words, a single flower seed.
　ここで、ｘの値としては、少なくとも５０以下の数値を用いる。このように数値設定することで、早麗の八重鑑別の正答率が約８０％以上となり、一般的に種子での八重鑑別においてほぼ成功と見做されるレベルの正答率となる。 Here, as the value of x, use a numerical value of at least 50 or less. By setting the numerical values in this way, the correct answer rate of the double identification of quick rebirth is about 80% or more, and the correct answer rate is generally considered to be almost successful in the double identification of seeds.
　決定部１２は、特定のストックの種子に係る赤色成分の色調値（ａ＊値）が、上記第１範囲としての、すべてのストックの種子に係る上記赤色成分の色調値のうちの下位５０％の範囲に含まれているか否かを判定しても良い。これにより、例えば、ストックの品種が早麗である場合に、８０％以上の高い精度で八重鑑別を行うことが可能になる。 The determining unit 12 determines that the color tone value (a * value) of the red component related to the seed of the specific stock is the lower 50% of the color value of the red component related to the seed of all the stocks as the first range. It may be determined whether or not it is included in the range. Thereby, for example, when the stock type is early, it is possible to perform double identification with a high accuracy of 80% or more.
　なお、ｘの値は、記憶部３または決定部１２内のメモリ（不図示）等に予め記憶されていてもよい。または、操作入力部２からユーザ入力することにより、ストックの種類等に応じた好ましい数値を任意に設定してもよい。 Note that the value of x may be stored in advance in the storage unit 3 or a memory (not shown) in the determination unit 12 or the like. Alternatively, a user may input a desired numerical value according to the type of stock or the like by inputting it through the operation input unit 2.
　決定部１２が特定の対象種子を八重咲き種子であると決定した場合、鑑別装置１０は、当該特定の対象種子を八重咲き種子であると鑑別し、鑑別結果を表示部１に表示させる。一方、決定部１２が特定の対象種子を一重咲き種子であると決定した場合、鑑別装置１０は、当該特定の対象種子を一重咲き種子であると鑑別し、鑑別結果を表示部１に表示させる。 When the determination unit 12 determines that the specific target seed is a double-flowered seed, the discrimination device 10 discriminates the specific target seed as a double-flowered seed, and displays the discrimination result on the display unit 1. On the other hand, when the determination unit 12 determines that the specific target seed is the single-flowered seed, the identification device 10 identifies the specific target seed as the single-flowered seed, and causes the display unit 1 to display the identification result. .
　また、決定部１２は、ストックの品種に応じて、上記第１範囲を変更することができるようになっていても良い。例えば、早麗とアイアンマリンとで、好適に八重鑑別できる第１範囲が異なっている。このため、ストックの品種に応じて、第１範囲を適切な範囲に変更することにより、八重鑑別の精度を上げることが可能になる。 The determining unit 12 may be able to change the first range according to the stock type. For example, Saury and iron marine differ in the first range in which the double discrimination can be performed properly. Therefore, by changing the first range to an appropriate range according to the type of stock, it becomes possible to improve the accuracy of double identification.
　＜赤色色調値に基づく八重咲き種子の鑑別方法＞
　図２に示すように、まずステップ１０１（以下、「Ｓ１０１」と略記）にて、撮像装置５００によってＴＩＦ画像を生成し、画像解析部１１が撮像装置５００からＴＩＦ画像を受信することにより、Ｓ１０２に進む。 <Method for distinguishing double-flowered seeds based on red color tone value>
As shown in FIG. 2, first, in step 101 (hereinafter abbreviated as “S101”), a TIF image is generated by the image capturing apparatus 500, and the image analysis unit 11 receives the TIF image from the image capturing apparatus 500, so that S102 is performed. Proceed to.
　Ｓ１０２（情報取得ステップ）では、画像解析部１１が、ＴＩＦ画像をＣＩＥＬＡＢ色空間に変換してＬ＊、ａ＊およびｂ＊の各画像に分割し、Ｓ１０３に進む。 In S102 (information acquisition step), the image analysis unit 11 converts the TIF image into the CIELAB color space and divides it into L * , a *, and b * images, and proceeds to S103.
　Ｓ１０３（情報取得ステップ）では、画像解析部１１が、ＣＩＥＬＡＢ色空間に変換されたＴＩＦ画像に二値化処理を施した上で、複数の対象種子のそれぞれについてＬ＊値、ａ＊値およびｂ＊値を算出し、すべての対象種子に係る種子のａ＊値を決定部１２に送信して、Ｓ１０４に進む。 In S103 (information acquisition step), the image analysis unit 11 performs binarization processing on the TIF image converted into the CIELAB color space, and then the L * value, a * value, and b for each of the plurality of target seeds. The * value is calculated, the a * values of the seeds related to all the target seeds are transmitted to the determination unit 12, and the process proceeds to S104.
　Ｓ１０４（決定ステップ）では、決定部１２が、複数の対象種子のそれぞれについて、種子のａ＊値がすべての対象種子に係る種子のａ＊値のうちの下位ｘ％に含まれているか否かを判定する。Ｓ１０４でＹＥＳ（以下、「Ｙ」と略記）と判定した場合、決定部１２は、判定対象となった特定の対象種子が八重咲き種子であることを決定して、Ｓ１０５に進む。 In S104 (determination step), the determination unit 12, for each of the plurality of target seeds, whether the a * value of the seed is contained in the lower x% of the a * value of seed according to any of the target seeds To judge. When it is determined to be YES (hereinafter abbreviated as “Y”) in S104, the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed, and proceeds to S105.
　Ｓ１０５では、鑑別装置１０が、決定部１２によって八重咲き種子であると決定された特定の対象種子を、最終的に八重咲き種子であると鑑別する。鑑別装置１０は、鑑別結果を表示部１に送信して表示させる。 In S105, the identification device 10 finally identifies the specific target seed, which has been determined to be a double-flowered seed by the determination unit 12, as a double-flowered seed. The discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
　一方、Ｓ１０４でＮＯ（以下、「Ｎ」と略記）と判定した場合、決定部１２は、判定対象となった特定の対象種子が一重咲き種子であることを決定して、Ｓ１０６に進む。Ｓ１０６では、鑑別装置１０が、決定部１２によって一重咲き種子であると決定された特定の対象種子を、最終的に一重咲き種子であると鑑別する。鑑別装置１０は、鑑別結果を表示部１に送信して表示させる。 On the other hand, if NO (hereinafter, abbreviated as “N”) is determined in S104, the determination unit 12 determines that the specific target seed that is the determination target is a single-flowered seed, and proceeds to S106. In S106, the identification device 10 finally identifies the specific target seed, which is determined to be a single-flowering seed by the determination unit 12, as a single-flowering seed. The discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
　すべての対象種子について、Ｓ１０５またはＳ１０６のいずれかの処理が終わることにより、鑑別装置１０による八重鑑別が終了する。 The double discrimination by the discrimination device 10 is completed by finishing the processing of either S105 or S106 for all the target seeds.
　上述した形態では、八重鑑別を行う第１の指標としての所定の色成分の色調値として、赤色成分の色調値であるａ＊値を用いる形態について説明した。しかしながら、所定の色成分の色調値はａ＊値に限定されず、例えば、黄色成分の色調値ｂ＊や青色成分の色調値Ｂを用いても本発明の所期の効果を得られることが判明している（図１３参照）。 In the above-described mode, the mode in which the a * value, which is the color tone value of the red component, is used as the color tone value of the predetermined color component as the first index for performing the double discrimination. However, the tone value of the predetermined color component is not limited to the a * value. For example, even if the tone value b * of the yellow component or the tone value B of the blue component is used, the intended effect of the present invention can be obtained. It is known (see FIG. 13).
　また、特にストックの種子の色は、品種によらず、一重咲き種子の方が八重咲き種子と比較して赤みが強い傾向がある。このため、種子の色の赤みの強さを示す指標として、赤色色調値（ａ＊値）を利用することにより、育苗コストを低減しつつ、ストックの様々な品種について種子の状態での八重鑑別を可能にすることができる。 In addition, the color of the stock seeds tends to be reddish more in the single-flowered seeds than in the double-flowered seeds, regardless of the variety. Therefore, by using the red color tone value (a * value) as an index showing the redness of the color of seeds, it is possible to reduce the seedling cost and at the same time, perform double identification in the seed state for various varieties of stock. Can be enabled.
　さらに、従来は、八重咲き種子と一重咲き種子とを選別することはできなかったが、鑑別装置１０によれば、八重咲き種子と一重咲き種子とを選別することが可能になる。 Further, conventionally, it was not possible to select double-flowered seeds and single-flowered seeds, but the discrimination device 10 makes it possible to select double-flowered seeds and single-flowered seeds.
　〔実施形態２〕
　ここで、種子の色特性値を表すパラメータの他、種子のサイズや種子の形状を表すパラメータを用いても、高い鑑別精度で八重鑑別できる可能性がある。例えば、種子の形状を表すパラメータとして種子の円形度ｒを用いる場合が考えられる。「円形度」は形の丸さ（丸みの程度）を示す指標であり、一重咲き種子では円形度が大きく（丸く）なり、八重咲き種子では円形度が小さく（細長く）なると考えられる。円形度は、下記の式１を用いて算出される。 [Embodiment 2]
Here, in addition to the parameter indicating the color characteristic value of the seed, the parameter indicating the size or shape of the seed may be used to perform the double discrimination with high discrimination accuracy. For example, the case where the roundness r of the seed is used as a parameter representing the shape of the seed can be considered. “Circularity” is an index indicating the roundness (roundness) of a shape, and it is considered that single-flowered seeds have a large circularity (roundness) and double-flowered seeds have a small circularity (long and slenderness). The circularity is calculated using Equation 1 below.
　（式１）
円形度＝４×種子の種子面積／（π×種子の最大径２）
　画像解析等によって鑑別対象となるストックの種子の円形度ｒを算出し、円形度ｒがより下位の種子から当該種子を八重咲き種子として順次選択することで、高い鑑別精度で八重鑑別できる可能性がある。 (Equation 1)
Roundness = 4 x seed area of seed / (π x maximum diameter of seed 2 )
By calculating the circularity r of the seeds of the stock to be distinguished by image analysis and selecting the seeds from the seeds with lower circularity r as double-flowered seeds in sequence, it is possible that the double discrimination can be performed with high accuracy. is there.
