CN117378318B - Coconut seed germination detection and sorting method and system based on X-rays - Google Patents
Coconut seed germination detection and sorting method and system based on X-rays Download PDFInfo
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- 235000013162 Cocos nucifera Nutrition 0.000 title claims abstract description 120
- 244000060011 Cocos nucifera Species 0.000 title claims abstract description 120
- 238000001514 detection method Methods 0.000 title claims abstract description 61
- 238000000034 method Methods 0.000 title claims abstract description 34
- 230000007226 seed germination Effects 0.000 title claims abstract description 23
- 238000003860 storage Methods 0.000 claims abstract description 57
- 238000003384 imaging method Methods 0.000 claims abstract description 26
- 230000035784 germination Effects 0.000 claims abstract description 18
- 230000008569 process Effects 0.000 claims abstract description 6
- 238000013519 translation Methods 0.000 claims abstract description 4
- 235000020415 coconut juice Nutrition 0.000 claims description 20
- 230000011218 segmentation Effects 0.000 claims description 20
- 238000012549 training Methods 0.000 claims description 20
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- 238000002372 labelling Methods 0.000 claims description 13
- 238000013527 convolutional neural network Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 9
- 238000010606 normalization Methods 0.000 claims description 8
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- 238000011176 pooling Methods 0.000 claims description 4
- 238000005259 measurement Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 abstract description 4
- 238000013135 deep learning Methods 0.000 abstract description 2
- 230000003213 activating effect Effects 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 3
- 238000012271 agricultural production Methods 0.000 description 2
- 230000002708 enhancing effect Effects 0.000 description 2
- 235000013372 meat Nutrition 0.000 description 2
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- 230000004048 modification Effects 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
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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
- A01C1/02—Germinating apparatus; Determining germination capacity of seeds or the like
- A01C1/025—Testing seeds for determining their viability or germination capacity
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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
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/02—Measures preceding sorting, e.g. arranging articles in a stream orientating
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/34—Sorting according to other particular properties
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B07—SEPARATING SOLIDS FROM SOLIDS; SORTING
- B07C—POSTAL SORTING; SORTING INDIVIDUAL ARTICLES, OR BULK MATERIAL FIT TO BE SORTED PIECE-MEAL, e.g. BY PICKING
- B07C5/00—Sorting according to a characteristic or feature of the articles or material being sorted, e.g. by control effected by devices which detect or measure such characteristic or feature; Sorting by manually actuated devices, e.g. switches
- B07C5/36—Sorting apparatus characterised by the means used for distribution
- B07C5/361—Processing or control devices therefor, e.g. escort memory
- B07C5/362—Separating or distributor mechanisms
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
- G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
- G01N23/04—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and forming images of the material
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P60/00—Technologies relating to agriculture, livestock or agroalimentary industries
- Y02P60/20—Reduction of greenhouse gas [GHG] emissions in agriculture, e.g. CO2
- Y02P60/21—Dinitrogen oxide [N2O], e.g. using aquaponics, hydroponics or efficiency measures
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Abstract
The invention relates to the technical field of agricultural products, in particular to a coconut seed germination detection and sorting method and system based on X-rays. Comprising the following steps: the device comprises a region to be detected, a conveyor belt, an objective table, a first mechanical arm, an X-ray imaging device, a second mechanical arm, a sample storage region and a computer system; the object stage is arranged on the conveyor belt and used for placing a sample to be tested; the conveyor belt is used for bearing a sample to be tested and receiving an instruction of the computer system to realize translation; the X-ray imaging device comprises an X-ray source and an X-ray detection plate which are respectively arranged at two sides of the conveyor belt; the first mechanical arm is used for grabbing a coconut seed sample to be detected and then is placed on the objective table; the second mechanical arm is used for receiving an operation instruction sent by the computer system and grabbing and placing the sample to be detected in the sample storage area. The advantages are that: and acquiring an imaging image of the detection sample after automatic imaging, acquiring the internal structure of the coconut fruit based on computer system deep learning, and automatically identifying key characteristics in the germination process.
Description
Technical Field
The invention relates to the technical field of agricultural products, in particular to a coconut seed germination detection and sorting method and system based on X-rays.