　具体的には、例えば、画像解析部１１は、上記ａ＊値に加重して、さらに種子の面積（種子面積ｓ）を示す面積情報を取得し、決定部１２は、複数のストックの種子のそれぞれについて、さらに、上記面積が所定の第２範囲に含まれているか否かを判定しても良い。これにより、赤色色調値と種子の面積とを組合せることで、八重鑑別の精度をより高くすることができる。 Specifically, for example, the image analysis unit 11 weights the a * value and further acquires area information indicating the area of the seed (seed area s). For each, it may be further determined whether or not the above area is included in the predetermined second range. Accordingly, by combining the red color tone value and the area of the seed, it is possible to further improve the accuracy of the double identification.
　また、画像解析部１１は、上記ａ＊値に加重して、さらに種子の断面形状の丸みの程度（円形度ｒ、真円度）を示す丸み情報を取得し、決定部１２は、複数のストックの種子のそれぞれについて、さらに、上記丸みの程度が所定の第３範囲に含まれているか否かを判定しても良い。これにより、赤色色調値と種子の丸み情報とを組合せることで、八重鑑別の精度をより高くすることができる。 Further, the image analysis unit 11 weights the a * value and further acquires roundness information indicating the degree of roundness (roundness r, roundness) of the cross-sectional shape of the seed. For each stock seed, it may be further determined whether or not the degree of roundness is included in the predetermined third range. Accordingly, by combining the red color tone value and the roundness information of the seed, it is possible to further improve the accuracy of the double identification.
　次に、画像解析部１１は、上記ａ＊値に加重して、さらに種子の色に含まれる黄色成分の色調値を表す黄色色調値情報を取得し、決定部１２は、複数のストックの種子のそれぞれについて、さらに、上記黄色成分の色調値が、所定の第４範囲に含まれているか否かを判定しても良い。黄色成分の色調値の具体例としては、ＣＩＥＬＡＢ色空間のｂ＊値を挙示することができる。これにより、赤色色調値と黄色色調値とを組合せることで、八重鑑別の精度をより高くすることができる。 Next, the image analysis unit 11 weights the a * value and further acquires yellow color tone value information representing the color tone value of the yellow component included in the seed color. For each of the above, it may be further determined whether or not the tone value of the yellow component is included in the predetermined fourth range. As a specific example of the color tone value of the yellow component, the b * value in the CIELAB color space can be listed . Accordingly, by combining the red color tone value and the yellow color tone value, it is possible to further improve the accuracy of the occultation.
　また、画像解析部１１は、上記ａ＊値に加重して、さらに種子の色に含まれる青色成分の色調値を表す青色色調値情報を取得し、決定部１２は、複数のストックの種子のそれぞれについて、さらに、上記青色成分の色調値が、所定の第５範囲に含まれているか否かを判定しても良い。青色成分の色調値の具体例としては、ＲＧＢ色空間のＢ値を挙示することができる。これにより、赤色色調値と青色色調値とを組合せることで、八重鑑別の精度をより高くすることができる。 In addition, the image analysis unit 11 weights the a * value and further acquires blue color tone value information indicating the color tone value of the blue component included in the seed color, and the determination unit 12 determines the seeds of a plurality of stocks. For each, it may be further determined whether or not the tone value of the blue component is included in the predetermined fifth range. As a specific example of the tone value of the blue component, the B value in the RGB color space can be listed. Accordingly, by combining the red color tone value and the blue color tone value, it is possible to further improve the accuracy of the double identification.
　次に、画像解析部１１は、上記ａ＊値に加重して、さらに種子の彩度Ｃ＊を示す彩度情報を取得し、決定部１２は、複数のストックの種子のそれぞれについて、さらに、上記彩度Ｃ＊が所定の第６範囲に含まれているか否かを判定しても良い。これにより、赤色色調値と彩度とを組合せることで、八重鑑別の精度をより高くすることができる。 Next, the image analysis unit 11 weights the a * value and further obtains saturation information indicating the saturation C * of the seed, and the determination unit 12 further determines, for each of the seeds of the plurality of stocks, It may be determined whether or not the saturation C * is included in the predetermined sixth range. Accordingly, by combining the red color tone value and the saturation, it is possible to further improve the accuracy of the double identification.
　図１に示す情報処理装置２００は、鑑別装置１０の画像解析部１１が、種子のａ＊値に加えて種子の円形度ｒを算出・取得する点で、実施形態１に係る情報処理装置１００と異なる。また、情報処理装置２００は、鑑別装置１０の決定部１２が、種子のａ＊値および種子の円形度ｒのそれぞれについて所定の数値範囲に含まれるか否かを判定する点でも、情報処理装置１００と異なる。 In the information processing apparatus 200 shown in FIG. 1, the image analysis unit 11 of the discrimination apparatus 10 calculates and acquires the circularity r of the seed in addition to the a * value of the seed. Different from In addition, the information processing apparatus 200 also has a point that the determination unit 12 of the discrimination apparatus 10 determines whether or not each of the seed a * value and the seed circularity r falls within a predetermined numerical range. Different from 100.
　＜複数の特徴量に基づく八重咲き種子の鑑別方法＞
　図３に示すフローチャートにおいて、Ｓ２０１、Ｓ２０３およびＳ２０４の各処理は、図２のフローチャートにおけるＳ１０１～Ｓ１０３の各処理と同様であるため、その説明を省略する。 <Differentiation method of double-flowered seeds based on multiple features>
In the flowchart shown in FIG. 3, each processing of S201, S203, and S204 is the same as each processing of S101 to S103 in the flowchart of FIG. 2, so the description thereof will be omitted.
　Ｓ２０２では、画像解析部１１が、複数の対象種子のそれぞれについて、種子の円形度ｒを算出・取得して、Ｓ２０３に進む。 In S202, the image analysis unit 11 calculates and acquires the roundness r of the seed for each of the plurality of target seeds, and proceeds to S203.
　Ｓ２０４（情報取得ステップ）では、画像解析部１１が、複数の対象種子のそれぞれについて、種子のａ＊値を算出・取得して、Ｓ２０５に進む。画像解析部１１は、すべての対象種子に係る種子の円形度ｒおよびａ＊値を決定部１２に送信する。 In S204 (information acquisition step), the image analysis unit 11 calculates and acquires the a * value of the seed for each of the plurality of target seeds, and proceeds to S205. The image analysis unit 11 transmits to the determination unit 12 the seed circularity r and a * value of all target seeds.
　Ｓ２０５（決定ステップ）では、決定部１２が、特定の対象種子に係る、（Ｉ）ａ＊値がすべてのａ＊値のうちの下位ｘ％に含まれているか否か、かつ（ＩＩ）円形度ｒがすべての円形度ｒのうちの下位ｙ％に含まれているか否かについて判定する。 In S205 (determination step), the determination unit 12 determines whether or not the (I) a * value related to the specific target seed is included in the lower x% of all the a * values, and (II) circle. It is determined whether or not the degree r is included in the lower y% of all the circular degrees r.
　Ｓ２０５でＹと判定した場合、言い換えれば、特定の対象種子のａ＊値、かつ円形度ｒが上記（Ｉ）および（ＩＩ）の数値範囲に含まれていた場合、Ｓ２０６に進む。 When it is determined to be Y in S205, in other words, when the a * value of the specific target seed and the circularity r are included in the numerical ranges of (I) and (II), the process proceeds to S206.
　Ｓ２０６（決定ステップ）では、決定部１２が、判定対象となった特定の対象種子が八重咲き種子であることを決定し、鑑別装置１０が、決定部１２によって八重咲き種子であると決定された特定の対象種子を、最終的に八重咲き種子であると鑑別する。鑑別装置１０は、鑑別結果を表示部１に送信して表示させる。 In S206 (determination step), the determination unit 12 determines that the specific target seed that is the determination target is a double-flowered seed, and the identification device 10 determines the specific target seed that is determined to be a double-flowered seed by the determination unit 12. The target seed is finally identified as a double-flowered seed. The discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
　一方、Ｓ２０５でＮＯ（以下、「Ｎ」と略記）と判定した場合、言い換えれば、特定の対象種子のａ＊値、および円形度ｒのいずれも上記（Ｉ）および（ＩＩ）の数値範囲に含まれていなかった場合、決定部１２は、判定対象となった特定の対象種子が一重咲き種子であることを決定して、Ｓ２０７に進む。 On the other hand, when it is determined to be NO (hereinafter abbreviated as “N”) in S205, in other words, both the a * value of the specific target seed and the circularity r fall within the numerical ranges of (I) and (II) above. If not included, the determination unit 12 determines that the specific target seed that is the determination target is a single-flowering seed, and proceeds to S207.
　Ｓ２０７では、鑑別装置１０が、決定部１２によって一重咲き種子であると決定された特定の対象種子を、最終的に一重咲き種子であると鑑別する。鑑別装置１０は、鑑別結果を表示部１に送信して表示させる。 In S207, the discrimination device 10 finally discriminates the specific target seed determined to be the single-blooming seed by the determination unit 12 as the single-blooming seed. The discrimination device 10 transmits the discrimination result to the display unit 1 to display it.
　すべての対象種子について、Ｓ２０６またはＳ２０７のいずれかの処理が終わることにより、鑑別装置１０による八重鑑別が終了する。 The double identification by the identification device 10 is completed by completing the processing of either S206 or S207 for all the target seeds.
　＜変形例＞
　上述した形態では、種子のａ＊値に加重して使用するパラメータとして円形度ｒを用いる形態について説明した。しかしながら、鑑別装置１０による八重鑑別においては、種子のａ＊値に加重して用いるパラメータとして、円形度ｒ以外のパラメータを用いても良い。 <Modification>
In the above-mentioned form, the form in which the circularity r is used as a parameter used by weighting the a * value of the seed has been described. However, in the double discrimination by the discrimination device 10, a parameter other than the circularity r may be used as a parameter used by weighting the a * value of the seed.
　円形度ｒ以外のパラメータとしては、種子面積ｓ、ＲＧＢ色空間のＲ値、ＣＩＥＬＡＢ色空間のｂ＊値、真円度、および彩度Ｃ＊などを例示することができる。 Examples of parameters other than circularity r include seed area s, R value in RGB color space, b * value in CIELAB color space, circularity, and saturation C * .
　種子面積ｓは、対象種子を平面視した場合における当該種子の外周で取り囲まれた領域の面積である。 The seed area s is the area of the region surrounded by the outer circumference of the target seed when the target seed is viewed in a plan view.
　彩度Ｃ＊は色の鮮やかさの尺度である。彩度Ｃ＊は下記の式２を用いて算出される。 Saturation C * is a measure of color vividness. The saturation C * is calculated using the following equation 2.
　（式２）
　Ｃ＊＝〔（ａ＊）２＋（ｂ＊）２〕１／２
　真円度は、円形形体の幾何学的に正しい円からの狂いの大きさである。真円度は、下記の式３を用いて算出される。 (Equation 2)
C * = [(a *) 2 + (b *) 2 ] 1/2
Roundness is the amount of deviation from the geometrically correct circle of a circular feature. The roundness is calculated using the following Equation 3.
　（式３）
真円度＝4π×（面積）/（円周）2
　例えば、画像解析部１１が、円形度ｒの代わりに種子面積ｓ、ＲＧＢ色空間のＲ値、Ｂ値、ＣＩＥＬＡＢ色空間のｂ＊値、真円度、または彩度Ｃ＊の何れかを算出・取得し、決定部１２が、種子のａ＊値、ならびに、種子面積ｓ、ＲＧＢ色空間のＲ値、Ｂ値、ＣＩＥＬＡＢ色空間のｂ＊値、真円度、または彩度Ｃ＊の何れかに基づいて八重咲き種子か否かを決定してもよい。 (Formula 3)
Roundness = 4π x (area) / (circumference) 2
For example, the image analysis unit 11 calculates one of the seed area s, the R value and the B value of the RGB color space, the b * value of the CIELAB color space, the roundness, and the saturation C * instead of the circularity r. • Obtained, and the determination unit 12 determines whether the seed a * value, the seed area s, the R value or B value in the RGB color space, the b * value in the CIELAB color space, the roundness, or the saturation C * . Whether or not it is a double-flowered seed may be determined based on.
　また、上述した形態では、種子のａ＊値および円形度ｒの２種類のパラメータを用いて八重鑑別を行ったが、この例に限定されず、種子のａ＊値を含む３種類以上のパラメータを用いて八重鑑別を行っても良い。例えば、特定の種子のａ＊値が下位ｘ％の範囲に含まれ、かつＲ値が下位ｙ％の範囲に含まれ、かつ種子面積ｓが上位ｚ％の範囲に含まれる場合に、上記特定の種子が、八重咲き種子であると判定しても良い。 Further, in the above-described embodiment, the octo-double discrimination is performed using two kinds of parameters, the seed a * value and the circularity r, but the present invention is not limited to this example, and three or more kinds of parameters including the seed a * value are used. You may perform double discrimination using. For example, when the a * value of a specific seed is included in the lower x% range, the R value is included in the lower y% range, and the seed area s is included in the upper z% range, The seed may be determined to be a double-flowered seed.
　図４に早麗のストックに係る八重咲き種子および一重咲き種子の撮像画像の例を示す。図４に示す撮像画像は白黒で表示されているため分かり難いが、八重咲き種子よりも一重咲き種子の方が、赤みが強い傾向がある。 Fig. 4 shows an example of a picked-up image of double-flowered seeds and single-flowered seeds related to the stock of Sorei. It is difficult to understand because the captured image shown in FIG. 4 is displayed in black and white, but single-flowered seeds tend to be more reddish than double-flowered seeds.
　図５のグラフ１０１は、早麗に関し、種子のａ＊値の算出結果を示すヒストグラムである。図５のグラフ１０１のヒストグラムは、上述したＴＩＦ画像に基づいて生成したものであり、図中の実線のグラフが八重咲き種子、破線のグラフが一重咲き種子である。図５のグラフ１０１に示すように、ａ＊値が小さい（５～１０）範囲では、八重咲き種子の頻度が、一重咲き種子の頻度よりも高い傾向があることが分かる。 The graph 101 in FIG. 5 is a histogram showing the calculation result of the a * value of seeds regarding the spoilage. The histogram of the graph 101 in FIG. 5 is generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double bloom seeds and the broken line graph indicates single bloom seeds. As shown in the graph 101 of FIG. 5, it can be seen that the frequency of double-flowered seeds tends to be higher than the frequency of single-flowered seeds in the range where the a * value is small (5 to 10).
　図５の表１０２は、早麗について特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位Ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。同図に示すように、種子選択率Ｘ％を小さくすればするほど、八重鑑別の正答率が高くなることが分かる。 The table 102 of FIG. 5 shows the seed selection rate and the correct answer rate regarding whether or not the a * value of the specific target seed for spoilage is included in the lower X% of the a * values of all the target seeds. It is a table which shows a relationship. As shown in the figure, it can be seen that the smaller the seed selection rate X%, the higher the correct answer rate of the double discrimination.
　種子選択率は、本実施形態の方法で八重鑑別を行った対象種子の数を対象種子の総数で除し、さらに１００を乗じて算出される値である。 The seed selection rate is a value calculated by dividing the number of target seeds that have been subjected to october discrimination by the method of the present embodiment by the total number of target seeds, and then multiplying by 100.
　なお、図５の表１０２に示す八重鑑別の正答率は、（１）2018年4月9日に撮影した３５０粒を対象とした鑑別結果と、（２）2018年4月13日に撮影した３００粒を対象とした鑑別結果と、の平均をとったものである。 In addition, the correct answer rate of the double identification shown in Table 102 of FIG. 5 is (1) the identification result for 350 grains taken on April 9, 2018, and (2) taken on April 13, 2018. It is the average of the discrimination results for 300 grains.
　図６にアイアンマリンに係る八重咲き種子および一重咲き種子の撮像画像の例を示す。図６に示す撮像画像は白黒で表示されているため分かり難いが、八重咲き種子よりも一重咲き種子の方が、赤みが強い傾向がある。しかしながら、その傾向は、早麗の場合に比べて弱く、アイアンマリンの場合は、種子の色の赤みでは、八重咲き種子と一重咲き種子とを見分けづらい傾向がある。 FIG. 6 shows an example of imaged images of double-flowered seeds and single-flowered seeds related to iron marine. Although the captured image shown in FIG. 6 is displayed in black and white, it is difficult to understand, but single-flowered seeds tend to be more reddish than double-flowered seeds. However, the tendency is weaker than that in the case of iris, and in the case of iron marine, the reddish seed color tends to make it difficult to distinguish between double-flowered seeds and single-flowered seeds.
　図７のグラフ１０３およびグラフ１０４は、アイアンマリンに関し、種子のａ＊値の算出結果を示すヒストグラムである。図７のグラフ１０３は、2018年4月9日に撮影した２２４粒を対象とした場合のヒストグラムである。また、図７のグラフ１０４は、2018年4月12日に撮影した２８４粒を対象とした場合のヒストグラムである。 Graphs 103 and 104 in FIG. 7 are histograms showing the results of calculating the a * value of seeds for iron marine. The graph 103 in FIG. 7 is a histogram in the case of targeting 224 particles taken on April 9, 2018. Further, the graph 104 in FIG. 7 is a histogram in the case where 284 particles photographed on April 12, 2018 are targeted.
　図７のグラフ１０３およびグラフ１０４のヒストグラムは、上述したＴＩＦ画像に基づいて生成したものであり、図中の実線のグラフが八重咲き種子、破線のグラフが一重咲き種子である。これらの図に示すように、ａ＊値が極めて小さい（１～４）範囲では、八重咲き種子の頻度と一重咲き種子の頻度が重なったり、頻度の高低が逆転したりしていることが分かる。 The histograms of the graph 103 and the graph 104 of FIG. 7 are generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double bloom seeds and the broken line graph indicates single bloom seeds. As shown in these figures, it can be seen that in the range where the a * value is extremely small (1 to 4), the frequency of double-flowered seeds and the frequency of single-flowered seeds overlap, and the frequency level is reversed.
　次に、図８の表１０５は、アイアンマリンについて特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位Ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。同図に示すように、アイアンマリンでは、種子選択率Ｘ％（１０～５０）の値に関わらず、八重鑑別の正答率は６６％～６９％の間の値が維持されている。 Next, Table 105 in FIG. 8 shows the seed selection rate and the correct answer regarding whether or not the a * value of the specific target seed for iron marine is included in the lower X% of the a * values of all the target seeds. It is a table which shows the relationship with a rate. As shown in the figure, in the iron marine, the correct answer rate of the double discrimination is maintained between 66% and 69% regardless of the seed selection rate X% (10 to 50).
　また、図８のグラフ１０６に示すように、早麗の場合の八重鑑別の正答率は、アイアンマリンの場合の八重鑑別の正答率より高く、これらの品種間で八重鑑別の正答率に大きな差があることが分かる。このため、上述した実施形態２のようにａ＊値に加えて円形度ｒなどのパラメータを用いて八重鑑別を行うことが好ましい。すなわち、複数の種子の特徴量を利用して八重鑑別を行うことで、鑑別精度を向上させることができる。 In addition, as shown in the graph 106 of FIG. 8, the correct answer rate of the double identification in the case of "Early" is higher than the correct answer rate of the double identification in the case of iron marine, and there is a large difference in the correct answer rate of the double identification between these varieties. I understand that there is. Therefore, it is preferable to perform the double discrimination using parameters such as the circularity r in addition to the a * value as in the second embodiment. That is, it is possible to improve the discrimination accuracy by performing the double discrimination by utilizing the characteristic quantities of a plurality of seeds.
　次に、対象種子のそれぞれについて、種子面積ｓ、ＲＧＢ色空間のＲ値、ＣＩＥＬＡＢ色空間のｂ＊値、真円度の各算出結果をヒストグラムで表すと、例えば、図９のグラフ１０７～グラフ１１０に示すようなヒストグラムとなった。なお、図９のヒストグラムは、上述したＴＩＦ画像に基づいて生成したものであり、図中の実線のグラフが八重咲き種子、破線のグラフが一重咲き種子である。 Next, for each of the target seeds, when the calculation results of the seed area s, the R value of the RGB color space, the b * value of the CIELAB color space, and the roundness are represented by a histogram, for example, graphs 107 to The resulting histogram is 110. Note that the histogram in FIG. 9 is generated based on the above-mentioned TIF image, and the solid line graph in the figure indicates double flower seeds and the broken line graph indicates single flower seeds.