Background
In current agricultural production, ensuring high quality and high yield of crops is important. And as a key link in agricultural production links, seed quality assessment is very important. Coconut is one of important crops in tropical areas of China, the economic value brought by the coconut is extremely high every year, however, the seed of the coconut is low in germination rate and long in period, the nuclear fruit structure enables the bad seed to be difficult to discover in early stage, a large amount of storage and manpower resources are wasted, and the coconut breeding research and the development of related industries are seriously influenced.
Currently, more traditional methods are generally used for evaluating the quality of seeds of crops, and destructive detection and monitoring methods are generally required, including cutting the seeds or soaking the seeds to observe the internal germination. This method is not only time consuming and laborious, but may also be detrimental to the integrity and germination capacity of the seeds. Meanwhile, coconut is hard in shell, so that the monitoring of seed germination of coconut is difficult to achieve by using a traditional disruption method. Therefore, it is highly desirable to provide a nondestructive detection method for germination of coconut seeds, so as to realize high-throughput accurate monitoring of germination of coconut seeds.
Disclosure of Invention
The invention provides a coconut seed germination detection and sorting method and system based on X-rays for solving the problems.
The first object of the invention is to provide a coconut seed germination detection and sorting system based on X-ray, comprising: the device comprises a region to be detected, a conveyor belt, an objective table, a first mechanical arm, an X-ray imaging device, a second mechanical arm, a sample storage region and a computer system;
the object stage is arranged on the conveyor belt and is used for placing a sample to be tested; the conveyor belt is used for bearing a sample to be tested and receiving an instruction of the computer system so as to realize a translation function;
the X-ray imaging device comprises an X-ray source and an X-ray detection plate, wherein the X-ray source and the X-ray detection plate are respectively arranged on two sides of the conveyor belt, the X-ray source is used for realizing automatic X-ray imaging of coconut seeds, the X-ray detection plate is used for collecting transmission ray information during X-ray scanning and transmitting the transmission ray information back to the computer system, and the computer system is used for carrying out image processing, classifying, distinguishing, displaying and storing measurement results;
the first mechanical arm is used for grabbing a coconut seed sample to be detected, then is placed on the object stage, and the conveying belt conveys the coconut seed sample to be detected forwards to a position between the X-ray source and the X-ray detection plate;
the sample storage area comprises a germinated sample storage box, an ungerminated sample storage box and a damaged sample storage box;
the second mechanical arm is used for receiving an operation instruction sent by the computer system, grabbing and placing the detected sample into the germinated sample storage box, the ungerminated sample storage box or the damaged sample storage box;
the computer system is connected with four PLC controllers through serial ports; the first PLC is connected with a servo motor and a driver of the conveyor belt and used for controlling the starting and stopping time and the stepping distance of the conveyor belt; the other three PLC controllers are respectively connected with the first mechanical arm, the X-ray source and the second mechanical arm and are used for enabling the first mechanical arm, the X-ray source and the second mechanical arm to respectively execute instructions sent by the computer system.
The second object of the invention is to provide a coconut seed germination detection and sorting method based on X-ray, which adopts a coconut seed germination detection and sorting system based on X-ray, and specifically comprises the following steps:
s1, preheating and calibrating an X-ray source;
s2, placing a coconut seed sample to be detected in a region to be detected;
s3, sequentially grabbing a coconut seed sample to be detected by the first mechanical arm, placing the coconut seed sample on an objective table of the conveyor belt, and then withdrawing the first mechanical arm;
s4, the coconut seed sample to be detected is transmitted between an X-ray source and an X-ray detection plate through a conveyor belt, the X-ray source is started for imaging, and data are collected through the X-ray detection plate and transmitted back to a computer system;
s5, performing image processing and classification discrimination through a computer system, and displaying and storing processing results;
and S6, giving categories to the coconut seed samples to be detected according to the processing results, sending control signals of the corresponding categories to the second mechanical arm, and placing the detected coconut seed samples to the corresponding areas of the sample storage areas by the second mechanical arm to finish classification of the coconut seeds.