　次に、アイアンマリンの特定の対象種子の特徴量が、対象の全ての種子の特徴量のうち、図１０の表１１１に示す範囲に含まれるかどうかで八重鑑別を行った。図１０の表１１１に示す条件（１）と条件（２）とをともに満たす種子を八重咲き種子と判定した場合、図１０の表１１２に示す種子選択率２４％にて、八重鑑別の正答率９１％の結果が得られた。 Next, double identification was performed based on whether the characteristic amount of the specific target seed of iron marine is included in the range shown in Table 111 of FIG. 10 among the characteristic amounts of all the target seeds. When the seeds that satisfy both the condition (1) and the condition (2) shown in Table 111 of FIG. 10 are determined to be double-flowered seeds, the correct answer rate of double identification 91 at the seed selection rate of 24% shown in Table 112 of FIG. % Results were obtained.
　次に、早麗の特定の対象種子の特徴量が、対象の全ての種子の特徴量のうち、図１１の表１１３に示す範囲に含まれるかどうかで八重鑑別を行った。図１１の表１１３に示す条件（１）と条件（２）とをともに満たす種子を八重咲き種子と判定した場合、図１１の表１１４に示す種子選択率２９％にて、八重鑑別の正答率９３％の結果が得られた。 Next, double identification was performed based on whether the characteristic amount of the specific target seed of Saury is included in the range shown in Table 113 of FIG. 11 among the characteristic amounts of all the target seeds. When the seeds satisfying both the condition (1) and the condition (2) shown in Table 113 of FIG. 11 are determined to be double-flowered seeds, the correct selection rate of double identification is 93 at the seed selection rate of 29% shown in Table 114 of FIG. % Results were obtained.
　複数の特徴量を組合せて八重鑑別を行った場合、図１２のグラフに示すように、早麗であるか、アイアンマリンであるかによらず、八重鑑別の正答率は向上した。図１２に示す「早麗単」は、早麗のストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「早麗複」は、早麗のストックについて複数の特徴量にて八重鑑別を行った結果を示す。「アイアンマリン単」は、アイアンマリンのストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「アイアンマリン複」は、アイアンマリンのストックについて複数の特徴量にて八重鑑別を行った結果を示す。 When the double discrimination is performed by combining a plurality of feature amounts, the correct answer rate of the double discrimination is improved regardless of whether it is quick or iron marine, as shown in the graph of FIG. “Sarei-unita” shown in FIG. 12 shows the result of performing double identification with a single feature amount for Saurya stock. In addition, "Sayori-doku" indicates the result of double discrimination of the stock of Sarayi using a plurality of characteristic quantities. "Iron Marine Single" indicates the result of double discrimination with a single characteristic amount for the iron marine stock. Further, "Iron Marine Compound" indicates the result of double discrimination of iron marine stock with a plurality of characteristic amounts.
　次に、図１３の表１１５に基づき、八重鑑別に用いる指標として、黄色色調値（ＣＩＥＬＡＢ色空間のｂ＊値）のみを用いて鑑別を行った結果を示す。ここでは、特定の対象種子のｂ＊値が、対象の全ての種子ｂ＊値のうち、下位Ｙ％に含まれるかどうかで八重を選別した。種子選択率Ｙは、１０～５０の範囲とした。同図に示すように、種子選択率１０％～５０％にて、早麗の八重鑑別の正答率６２％～７３％の結果が得られた。また、アイアンマリンの八重鑑別の正答率６０％～６５％の結果が得られた。 Next, based on Table 115 in FIG. 13, the result of performing the discrimination using only the yellow color tone value (b * value of the CIELAB color space) as an index used for the octo-fold discrimination is shown. Here, Yae was selected depending on whether or not the b * value of a specific target seed is included in the lower Y% of all target seed b * values. The seed selectivity Y was in the range of 10-50. As shown in the figure, when the seed selection rate was 10% to 50%, the correct answer rate of 62% to 73% was obtained in the double identification of Sauryi. In addition, the result of the correct answer rate of the double identification of iron marine was 60% to 65%.
　次に、図１３の表１１６に基づき、八重鑑別に用いる指標として、青色色調値（ＲＧＢ色空間のＢ値）のみを用いて鑑別を行った結果を示す。特定の対象種子のＢ値が、対象の全ての種子Ｂ値のうち、上位Ｚ％に含まれるかどうかで八重を選別した。種子選択率Ｙは、１０～５０の範囲とした。同図に示すように、種子選択率１０％～５０％にて、早麗の八重鑑別の正答率６１％～６３％の結果が得られた。また、アイアンマリンについては、種子選択率１０および２０の範囲において、八重鑑別の正答率５９％、６２％の結果が得られた。 Next, based on the table 116 of FIG. 13, the result of performing discrimination using only the blue color tone value (B value of the RGB color space) as an index used for octo-identification is shown. Yae was selected based on whether the B value of a specific target seed is included in the top Z% of all the target B values of the target. The seed selectivity Y was in the range of 10-50. As shown in the figure, when the seed selection rate was 10% to 50%, the correct answer rate of 61% to 63% was obtained in the double identification of splendid rice. With regard to iron marine, in the range of seed selection rates of 10 and 20, the results of the correct answer rates of double discrimination were 59% and 62%, respectively.
　以上のように、八重鑑別に用いる指標としてｂ＊値やＢ値のみを用いた場合、八重鑑別の正答率は、ａ＊値のみを用いた場合と比較して多少劣るものの、ある程度向上させることが可能であることが分かった。 As described above, when only the b * value or the B value is used as the index used for the occultation, the correct answer rate of the occultation is slightly inferior to the case where only the a * value is used, but should be improved to some extent. It turns out that is possible.
　次に、図１４の表１１７は、早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれについて特定の対象種子のａ＊値が、対象の全ての種子のａ＊値のうち、下位Ｘ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。同図に示すように、種子選択率Ｘ％を小さくすればするほど、八重鑑別の正答率が高くなる。 Next, Table 117 in FIG. 14 shows that the a * value of a specific target seed for each of Soryo, iron marine, iron cherry, and iron white is in the lower X% of the a * values of all target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is contained. As shown in the figure, the smaller the seed selection rate X% is, the higher the correct answer rate of the double discrimination is.
　なお、図１４の表１１７に示す早麗についての八重鑑別の正答率は、（１）2018年4月9日に撮影した３５０粒を対象とした鑑別結果と、（２）2018年4月13日に撮影した３００粒を対象とした鑑別結果と、（３）2018年9月3日に撮影した２００粒を対象とした鑑別結果と、（４）2018年9月5日に撮影した２００粒を対象とした鑑別結果と、の平均をとったものである。 In addition, the correct answer rate of the double identification for Sauryi shown in Table 117 of FIG. 14 is (1) the discrimination result for 350 grains taken on April 9, 2018, and (2) April 13, 2018. Identification result for 300 tablets taken on the day, (3) Identification result for 200 tablets taken on September 3, 2018, and (4) 200 tablets taken on September 5, 2018 It is the average of the discrimination result for.
　次に、図１４の表１１７に示すアイアンマリンについての八重鑑別の正答率は、（１）2018年4月9日に撮影した２５０粒を対象とした鑑別結果と、（２）2018年4月12日に撮影した３００粒を対象とした鑑別結果と、の平均をとったものである。 Next, the correct answer rate of the double identification for iron marine shown in Table 117 of FIG. 14 is (1) the identification result of 250 tablets taken on April 9, 2018, and (2) April 2018. It is the average of the discrimination results of 300 tablets taken on the 12th.
　また、図１４の表１１７に示すアイアンチェリーおよびアイアンホワイトのそれぞれについての八重鑑別の正答率は、（１）2018年9月3日に撮影した２５０粒を対象とした鑑別結果と、（２）2018年9月5日に撮影した２５０粒を対象とした鑑別結果と、の平均をとったものである。 In addition, the correct answer rate of the double identification for each of the iron cherry and the iron white shown in Table 117 of FIG. 14 is (1) the discrimination result of 250 grains taken on September 3, 2018, and (2) It is the average of the discrimination results of 250 tablets taken on September 5, 2018.
　次に、図１４のグラフ１１８は、ａ＊値による八重鑑別の種子選択率と正答率との品種比較を示すグラフである。同図に示すように、早麗、アイアンチェリーおよびアイアンホワイトは、いずれも種子選択率が低くなるにつれて、八重鑑別の正答率が高くなる傾向がある。一方、アイアンマリンは、種子選択率を変化させても八重鑑別の正答率はあまり変化しない傾向がある。 Next, a graph 118 in FIG. 14 is a graph showing a variety comparison between the seed selection rate and the correct answer rate in the double discrimination by the a * value. As shown in the same figure, with respect to Saury, iron cherry and iron white, the correct answer rate for double discrimination tends to increase as the seed selection rate decreases. On the other hand, in the case of iron marine, the rate of correct answers in double discrimination tends not to change so much even if the seed selection rate is changed.
　次に、図１５の表１１９は、早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれについて特定の対象種子のｂ＊値が、対象の全ての種子のｂ＊値のうち、下位Ｙ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。同図によれば、種子選択率２０％以下では、アイアンチェリーおよびアイアンホワイトの八重鑑別の正答率が、早麗およびアイアンマリンに比較して高くなっている。 Next, Table 119 of FIG. 15 shows that the b * value of a specific target seed for each of Saury, Iron Marine, Iron Cherry, and Iron White is in the lower Y% of the b * values of all the target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is contained. According to the figure, when the seed selection rate is 20% or less, the correct answer rate of the double discrimination of iron cherry and iron white is higher than that of Saurya and iron marine.
　次に、図１５の表１２０は、早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれについて特定の対象種子のＢ値が、対象の全ての種子のＢ値のうち、下位Ｚ％に含まれるかどうかに関し、種子選択率と正答率との関係を示す表である。同図によれば、種子選択率２０％以下では、アイアンホワイトの八重鑑別の正答率が、早麗おアイアンマリンおよびアイアンチェリーに比較して高くなっている。 Next, in the table 120 of FIG. 15, the B value of a particular target seed for each of Saury, iron marine, iron cherry, and iron white is included in the lower Z% of the B values of all the target seeds. It is a table which shows the relationship between a seed selection rate and a correct answer rate regarding whether it is. According to the figure, when the seed selection rate is 20% or less, the correct answer rate of the double identification of iron white is higher than that of Sauryo iron marine and iron cherry.