Preferably, step S5 comprises the following sub-steps:
s501, obtaining a coconut seed projection surface array image with a single view angle through a reconstruction algorithm;
s502, performing differential labeling on each tissue through coconut seed projection surface array images to obtain a labeling file;
s503, collecting paired coconut seed projection surface array images and annotation files as training sets, and training a segmentation model based on a U-type convolutional neural network; the segmentation model is used for learning image features of the internal structure, the position and the form of germination points of coconut seeds;
s504, preprocessing the images of the training set under the condition that the input shape of the segmentation model is met;
s505, inputting a preprocessed U-type convolutional neural network, performing iterative training through random gradient descent, and performing similarity comparison on an output result and a coconut seed projection surface array image from the average interaction ratio and the Dice coefficient until the output result is higher than a fixed threshold value, and completing the training process;
s506, predicting the input image by utilizing the trained network parameters, and counting the pixel number of each region in the prediction result; when the total number of pixels in a certain area is more than 100 x 100, the tissue is considered to exist; marking an input sample according to an output result, recording according to a three-bit 0/1 character string method, wherein 0 corresponds to the absence of the organization and 1 corresponds to the presence of the organization; the first position of the character string corresponds to the rhizome, the second position corresponds to the aspirator, and the last position corresponds to the coconut water; the character strings are classified and then provided with different differentiation labels, and the labels comprise germinated, ungerminated and damaged; wherein the germinated tag codes correspond to 110, 101, and 111, the ungerminated tag code corresponds to 001, and the damaged tag code corresponds to 000.
Preferably, in step S6, the location information of the germinated sample storage boxes, the ungerminated sample storage boxes and the damaged sample storage boxes are coded in the computer system as 01, 10 and 11, respectively; after the computer system receives the tag code in step S506, control signals of corresponding categories are sent to the second robot according to the corresponding rules of 110-01, 101-01, 111-01, 001-10 and 000-11.
Preferably, the coconut seed projection area array image is 8-bit single channel image data, with a size of 2512 x 3008 pixels.
Preferably, the labeling file in step S502 is a single-channel 8-bit gray map, wherein the gray value of the rhizome region is 1, the gray value of the aspirator region is 2, the gray value of the coconut water region is 3, and the gray values of the rest regions are all 0; the rhizome tissue is labeled as label_1, the aspirator tissue is labeled as label_2, and the coconut water area is labeled as label_3.
Preferably, the segmentation model in step S503 includes four downsampling layers, four upsampling layers, and a softmax layer for classification; each downsampling layer comprises a full convolution relu activation layer, a batch normalization layer and a maximum pooling layer, and each upsampling layer comprises a full convolution relu activation layer, a deconvolution layer and a feature fusion layer.
Preferably, the preprocessing of step S504 includes image size normalization, feature enhancement, and image rotation, and is calculated from the receptive field of the segmentation model.
Preferably, the fixed threshold of step S505 is set to 0.9.
Compared with the prior art, the invention has the following beneficial effects:
(1) The X-ray imaging device is used for continuously and rapidly imaging the coconut seeds;
(2) The X-ray source and the X-ray detector are kept still, the movement of a detection sample is realized by utilizing a computer system and a PLC controller to control a conveyor belt, the movement distance is easy to accurately control, and the system is simple to construct;
(3) Based on an X-ray imaging technology and an image processing technology, obtaining a high-quality coconut seed internal structure image, and providing a necessary premise for coconut germination classification;
(4) Based on the coconut seed imaging result, classifying samples by using a U-shaped convolutional neural network and a classification module, so as to realize accurate detection of coconut seed germination conditions;
(5) Converting the detection result into an operation instruction, transmitting the operation instruction to the second mechanical arm, and controlling the second mechanical arm to move the detected sample to a storage area corresponding to each category; and finally, the extracted data is stored as a spreadsheet, so that a user can index, manage and analyze the measured data conveniently.
Drawings
Fig. 1 is a schematic diagram of a coconut seed germination detection and sorting system based on X-rays according to an embodiment of the present invention.
Fig. 2 is a flowchart of a coconut seed germination detection and sorting method based on X-rays according to an embodiment of the invention.
FIG. 3 is a flow chart of image processing and classification of coconut seed projection surface arrays according to an embodiment of the present invention.
Reference numerals:
1. a region to be detected; 2. a conveyor belt; 3. an objective table; 4. a first mechanical arm; 5. an X-ray source; 6. an X-ray detection plate; 7. a second mechanical arm; 8. a germinated sample storage box; 9. an ungerminated sample storage bin; 10. a damaged sample storage bin; 11. a computer system.
Detailed Description
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In the following description, like modules are denoted by like reference numerals. In the case of the same reference numerals, their names and functions are also the same. Therefore, a detailed description thereof will not be repeated.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting the invention.