　（種子の面積、色情報および形態情報の組合せによる八重鑑別の結果）
　次に、図１６に基づき、種子の面積、色情報および形態情報の組合せによる八重鑑別の結果について説明する。 (Results of double discrimination by combination of seed area, color information and morphological information)
Next, based on FIG. 16, the results of double identification by combining the area of seeds, color information and morphological information will be described.
　早麗については、条件（１）〔面積：上位１０％，Ｒ値：下位２０％，ａ＊値：下位２０％〕のいずれかに該当し、かつ条件（２）〔Ｂ値：下位１０％，ａ＊値：上位４０％〕のいずれにも該当しない種子を八重として選別した。この結果、図１６の表１２１に示すように、種子選択率３０％で、八重鑑別の正答率が９０％の高い値を示した。 For quick access, any of the conditions (1) [area: upper 10%, R value: lower 20%, a * value: lower 20%] is satisfied, and condition (2) [B value: lower 10%]. , A * value: top 40%] was selected as a double layer. As a result, as shown in Table 121 in FIG. 16, the seed selection rate was 30%, and the correct answer rate in the double discrimination was 90%.
　次に、アイアンマリンについては、条件（１）〔面積：上位５％，Ｒ値：下位５～１０％，ａ＊値：下位５５％，円形度：下位５％〕のいずれかに該当し、かつ条件（２）〔面積：下位１０％，Ｒ値：上位１０～３０％，ａ＊値：下位３％，ｂ＊値：上位３５％，彩度：上位３５％または平均値×０．８以下，真円度：下位３％〕のいずれにも該当しない種子を八重として選別した。この結果、図１６の表１２２に示すように、種子選択率２４％で、八重鑑別の正答率が９１％の高い値を示した。 Next, regarding the iron marine, any of the conditions (1) [area: upper 5%, R value: lower 5 to 10%, a * value: lower 55%, circularity: lower 5%], And condition (2) [area: lower 10%, R value: upper 10 to 30%, a * value: lower 3%, b * value: upper 35%, saturation: upper 35% or average value x 0.8 Hereinafter, seeds that do not correspond to any of the roundness: lower 3%] were selected as double layers. As a result, as shown in Table 122 in FIG. 16, the seed selection rate was 24%, and the correct answer rate in the double discrimination was 91%, which was a high value.
　次に、アイアンチェリーについては、条件（１）〔色差：下位１０％，彩度：下位２０％〕のいずれかに該当し、かつ条件（２）〔面積：下位１５％または上位１０％，Ｒ値：上位３％，ａ＊値：上位５％，ｂ＊値：上位１５％〕のいずれにも該当しない種子を八重として選別した。この結果、図１６の表１２３に示すように、種子選択率２６％で、八重鑑別の正答率が８８％の高い値を示した。 Next, regarding the iron cherry, it corresponds to any one of the condition (1) [color difference: lower 10%, saturation: lower 20%], and the condition (2) [area: lower 15% or upper 10%, R]. Values: top 3%, a * value: top 5%, b * value: top 15%] Seeds that did not correspond to any of the above were selected as double layers. As a result, as shown in Table 123 in FIG. 16, the seed selection rate was 26%, and the correct answer rate in the double discrimination was 88%.
　次に、アイアンホワイトについては、条件（１）〔ａ＊値：下位３５％〕に該当し、かつ条件（２）〔面積：下位１５％または上位３％，Ｒ値：上位３％，Ｂ値：下位２％，ｂ＊値：下位３％，アスペクト比：下位５％〕のいずれにも該当しない種子を八重として選別した。ここで、アスペクト比は、対象種子を楕円にフィッティングさせたときにおける長軸の長さを短軸で割った値である。上記の八重鑑別の結果、図１６の表１２４に示すように、種子選択率２３％で、八重鑑別の正答率が８４％の高い値を示した。 Next, regarding the iron white, the condition (1) [a * value: lower 35%] is satisfied, and the condition (2) [area: lower 15% or upper 3%, R value: upper 3%, B value] : Lower 2%, b * value: lower 3%, aspect ratio: lower 5%], seeds that did not correspond to any of the above were selected as double layers. Here, the aspect ratio is a value obtained by dividing the length of the major axis by fitting the target seed to an ellipse by the minor axis. As a result of the above-mentioned double discrimination, as shown in Table 124 of FIG. 16, the seed selectivity was 23%, and the correct answer rate of double discrimination was 84%, which was a high value.
　次に、図１７は、単一または複数の特徴量を用い八重鑑別を行った場合における早麗、アイアンマリン、アイアンチェリーおよびアイアンホワイトのそれぞれの種子選択率と正答率との関係を示すグラフである。 Next, FIG. 17 is a graph showing the relationship between the seed selection rate and the correct answer rate of each of Saury, iron marine, iron cherry and iron white in the case of performing double discrimination using a single or a plurality of feature amounts. is there.
　同図に示すように、単一の特徴量を用い八重鑑別を行った場合、早麗、アイアンチェリーおよびアイアンホワイトは、いずれも種子選択率が低くなるにつれて、八重鑑別の正答率が高くなる傾向がある。一方、アイアンマリンは、種子選択率を変化させても八重鑑別の正答率はあまり変化しない傾向がある。 As shown in the figure, when double discrimination is performed using a single feature amount, Saury, Iron Cherry, and Iron White tend to have a higher correct answer rate for double discrimination as the seed selection rate decreases. There is. On the other hand, in the case of iron marine, the rate of correct answers in double discrimination tends not to change so much even if the seed selection rate is changed.
　次に、複数の特徴量を組合せて八重鑑別を行った場合、図１７のグラフに示すように、早麗であるか、アイアンマリンであるか、アイアンチェリーであるか、アイアンホワイトであるかによらず、八重鑑別の正答率は向上した。図１７に示す「早麗単」は、早麗のストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「早麗複」は、早麗のストックについて複数の特徴量にて八重鑑別を行った結果を示す。「アイアンマリン単」は、アイアンマリンのストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「アイアンマリン複」は、アイアンマリンのストックについて複数の特徴量にて八重鑑別を行った結果を示す。 Next, in the case of performing double discrimination by combining a plurality of feature amounts, as shown in the graph of FIG. 17, whether it is quick, iron marine, iron cherry, or iron white. Therefore, the rate of correct answers for double discrimination was improved. “Sayrei unit” shown in FIG. 17 shows the result of performing double discrimination with a single feature amount on the stock of Sarayi. In addition, "Sayori-doku" indicates the result of double discrimination of the stock of Sarayi using a plurality of characteristic quantities. "Iron Marine Single" indicates the result of double discrimination with a single characteristic amount for the iron marine stock. Further, "Iron Marine Compound" indicates the result of double discrimination of iron marine stock with a plurality of characteristic amounts.
　次に、「アイアンチェリー単」は、アイアンチェリーのストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「アイアンチェリー複」は、アイアンチェリーのストックについて複数の特徴量にて八重鑑別を行った結果を示す。 Next, "Iron Cherry Single" shows the result of double discrimination of iron cherry stock with a single feature amount. "Iron cherry compound" indicates the result of double discrimination of iron cherry stock with a plurality of characteristic amounts.
　また、「アイアンホワイト単」は、アイアンホワイトのストックについて単一の特徴量にて八重鑑別を行った結果を示す。また、「アイアンホワイト複」は、アイアンホワイトのストックについて複数の特徴量にて八重鑑別を行った結果を示す。 Also, "Iron White Single" shows the result of performing double discrimination with a single feature amount on the Iron White stock. “Iron white compound” indicates the result of double discrimination of iron white stock with a plurality of characteristic amounts.
　〔実施形態３〕
　次に、図１８は、本発明の実施形態３に係るカラーセンサ６００および情報処理装置３００の機能的構成を示すブロック図である。本実施形態では、実施形態１および２の撮像装置５００に替えてカラーセンサ６００を用いている点で、上述した形態と異なっている。すなわち、本実施形態では、複数のストックの種子からの反射光の光情報が、カラーセンサ６００によって検出される検出情報となっている。これにより、上記光情報が撮像装置５００によって取得される画像情報である場合と比較して、色調情報を取得する処理が簡単になり、処理コストを低減させることができる。また、本実施形態の情報処理装置３００は、制御部４が、画像解析部１１に替えて、データ解析部１１ａを備えている点で上述した形態と異なっている。 [Embodiment 3]
Next, FIG. 18 is a block diagram showing the functional configurations of the color sensor 600 and the information processing apparatus 300 according to the third embodiment of the present invention. The present embodiment is different from the above-described embodiment in that a color sensor 600 is used instead of the imaging device 500 of the first and second embodiments. That is, in this embodiment, the light information of the reflected light from the seeds of the plurality of stocks is the detection information detected by the color sensor 600. As a result, as compared with the case where the optical information is image information acquired by the imaging device 500, the process of acquiring the color tone information becomes simple and the processing cost can be reduced. The information processing apparatus 300 of the present embodiment is different from the above-described embodiment in that the control unit 4 includes a data analysis unit 11a instead of the image analysis unit 11.
　カラーセンサ６００は、光源と受光部とを備えており、光源から出射した光が種子に当たって反射した反射光を受光部で検出するようになっている。カラーセンサ６００は、ＲＧＢ値を出力するものであっても良く、直接ａ＊値やｂ＊値を出力するものであって良い。なお、カラーセンサ６００が、ＲＧＢ値を出力する場合、データ解析部１１ａは、上記ＲＧＢ値を基に、ａ＊値やｂ＊値を算出する。 The color sensor 600 includes a light source and a light receiving unit, and the light receiving unit detects the reflected light, which is the light emitted from the light source hitting the seed and reflected. The color sensor 600 may output RGB values, or may directly output a * values or b * values. When the color sensor 600 outputs RGB values, the data analysis unit 11a calculates a * values and b * values based on the RGB values.
　直接ａ＊値やｂ＊値を出力するカラーセンサ６００の具体例としては、色彩色差計（コニカミノルタ社製；ＣＲ－２００）を例示することができる。この色彩色差計による色調情報の取得方法は、以下のとおりである。色彩色差計の光源をＣまたはＤ６５のいずれかに設定する。測定開始前には白色校正板によって白色校正を行う。全ての対象種子の測定は同一の設定および校正条件で行う。種子を白い板に置き、設定・校正済みの色彩色差計を用いてａ＊値およびｂ＊値を１粒ずつ測定する。このとき、早麗の種子のａ＊値およびｂ＊値は、それぞれ、図１９の符号１２５で示すグラフ、および符号１２６で示すグラフのような分布となる。 As a specific example of the color sensor 600 that directly outputs the a * value or the b * value, a colorimeter (CR-200 manufactured by Konica Minolta Co., Ltd.) can be exemplified. The method of acquiring color tone information by this color difference meter is as follows. The light source of a color difference meter is set to either C or D 65. Before starting measurement, perform white calibration with a white calibration plate. All target seeds are measured under the same settings and calibration conditions. The seeds are placed on a white plate, and the a * value and the b * value are measured one by one using a colorimeter that has been set and calibrated. At this time, the a * value and the b * value of the early seeds have distributions as shown by a graph 125 and a graph 126 in FIG. 19, respectively.