Referring to fig. 1, there is provided an X-ray based coconut seed germination detection, sorting system comprising: the detection device comprises a region to be detected 1, a conveyor belt 2, an object stage 3, a first mechanical arm 4, an X-ray imaging device, a second mechanical arm 7, a sample storage region and a computer system 11;
the object stage 3 is arranged on the conveyor belt 2 and is used for placing a sample to be tested; the conveyor belt 2 is used for bearing a sample to be tested and receiving an instruction of the computer system 11 so as to realize a translation function;
the X-ray imaging device comprises an X-ray source 5 and an X-ray detection plate 6, wherein the X-ray source 5 and the X-ray detection plate 6 are respectively arranged on two sides of the conveyor belt 2, the X-ray source 5 is used for realizing automatic X-ray imaging of coconut seeds, the X-ray detection plate 6 is used for collecting transmission ray information during X-ray scanning and transmitting the transmission ray information back to the computer system 11, and the computer system 11 is used for carrying out image processing, classifying, distinguishing, displaying and storing measurement results;
the coconut seed sample to be detected is placed in a region to be detected 1, after the first mechanical arm 4 grabs the coconut seed sample to be detected, the coconut seed sample to be detected is placed on the object stage 3 on the conveyor belt 2, and the conveyor belt 2 forwards conveys the coconut seed sample to be detected to a position between the X-ray source 5 and the X-ray detection plate 6;
the sample storage area comprises a germinated sample storage box 8, an ungerminated sample storage box 9 and a damaged sample storage box 10, and after receiving an operation instruction sent by the computer system 11, the second mechanical arm 7 grabs and places the detected sample into the germinated sample storage box 8, the ungerminated sample storage box 9 or the damaged sample storage box 10;
the computer system 11 is connected with four PLC controllers through serial ports; the first PLC is connected with a servo motor and a driver of the conveyor belt 2 and is used for controlling the starting and stopping time and the stepping distance of the conveyor belt 2; the other three PLC controllers are respectively connected with the first mechanical arm 4, the X-ray source 5 and the second mechanical arm 7, and the first mechanical arm 4, the X-ray source 5 and the second mechanical arm 7 respectively execute instructions sent by the computer system 11.
The invention provides an X-ray-based coconut seed germination detection and sorting method, which adopts the detection and sorting system to detect and sort, and specifically comprises the following steps:
s1, preheating and calibrating the X-ray source 5.
S2, placing the coconut seed sample to be detected in the region 1 to be detected.
S3, the first mechanical arm 4 sequentially grabs coconut seed samples to be detected and places the coconut seed samples on the object stage 3 of the conveyor belt 2, and then the first mechanical arm 4 withdraws.
S4, the coconut seed sample to be detected is transmitted between an X-ray source 5 and an X-ray detection plate 6 through a conveyor belt 2, the X-ray source 5 is started for imaging, and data are collected through the X-ray detection plate 6 and transmitted back to a computer system 11;
specifically, the coconut seed sample to be detected is conveyed between an X-ray source 5 and an X-ray detection plate 6 through a conveyor belt 2, the coconut seed sample to be detected, the X-ray source 5 and the X-ray detection plate 6 are kept stationary, a scanning mode of the X-ray source 5 is fixed, the coconut seed sample to be detected is scanned at an angle parallel to the sample, the parameters of the X-ray source 5 are 100 kilovolts and 50 microamps, and the single scanning duration is 1 second; the X-ray detection board 6 returns the received data in real time to the computer system 11.
S5, performing image processing and classification discrimination through a computer system, and displaying and storing processing results;
specifically, the method comprises the following substeps:
s501, obtaining a coconut seed projection surface array image with a single view angle through a reconstruction algorithm; the coconut seed projection area array image is 8-bit single channel image data, with a size of 2512 x 3008 pixels.
S502, performing differential labeling on each tissue through coconut seed projection surface array images to obtain a labeling file; the marked file is an 8-bit gray map with a single channel, wherein the gray value of a rhizome region is 1, the gray value of a sucker region is 2, the gray value of a coconut water region is 3, and the gray values of the other regions are all 0; the rhizome tissue is labeled as label_1, the aspirator tissue is labeled as label_2, and the coconut water area is labeled as label_3.