　〔ＡＩの適用例〕
　画像解析部１１（またはデータ解析部１１ａ）による赤色色調情報等（特徴情報）の取得の手法、および決定部１２による八重咲き種子か否かの決定の手法は、上述の各実施形態に限定されない。上記の各手法として、例えば機械学習、若しくは、以下に示すような機械学習的手法の何れかまたはそれらの組合せを用いることができる。 [Application example of AI]
The method of acquiring red color tone information and the like (feature information) by the image analysis unit 11 (or the data analysis unit 11a) and the method of determining whether or not double-flowered seeds are determined by the determination unit 12 are not limited to the above-described embodiments. As each of the above methods, for example, machine learning, or any of the following machine learning methods or a combination thereof can be used.
　・サポートベクターマシン（SVM: Support Vector Machine）
　・クラスタリング（Clustering）
　・帰納論理プログラミング（ILP: Inductive Logic Programming）
　・遺伝的アルゴリズム（GP: Genetic Programming）
　・ベイジアンネットワーク（BN: Bayesian Network）
　・ニューラルネットワーク（NN: Neural Network）
　ニューラルネットワークを用いる場合、畳み込み処理を含む畳み込みニューラルネットワーク（ＣＮＮ:Convolutional Neural Network）を用いてもよいし、再帰的処理を含むリカレントニューラルネットワーク（ＲＮＮ:Recurrent Neural Network）を用いてもよい。なお、鑑別装置１０が行っているような画像処理の場合であれば、ＣＮＮを用いる方が好ましい。 ・ Support Vector Machine (SVM)
・ Clustering
・ Inductive Logic Programming (ILP)
・ Genetic Algorithm (GP)
・ Bayesian Network (BN)
・ Neural Network (NN)
When the neural network is used, a convolutional neural network (CNN: Convolutional Neural Network) including convolution processing may be used, or a recurrent neural network (RNN: Recurrent Neural Network) including recursive processing may be used. In the case of image processing performed by the identification device 10, it is preferable to use CNN.
　ＣＮＮを用いる場合、より具体的には、ニューラルネットワークに含まれる１または複数の層（レイヤ）として、畳み込み演算を行う畳み込み層を設け、当該層に入力される入力データに対してフィルタ演算（積和演算）を行う構成としてもよい。またフィルタ演算を行う際には、パディング等の処理を併用したり、適宜設定されたストライド幅を採用したりしてもよい。また、ニューラルネットワークとして、数十～数千層に至る多層型または超多層型のニューラルネットワークを用いてもよい。 When the CNN is used, more specifically, a convolutional layer that performs a convolutional operation is provided as one or a plurality of layers included in the neural network, and a filter operation (product) is performed on input data input to the layer. It may be configured to perform a sum operation). When performing the filter operation, processing such as padding may be used in combination, or an appropriately set stride width may be employed. Further, as the neural network, a multi-layer type or super multi-layer type neural network having tens to thousands of layers may be used.
　さらに、画像解析部１１による赤色色調情報等の取得、および決定部１２による八重咲き種子か否かの決定に用いられる機械学習は、教師あり学習であってもよいし、教師なし学習であってもよい。 Furthermore, the machine learning used by the image analysis unit 11 to acquire red color tone information and the like and the determination unit 12 to determine whether or not double-flowered seeds are used may be supervised learning or unsupervised learning. Good.
　ここで、鑑別装置１０が教師あり学習を行う場合、すなわち鑑別装置１０が教師データを用いて機械学習する場合、この機械学習に用いられる教師データとしては、以下に示すような構造を有するものが想定される。「教師データ」とは、鑑別装置１０が複数のストックの種子について八重咲き種子を鑑別することを機械学習する際に適用される、学習モデルを構築ないし更新するためのデータを指す。 Here, when the classification device 10 performs learning with a teacher, that is, when the classification device 10 performs machine learning using teacher data, the teacher data used for this machine learning has a structure as shown below. is assumed. The “teacher data” refers to data for constructing or updating a learning model, which is applied when the identification device 10 performs machine learning to identify double-flowered seeds for a plurality of stock seeds.
　例えば、（ｉ）画像解析部１１が少なくとも複数のストックの種子の赤色色調情報等を取得する処理に用いられる、当該複数のストックの種子の画像データと、（ｉｉ）画像データに含まれる種子の成育後に咲く花の形態を示す形態データと、を含む構造を有する教師データが想定される。このような画像データと形態データが対応付けられた教師データを鑑別装置１０の記憶部３等に記録しておき、鑑別装置１０に学習させることで、鑑別装置１０の鑑別精度を簡易かつ飛躍的に向上させることができる。 For example, (i) the image analysis unit 11 uses the image data of the seeds of the plurality of stocks used in the process of acquiring the red color tone information of the seeds of the plurality of stocks, and (ii) the seeds of the seeds included in the image data. Teacher data having a structure including morphological data indicating the morphology of flowers blooming after growth is assumed. By recording the teacher data in which the image data and the morphological data are associated with each other in the storage unit 3 or the like of the classification device 10 and causing the classification device 10 to learn, the classification accuracy of the classification device 10 can be simplified and dramatically improved. Can be improved.
　〔ソフトウェアによる実現例〕
　鑑別装置１０の制御ブロック（特に画像解析部１１および決定部１２）は、集積回路（ＩＣチップ）等に形成された論理回路（ハードウェア）によって実現してもよいし、ソフトウェアによって実現してもよい。 [Example of software implementation]
The control block (particularly the image analysis unit 11 and the determination unit 12) of the identification device 10 may be realized by a logic circuit (hardware) formed in an integrated circuit (IC chip) or the like, or may be realized by software. Good.
　後者の場合、鑑別装置１０は、各機能を実現するソフトウェアであるプログラムの命令を実行するコンピュータを備えている。このコンピュータは、例えば１つ以上のプロセッサを備えていると共に、上記プログラムを記憶したコンピュータ読み取り可能な記録媒体を備えている。そして、上記コンピュータにおいて、上記プロセッサが上記プログラムを上記記録媒体から読み取って実行することにより、本発明の目的が達成される。上記プロセッサとしては、例えばＣＰＵ（Central Processing Unit）を用いることができる。上記記録媒体としては、「一時的でない有形の媒体」、例えば、ＲＯＭ（Read Only Memory）等の他、テープ、ディスク、カード、半導体メモリ、プログラマブルな論理回路などを用いることができる。また、上記プログラムを展開するＲＡＭ（Random Access Memory）などをさらに備えていてもよい。また、上記プログラムは、該プログラムを伝送可能な任意の伝送媒体（通信ネットワークや放送波等）を介して上記コンピュータに供給されてもよい。なお、本発明の一態様は、上記プログラムが電子的な伝送によって具現化された、搬送波に埋め込まれたデータ信号の形態でも実現され得る。 In the latter case, the discrimination device 10 includes a computer that executes the instructions of a program that is software that realizes each function. The computer includes, for example, one or more processors and a computer-readable recording medium that stores the program. Then, in the computer, the processor reads the program from the recording medium and executes the program to achieve the object of the present invention. As the processor, for example, a CPU (Central Processing Unit) can be used. Examples of the recording medium include “temporary tangible media” such as ROM (Read Only Memory), tapes, disks, cards, semiconductor memories, and programmable logic circuits. Further, a RAM (Random Access Memory) for expanding the program may be further provided. Further, the program may be supplied to the computer via an arbitrary transmission medium (a communication network, a broadcast wave, or the like) capable of transmitting the program. Note that one embodiment of the present invention can also be realized in the form of a data signal embedded in a carrier wave, in which the program is embodied by electronic transmission.
　〔まとめ〕
　本発明の態様１に係る鑑別装置は、八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別装置であって、上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得部と、上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定部と、を備えている。上記構成によれば、ストックの種子の色に含まれる所定の色成分の色調値を利用することにより、ストックの様々な品種について種子の状態での八重鑑別を可能にすることができる。 [Summary]
The discrimination apparatus according to Aspect 1 of the present invention is a discrimination apparatus for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, and the light of reflected light from the seeds of the plurality of stocks. From the information, for each of the seeds of the plurality of stock, an information acquisition unit that acquires at least color tone information indicating the color tone value of a predetermined color component included in the color of the seed, and the predetermined number of seeds of the plurality of stocks. Color tone values of the color components are ranked in descending order, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range. By determining whether or not the particular seed of the stock is the double-flowered seed, a determining unit is provided. According to the above configuration, by utilizing the tone value of the predetermined color component contained in the color of the seed of the stock, it is possible to perform the double discrimination in the seed state for various varieties of the stock.
　本発明の態様２に係る鑑別装置は、上記態様１において、上記光情報が、撮像装置によって取得される画像情報であっても良い。上記構成によれば、色調情報の他、種子の面積や種子の断面形状の丸みの程度を示す情報など、種子の大きさや形に係る情報を取得することが可能になる。 In the discrimination device according to the second aspect of the present invention, in the first aspect, the optical information may be image information acquired by the imaging device. According to the above configuration, in addition to the color tone information, it is possible to acquire information relating to the size and shape of the seed, such as information indicating the area of the seed and the degree of roundness of the cross-sectional shape of the seed.
　本発明の態様３に係る鑑別装置は、上記態様１において、上記光情報が、カラーセンサによって検出される検出情報であっても良い。上記構成によれば、上記光情報が撮像装置によって取得される画像情報である場合と比較して、色調情報を取得する処理が簡単になり、処理コストを低減させることができる。 In the discrimination apparatus according to aspect 3 of the present invention, in the aspect 1, the optical information may be detection information detected by a color sensor. According to the above configuration, as compared with the case where the optical information is image information obtained by the image pickup apparatus, the process of obtaining the color tone information is simplified and the processing cost can be reduced.
　本発明の態様４に係る鑑別装置は、上記態様１～３の何れかにおいて、上記所定の色成分の色調値は、赤色成分の色調値であり、上記色調情報は、上記赤色成分の色調値を示す赤色色調情報であっても良い。ストックの種子の色は、品種によらず、一重咲き種子の方が八重咲き種子と比較して赤みが強い傾向がある。このため、上記構成によれば、種子の色の赤みの強さを示す指標として、赤色色調値を利用することにより、ストックの様々な品種について種子の状態での八重鑑別を可能にすることができる。 The discrimination apparatus according to aspect 4 of the present invention is the discrimination apparatus according to any one of aspects 1 to 3, wherein the color tone value of the predetermined color component is the color tone value of the red component, and the color tone information is the color tone value of the red component. It may be red color tone information indicating. Regarding the color of the stock seeds, the single-flowered seeds tend to be more reddish than the double-flowered seeds, regardless of the variety. Therefore, according to the above configuration, by using the red color tone value as an index indicating the redness of the seed color, it is possible to perform double discrimination in the seed state for various varieties of stock. it can.
　本発明の態様５に係る鑑別装置は、上記態様１～４の何れかにおいて、上記決定部は、上記ストックの品種に応じて、上記第１範囲を変更することができる。上記構成によれば、ストックの品種に応じて、第１範囲を適切な範囲に変更することにより、八重鑑別の精度を上げることが可能になる。 In the discrimination apparatus according to Aspect 5 of the present invention, in any one of Aspects 1 to 4, the determining unit can change the first range according to the stock type. According to the above configuration, by changing the first range to an appropriate range according to the type of stock, it is possible to improve the accuracy of double identification.
　本発明の態様６に係る鑑別装置は、上記態様４において、上記決定部は、特定の上記ストックの種子に係る上記赤色成分の色調値が、上記第１範囲としての、すべてのストックの種子に係る上記赤色成分の色調値のうちの下位５０％の範囲に含まれているか否かを判定する。上記構成によれば、例えば、ストックの品種が早麗である場合に、８０％以上の高い精度で八重鑑別を行うことが可能になる。 The discrimination device according to aspect 6 of the present invention is, in the aspect 4, in the determination unit, the tone value of the red component related to the seed of the specific stock is the seed of all the stocks as the first range. It is determined whether or not the color tone value of the red component is included in the lower 50% range. According to the above configuration, for example, when the variety of stock is early, it is possible to perform double identification with high accuracy of 80% or more.
　本発明の態様７に係る鑑別装置は、上記態様２において、上記情報取得部は、さらに上記種子の面積を示す面積情報を取得し、上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記面積が所定の第２範囲に含まれているか否かを判定する。上記構成によれば、所定の色成分の色調値と種子の面積とを組合せることで、八重鑑別の精度をより高くすることができる。 The discrimination apparatus according to the seventh aspect of the present invention, in the second aspect, the information acquisition unit further acquires area information indicating the area of the seed, the determination unit, for each of the seeds of the plurality of stock, Further, it is determined whether or not the above area is included in the predetermined second range. According to the above configuration, by combining the color tone value of the predetermined color component and the area of the seed, it is possible to further increase the accuracy of the double identification.
　本発明の態様８に係る鑑別装置は、上記態様２において、上記情報取得部は、さらに上記種子の断面形状の丸みの程度を示す丸み情報を取得し、上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記丸みの程度が所定の第３範囲に含まれているか否かを判定する。上記構成によれば、所定の色成分の色調値と種子の丸み情報とを組合せることで、八重鑑別の精度をより高くすることができる。 The discrimination apparatus according to Aspect 8 of the present invention is the Aspect 2 according to Aspect 2, wherein the information acquisition unit further acquires roundness information indicating the degree of roundness of the cross-sectional shape of the seed, and the determination unit is configured to store the plurality of stocks. For each seed, it is further determined whether or not the degree of roundness is included in the predetermined third range. According to the above configuration, by combining the tone value of the predetermined color component and the roundness information of the seed, it is possible to further improve the accuracy of the double identification.
　本発明の態様９に係る鑑別装置は、上記態様１から８までの何れかにおいて、上記情報取得部は、さらに上記種子の色に含まれる黄色成分の色調値を表す黄色色調値情報を取得し、上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記黄色成分の色調値が、所定の第４範囲に含まれているか否かを判定する。上記構成によれば、所定の色成分（黄色成分を除く）の色調値と黄色色調値とを組合せることで、八重鑑別の精度をより高くすることができる。 In the discrimination apparatus according to aspect 9 of the present invention, in any of the aspects 1 to 8, the information acquisition unit further acquires yellow color tone value information indicating a color tone value of a yellow component included in the seed color. The determining unit further determines whether or not the color tone value of the yellow component is included in the predetermined fourth range for each of the seeds of the plurality of stocks. According to the above configuration, by combining the tone value of a predetermined color component (excluding the yellow component) and the yellow tone value, it is possible to further improve the accuracy of the double identification.
　本発明の態様１０に係る鑑別装置は、上記態様１から９までの何れかにおいて、上記情報取得部は、さらに上記種子の色に含まれる青色成分の色調値を表す青色色調値情報を取得し、上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記青色成分の色調値が、所定の第５範囲に含まれているか否かを判定する。上記構成によれば、所定の色成分（青色成分を除く）の色調値と青色色調値とを組合せることで、八重鑑別の精度をより高くすることができる。 In the discrimination apparatus according to aspect 10 of the present invention, in any one of aspects 1 to 9, the information acquisition unit further acquires blue color tone value information representing a color tone value of a blue component included in the seed color. The determination unit further determines, for each of the plurality of stock seeds, whether the color tone value of the blue component is included in a predetermined fifth range. According to the above configuration, by combining the color tone value of a predetermined color component (excluding the blue component) and the blue color tone value, it is possible to further improve the accuracy of the double identification.
　本発明の態様１１に係る鑑別装置は、上記態様１から１０までの何れかにおいて、上記情報取得部は、さらに上記種子の彩度を示す彩度情報を取得し、上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記彩度が所定の第６範囲に含まれているか否かを判定する。上記構成によれば、所定の色成分の色調値と彩度とを組合せることで、八重鑑別の精度をより高くすることができる。 The discrimination apparatus according to aspect 11 of the present invention is the discrimination apparatus according to any one of aspects 1 to 10, wherein the information acquisition unit further acquires saturation information indicating the saturation of the seed, and the determination unit includes the plurality of units. For each of the seeds of the stock, it is further determined whether or not the saturation is included in the predetermined sixth range. According to the above configuration, by combining the tone value and the saturation of the predetermined color component, it is possible to further improve the accuracy of the octo-identification.
　本発明の態様１２に係る鑑別装置は、上記態様４において、上記赤色成分の色調値が、Ｌａｂ色空間におけるａ＊値である。上記構成によれば、所定の色成分の色調値としてＬａｂ色空間のａ＊値を用いることで、八重鑑別の精度をより高くすることができる。 In the discrimination apparatus according to aspect 12 of the present invention, in the aspect 4, the tone value of the red component is the a * value in the Lab color space. According to the above configuration, by using the a * value of the Lab color space as the tone value of the predetermined color component, it is possible to further improve the accuracy of the double identification.
　本発明の態様１３に係る情報処理装置は、上記態様１から１２までの何れかに係る鑑別装置を備えている。上記構成によれば、上記態様１と同様の効果を得ることができる。 The information processing apparatus according to the thirteenth aspect of the present invention includes the discrimination apparatus according to any of the first to twelfth aspects. According to the above configuration, it is possible to obtain the same effect as that of the first aspect.
　本発明の態様１４に係る教師データは、八重咲き種子を鑑別する前の複数のストックの種子のそれぞれについて、当該種子の画像データから上記種子の特徴を示す特徴情報を少なくとも取得する情報取得部を備え、上記複数のストックの種子について八重咲き種子を鑑別することを機械学習する鑑別装置に適用される、学習モデルを構築ないし更新するための教師データであって、上記複数のストックの種子の特徴情報を、上記鑑別装置の上記情報取得部が少なくとも取得する処理に用いられる、上記複数のストックの種子の画像データと、上記画像データに含まれる種子の成育後に咲く花の形態を示す形態データと、を含む構造を有している。上記構成によれば、教師データを用いて鑑別装置に学習させることで、鑑別装置の鑑別精度を簡易かつ飛躍的に向上させることができる。 The teacher data according to aspect 14 of the present invention includes an information acquisition unit that acquires at least the characteristic information indicating the characteristics of the seed from the image data of the seed for each of the plurality of stock seeds before the double-flowered seeds are distinguished. , Is applied to a discrimination device that machine-learns to discriminate double-flowered seeds for the seeds of the plurality of stocks, and is teacher data for building or updating a learning model, which is characteristic information of the seeds of the plurality of stocks. , Image information of the seeds of the plurality of stocks, which is used in the process of obtaining at least the information acquisition unit of the discrimination device, and morphological data indicating the morphology of flowers blooming after the growth of seeds included in the image data, It has a structure including. According to the above configuration, by making the discrimination device learn using the teacher data, the discrimination accuracy of the discrimination device can be easily and dramatically improved.
　本発明の態様１５に係る鑑別方法は、八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別方法であって、上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得ステップと、上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定ステップと、を含んでいる。上記方法によれば、上記態様１と同様の効果を得ることができる。 The discrimination method according to aspect 15 of the present invention is a discrimination method for discriminating the double-flowered seeds from the seeds of the plurality of stocks before discriminating the double-flowered seeds, wherein the light of reflected light from the seeds of the plurality of stocks is used. From the information, for each of the seeds of the plurality of stock, an information acquisition step of obtaining at least color tone information indicating the color tone value of a predetermined color component included in the color of the seed, and the predetermined of the seeds of the plurality of stocks. Color tone values of the color components are ranked in descending order, and for each of the seeds of the plurality of stocks, at least the order of the tone values of the predetermined color components is included in the predetermined first range. By determining whether or not the particular stock seed is the double-flowered seed. According to the above method, it is possible to obtain the same effect as that of the first aspect.