S503, collecting paired coconut seed projection surface array images and annotation files as training sets, and training a segmentation model based on a U-type convolutional neural network; the segmentation model comprises four downsampling layers, four upsampling layers and a softmax layer for classification; each downsampling layer comprises a full convolution relu activating layer, a batch normalization layer and a maximum pooling layer, and each upsampling layer comprises a full convolution relu activating layer, a deconvolution layer and a feature fusion layer; the segmentation model is used for learning image features such as the internal structure of the seeds, the positions and the forms of germination points and the like.
S504, preprocessing the images of the training set under the condition that the input shape of the segmentation model is met; preprocessing comprises image size normalization (1/2 downsampling), feature enhancement, image rotation and the like, and is calculated according to the receptive field of the segmentation model.
S505, inputting a preprocessed U-type convolutional neural network, performing iterative training through random gradient descent, and performing similarity comparison on an output result and a coconut seed projection surface array image from an average interaction ratio and a Dice coefficient until the output result is higher than a fixed threshold value, wherein the training process is considered to be completed; the threshold is typically set to 0.9.
S506, predicting the input image by utilizing the trained network parameters, and counting the pixel number of each region in the prediction result; setting that the tissue is considered to exist when the total number of pixels in a certain area is more than 100 x 100; marking an input sample according to an output result, recording according to a three-bit 0/1 character string method, wherein 0 corresponds to the absence of the organization and 1 corresponds to the presence of the organization; the first position of the character string corresponds to the rhizome, the second position corresponds to the aspirator, and the last position corresponds to the coconut water; the character strings are classified and then are endowed with different differentiation labels, and the labels comprise germinated, ungerminated and damaged; wherein the germinated tag codes correspond to 110, 101 and 111, the ungerminated tag code corresponds to 001, the damaged tag code corresponds to 000, and the rest of the codes are objectively absent;
principle of: the germination of the coconut seeds is judged mainly by the existence relation of the rhizome, the sucker and the coconut water; when the rootstalk and the aspirator appear, the coconut is indicated to start to germinate and belongs to the germinated state; when the rhizome and the aspirator are not present and coconut water is present, the coconut has germination potential, and the coconut belongs to an ungerminated state; when the rhizome, the aspirator and the coconut water are all absent or the coconut meat is missing in a large area, the coconut seeds are not provided with germination conditions, and the coconut seeds are in a damaged state; the rhizome, the aspirator and the coconut water can be obviously identified from the sample projection map file obtained by analyzing imaging data, enhancing the image and correcting the image.
S6, giving categories to the coconut seed samples to be detected according to the processing results, and sending control signals of the corresponding categories to the second mechanical arm 7, wherein the second mechanical arm 7 places the coconut seed samples to be detected in the corresponding areas of the sample storage areas;
the corresponding areas of the sample storage area comprise germinated sample storage boxes 8, ungerminated sample storage boxes 9 and damaged sample storage boxes 10;
specifically, the location information of germinated sample storage boxes 8, ungerminated sample storage boxes 9, and damaged sample storage boxes 10 are encoded in computer system 11 as 01, 10, and 11, respectively; when the computer system 11 receives the classification labels of the samples, the operation instructions are transmitted to the second mechanical arm 7 according to the corresponding rules of 110-01, 101-01, 111-01, 001-10 and 000-11. And controlling the conveyor belt to rotate so as to convey the object placing table and the detected sample to the completion area from left to right, and controlling the mechanical arm to grasp the sample and place the sample in the corresponding area for storage.
Example 1
A coconut seed germination detection and sorting method based on X-ray adopts a coconut seed germination detection and sorting system based on X-ray, wherein the distance between an X-ray source 5 and an X-ray detection plate 6 is 75cm, and when a sample is placed, the distance between the coconut seed sample to be detected and the X-ray source 5 and the X-ray detection plate 6 is 50cm and 25cm respectively; fig. 2 shows the flow of the method, which specifically comprises the following steps:
s1, preheating and calibrating the X-ray source 5.
S2, placing the coconut seed sample to be detected in the region 1 to be detected.
S3, the first mechanical arm 4 sequentially grabs coconut seed samples to be detected and places the coconut seed samples on the object stage 3 of the conveyor belt 2, and then the first mechanical arm 4 withdraws.