　本発明の各態様に係る鑑別装置は、コンピュータによって実現してもよく、この場合には、コンピュータを上記鑑別装置が備える各部（ソフトウェア要素）として動作させることにより上記鑑別装置をコンピュータにて実現させる鑑別装置のプログラム、およびそれを記録したコンピュータ読み取り可能な記録媒体も、本発明の範疇に入る。 The discrimination apparatus according to each aspect of the present invention may be realized by a computer, and in this case, the discrimination apparatus is realized by the computer by operating the computer as each unit (software element) included in the discrimination apparatus. The program of the identification device and the computer-readable recording medium recording the program are also included in the scope of the present invention.
　〔付記事項〕
　本発明は上述した各実施形態に限定されるものではなく、請求項に示した範囲で種々の変更が可能であり、異なる実施形態にそれぞれ開示された技術的手段を適宜組み合わせて得られる実施形態についても本発明の技術的範囲に含まれる。さらに、各実施形態にそれぞれ開示された技術的手段を組み合わせることにより、新しい技術的特徴を形成することができる。 [Appendix]
The present invention is not limited to the embodiments described above, and various modifications are possible within the scope shown in the claims, and embodiments obtained by appropriately combining technical means disclosed in different embodiments. Is also included in the technical scope of the present invention. Furthermore, new technical features can be formed by combining the technical means disclosed in each embodiment.
　１０　鑑別装置
　１１　画像解析部（情報取得部）
　１１ａ　データ解析部（情報取得部）
　１２　決定部
　１００、２００、３００　情報処理装置
　５００　撮像装置
　６００　カラーセンサ
　ａ＊　種子の色調値（赤色成分の色調値）
　ｂ＊　種子の色調値（黄色成分の色調値）
　Ｂ　種子の色調値（青色成分の色調値）
　ｒ　種子の円形度（丸みの程度）
　ｓ　種子の面積
　Ｃ＊　種子の彩度 10 Identification device 11 Image analysis unit (information acquisition unit)
11a Data analysis unit (information acquisition unit)
12 Determining unit 100, 200, 300 Information processing device 500 Imaging device 600 Color sensor a * Seed color tone value (red component color tone value)
b * Seed tone value (yellow component tone value)
B Seed color value (blue component color value)
r Roundness of seed (roundness)
s Seed area C * Seed saturation

Claims (16)

  1. 　八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別装置であって、
    　上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得部と、
    　上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、
    　上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定部と、を備える、鑑別装置。     A discrimination device for discriminating the double-flowered seeds from a plurality of stock seeds before discriminating the double-flowered seeds,
    From the light information of the reflected light from the seeds of the plurality of stocks, for each of the seeds of the plurality of stocks, an information acquisition unit that acquires at least color tone information indicating a color tone value of a predetermined color component included in the color of the seeds. When,
    Tone value of the predetermined color component of the seeds of the plurality of stock, each is ranked from the highest value,
    For each of the seeds of the plurality of stocks, by determining whether or not the order of the tone value of the predetermined color component is included in the predetermined first range, the seeds of the specific stock are the double-flowered seeds. And a determination unit that determines whether or not it is a discrimination device.
  2. 　上記光情報が、撮像装置によって取得される画像情報である、請求項１に記載の鑑別装置。 The discrimination device according to claim 1, wherein the optical information is image information acquired by the imaging device.
  3. 　上記光情報が、カラーセンサによって検出される検出情報である、請求項１に記載の鑑別装置。 The discrimination device according to claim 1, wherein the light information is detection information detected by a color sensor.
  4. 　上記所定の色成分の色調値は、赤色成分の色調値であり、
    　上記色調情報は、上記赤色成分の色調値を示す赤色色調情報である、請求項１から３までの何れか１項に記載の鑑別装置。     The tone value of the predetermined color component is the tone value of the red component,
    The discrimination device according to any one of claims 1 to 3, wherein the color tone information is red color tone information indicating a color tone value of the red component.
  5. 　上記決定部は、上記ストックの品種に応じて、上記第１範囲を変更することができる、請求項１から４までの何れか１項に記載の鑑別装置。 The identification device according to any one of claims 1 to 4, wherein the determination unit can change the first range according to the stock type.
  6. 　上記決定部は、特定の上記ストックの種子に係る上記赤色成分の色調値が、上記第１範囲としての、すべてのストックの種子に係る上記赤色成分の色調値のうちの下位５０％の範囲に含まれているか否かを判定する、請求項４に記載の鑑別装置。 The determination unit is configured such that the tone value of the red component related to the seed of the specific stock is within the lower 50% range of the tone value of the red component related to the seeds of all the stocks as the first range. The discrimination apparatus according to claim 4, which determines whether or not it is included.
  7. 　上記情報取得部は、さらに上記種子の面積を示す面積情報を取得し、
    　上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記面積が所定の第２範囲に含まれているか否かを判定する、請求項２に記載の鑑別装置。     The information acquisition unit further acquires area information indicating the area of the seed,
    The discrimination device according to claim 2, wherein the determination unit further determines whether or not the area is included in a predetermined second range for each of the plurality of stock seeds.
  8. 　上記情報取得部は、さらに上記種子の断面形状の丸みの程度を示す丸み情報を取得し、
    　上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記丸みの程度が所定の第３範囲に含まれているか否かを判定する、請求項２に記載の鑑別装置。     The information acquisition unit further acquires roundness information indicating the degree of roundness of the cross-sectional shape of the seed,
    The discrimination device according to claim 2, wherein the determination unit further determines whether or not the degree of roundness is included in a predetermined third range for each of the seeds of the plurality of stocks.
  9. 　上記情報取得部は、さらに上記種子の色に含まれる黄色成分の色調値を表す黄色色調値情報を取得し、
    　上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記黄色成分の色調値が、所定の第４範囲に含まれているか否かを判定する、請求項１から８までの何れか１項に記載の鑑別装置。     The information acquisition unit further acquires yellow color tone value information representing the color tone value of the yellow component contained in the seed color,
    The determination unit further determines whether or not the color tone value of the yellow component is included in a predetermined fourth range for each of the plurality of stock seeds. The identification device according to item 1.
  10. 　上記情報取得部は、さらに上記種子の色に含まれる青色成分の色調値を表す青色色調値情報を取得し、
    　上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記青色成分の色調値が、所定の第５範囲に含まれているか否かを判定する、請求項１から９までの何れか１項に記載の鑑別装置。     The information acquisition unit further acquires blue tone value information representing the tone value of the blue component contained in the seed color,
    The determination unit further determines, for each of the seeds of the plurality of stocks, whether the color tone value of the blue component is included in a predetermined fifth range, any one of claims 1 to 9. The identification device according to item 1.
  11. 　上記情報取得部は、さらに上記種子の彩度を示す彩度情報を取得し、
    　上記決定部は、上記複数のストックの種子のそれぞれについて、さらに、上記彩度が所定の第６範囲に含まれているか否かを判定する、請求項１から１０までの何れか１項に記載の鑑別装置。     The information acquisition unit further acquires saturation information indicating the saturation of the seed,
    The determination unit further determines whether or not the saturation is included in a predetermined sixth range for each of the seeds of the plurality of stocks. Identification device.
  12. 　上記赤色成分の色調値が、Ｌａｂ色空間におけるａ＊値である、請求項４に記載の鑑別装置。 The discrimination device according to claim 4, wherein the color tone value of the red component is an a * value in the Lab color space.
  13. 　請求項１から１２の何れか１項に記載の鑑別装置を備える、情報処理装置。 An information processing device comprising the discrimination device according to any one of claims 1 to 12.
  14. 　請求項１に記載の鑑別装置としてコンピュータを機能させるためのプログラムであって、上記情報取得部および上記決定部としてコンピュータを機能させるためのプログラム。 A program for causing a computer to function as the identification device according to claim 1, and causing the computer to function as the information acquisition unit and the determination unit.
  15. 　八重咲き種子を鑑別する前の複数のストックの種子のそれぞれについて、当該種子の画像データから上記種子の特徴を示す特徴情報を少なくとも取得する情報取得部を備え、上記複数のストックの種子について八重咲き種子を鑑別することを機械学習する鑑別装置に適用される、学習モデルを構築ないし更新するための教師データであって、
    　上記複数のストックの種子の特徴情報を、上記鑑別装置の上記情報取得部が少なくとも取得する処理に用いられる、上記複数のストックの種子の画像データと、
    　上記画像データに含まれる種子の成育後に咲く花の形態を示す形態データと、を含む構造を有する、教師データ。     For each of the plurality of stock seeds before distinguishing the double-flowered seeds, an information acquisition unit for acquiring at least characteristic information indicating the characteristics of the seeds from the image data of the seeds is provided, and the double-flowered seeds for the plurality of stock seeds are Teacher data for constructing or updating a learning model, which is applied to a discrimination device for machine learning to discriminate,
    Characteristic information of the seeds of the plurality of stocks, used in the process of obtaining at least the information acquisition unit of the identification device, image data of the seeds of the plurality of stocks,
    Teacher data having a structure including: morphological data indicating the morphology of flowers that bloom after the seeds are grown, which is included in the image data.
  16. 　八重咲き種子を鑑別する前の複数のストックの種子から上記八重咲き種子を鑑別するための鑑別方法であって、
    　上記複数のストックの種子からの反射光の光情報から、上記複数のストックの種子のそれぞれについて、当該種子の色に含まれる所定の色成分の色調値を示す色調情報を少なくとも取得する情報取得ステップと、
    　上記複数のストックの種子の上記所定の色成分の色調値を、それぞれ値が大きい順から順位付けし、
    　上記複数のストックの種子のそれぞれについて、少なくとも上記所定の色成分の色調値の順位が所定の第１範囲に含まれているか否かを判定することにより、特定の上記ストックの種子が上記八重咲き種子か否かを決定する決定ステップと、を含む、鑑別方法。     A discrimination method for discriminating the double-flowered seeds from a plurality of stock seeds before discriminating the double-flowered seeds,
    From the light information of the reflected light from the seeds of the plurality of stocks, for each of the seeds of the plurality of stocks, an information acquisition step of acquiring at least color tone information indicating a color tone value of a predetermined color component included in the color of the seeds. When,
    Tone value of the predetermined color component of the seeds of the plurality of stock, each is ranked from the highest value,
    For each of the seeds of the plurality of stocks, by determining whether or not the order of the tone value of the predetermined color component is included in the predetermined first range, the seeds of the specific stock are the double-flowered seeds. And a determination step of determining whether or not the identification method.
PCT/JP2019/039960 2018-10-12 2019-10-10 Distinguishing device, information processing device, program, teacher data, and distinguishing method WO2020075793A1 (en)

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