S4, the coconut seed sample to be detected is transmitted between an X-ray source 5 and an X-ray detection plate 6 through a conveyor belt 2, the X-ray source 5 is started for imaging, and data are collected through the X-ray detection plate 6 and transmitted back to a computer system 11;
specifically, the coconut seed sample to be detected is conveyed between an X-ray source 5 and an X-ray detection plate 6 through a conveyor belt 2, the coconut seed sample to be detected, the X-ray source 5 and the X-ray detection plate 6 are kept stationary, a scanning mode of the X-ray source 5 is fixed, the coconut seed sample to be detected is scanned at an angle parallel to the sample, the parameters of the X-ray source 5 are 100 kilovolts and 50 microamps, and the single scanning duration is 1 second; the X-ray detection board 6 returns the received data in real time to the computer system 11.
S5, performing image processing and classification discrimination through a computer system, and displaying and storing processing results; specifically, the method comprises the following substeps:
s501, obtaining a coconut seed projection surface array image with a single view angle through a reconstruction algorithm; the coconut seed projection area array image is 8-bit single channel image data, with a size of 2512 x 3008 pixels.
S502, performing differential labeling on each tissue through coconut seed projection surface array images to obtain a labeling file; the marked file is an 8-bit gray map with a single channel, wherein the gray value of a rhizome region is 1, the gray value of a sucker region is 2, the gray value of a coconut water region is 3, and the gray values of the other regions are all 0; the rhizome tissue is labeled as label_1, the aspirator tissue is labeled as label_2, and the coconut water area is labeled as label_3.
S503, collecting paired coconut seed projection surface array images and annotation files as training sets, and training a segmentation model based on a U-type convolutional neural network; the segmentation model comprises four downsampling layers, four upsampling layers and a softmax layer for classification; each downsampling layer comprises a full convolution relu activating layer, a batch normalization layer and a maximum pooling layer, and each upsampling layer comprises a full convolution relu activating layer, a deconvolution layer and a feature fusion layer; the segmentation model is used for learning image features such as the internal structure of the seeds, the positions and the forms of germination points and the like.
S504, preprocessing the images of the training set under the condition that the input shape of the segmentation model is met; preprocessing comprises image size normalization (1/2 downsampling), feature enhancement, image rotation and the like, and is calculated according to the receptive field of the segmentation model.
S505, inputting a preprocessed U-type convolutional neural network, performing iterative training through random gradient descent, and performing similarity comparison on an output result and a coconut seed projection surface array image from an average interaction ratio and a Dice coefficient until the output result is higher than a fixed threshold value, wherein the training process is considered to be completed; the fixed threshold is typically set to 0.9.
S506, predicting the input image by utilizing the trained network parameters, and counting the pixel number of each region in the prediction result; in the embodiment, the distances between the coconut seed sample to be detected and the X-ray source 5 and the X-ray detection plate 6 are respectively 50cm and 25cm, and the single pixel size is about 0.067mm; setting that the tissue is considered to exist when the total number of pixels in a certain area is more than 100 x 100; marking an input sample according to an output result, recording according to a three-bit 0/1 character string method, wherein 0 corresponds to the absence of the organization and 1 corresponds to the presence of the organization; the first position of the character string corresponds to the rhizome, the second position corresponds to the aspirator, and the last position corresponds to the coconut water; the character strings are classified and then are endowed with different differentiation labels, and the labels comprise germinated, ungerminated and damaged; wherein the germinated tag codes correspond to 110, 101 and 111, the ungerminated tag code corresponds to 001, the damaged tag code corresponds to 000, and the rest of the codes are objectively absent;
principle of: the germination of the coconut seeds is judged mainly by the existence relation of the rhizome, the sucker and the coconut water; when the rootstalk and the aspirator appear, the coconut is indicated to start to germinate and belongs to the germinated state; when the rhizome and the aspirator are not present and coconut water is present, the coconut has germination potential, and the coconut belongs to an ungerminated state; when the rhizome, the aspirator and the coconut water are all absent or the coconut meat is missing in a large area, the coconut seeds are not provided with germination conditions, and the coconut seeds are in a damaged state; the rhizome, the aspirator and the coconut water can be obviously identified from the sample projection map file obtained by analyzing imaging data, enhancing the image and correcting the image.
S6, giving categories to the coconut seed samples to be detected according to the processing results, and sending control signals of the corresponding categories to the second mechanical arm 7, wherein the second mechanical arm 7 places the coconut seed samples to be detected in the corresponding areas of the sample storage areas;
the corresponding areas of the sample storage area comprise germinated sample storage boxes 8, ungerminated sample storage boxes 9 and damaged sample storage boxes 10;
specifically, the location information of germinated sample storage boxes 8, ungerminated sample storage boxes 9, and damaged sample storage boxes 10 are encoded in computer system 11 as 01, 10, and 11, respectively; when the computer system 11 receives the classification labels of the samples, the operation instructions are transmitted to the second mechanical arm 7 according to the corresponding rules of 110-01, 101-01, 111-01, 001-10 and 000-11. And controlling the conveyor belt to rotate so as to convey the object placing table and the detected sample to the completion area from left to right, and controlling the mechanical arm to grasp the sample and place the sample in the corresponding area for storage.
The invention has the advantages that: the automatic imaging work is completed by the X-ray imaging device, imaging images of detection samples can be acquired through the X-ray detection plate, then the internal structure of coconut fruits can be acquired simultaneously based on the deep learning technology of the computer system, key features in the germination process of coconut seeds can be automatically identified based on the internal structure of the coconut fruits, complex preprocessing operation is not needed, the software compatibility is good, the integration with the existing plant phenotype extraction technology (structured light, hyperspectral, micro CT and the like) is easy, and the method can be conveniently applied to seed germination classification of other tropical crops after the software parameters are slightly adjusted.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the technical solutions of the present disclosure are achieved, and are not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (5)
1. The coconut seed germination detection and sorting method based on X-rays is characterized in that a coconut seed germination detection and sorting system is adopted, and the system comprises: the device comprises a region to be detected, a conveyor belt, an objective table, a first mechanical arm, an X-ray imaging device, a second mechanical arm, a sample storage region and a computer system;
the object stage is arranged on the conveyor belt and is used for placing a sample to be tested; the conveyor belt is used for bearing a sample to be tested and receiving an instruction of the computer system so as to realize a translation function;
the X-ray imaging device comprises an X-ray source and an X-ray detection plate, wherein the X-ray source and the X-ray detection plate are respectively arranged on two sides of the conveyor belt, the X-ray source is used for realizing automatic X-ray imaging of coconut seeds, the X-ray detection plate is used for collecting transmission ray information during X-ray scanning and transmitting the transmission ray information back to the computer system, and the computer system is used for carrying out image processing, classifying, distinguishing, displaying and storing measurement results;
the first mechanical arm is used for grabbing a coconut seed sample to be detected, then is placed on the object stage, and the conveying belt conveys the coconut seed sample to be detected forwards to a position between the X-ray source and the X-ray detection plate;
the sample storage area comprises a germinated sample storage box, an ungerminated sample storage box and a damaged sample storage box;
the second mechanical arm is used for receiving an operation instruction sent by the computer system, grabbing and placing the detected sample into the germinated sample storage box, the ungerminated sample storage box or the damaged sample storage box;
the computer system is connected with four PLC controllers through serial ports; the first PLC is connected with a servo motor and a driver of the conveyor belt and used for controlling the starting and stopping time and the stepping distance of the conveyor belt; the other three PLC controllers are respectively connected with the first mechanical arm, the X-ray source and the second mechanical arm and are used for enabling the first mechanical arm, the X-ray source and the second mechanical arm to respectively execute instructions sent by the computer system;
the method specifically comprises the following steps:
s1, preheating and calibrating an X-ray source;
s2, placing a coconut seed sample to be detected in a region to be detected;
s3, sequentially grabbing a coconut seed sample to be detected by the first mechanical arm, placing the coconut seed sample on an objective table of the conveyor belt, and then withdrawing the first mechanical arm;
s4, the coconut seed sample to be detected is transmitted between an X-ray source and an X-ray detection plate through a conveyor belt, the X-ray source is started for imaging, and data are collected through the X-ray detection plate and transmitted back to a computer system;
s5, performing image processing and classification discrimination through a computer system, and displaying and storing processing results; the method comprises the following substeps:
s501, obtaining a coconut seed projection surface array image with a single view angle through a reconstruction algorithm;
s502, performing differential labeling on each tissue through coconut seed projection surface array images to obtain a labeling file; the labeling file is an 8-bit gray map with a single channel, wherein the gray value of a rhizome region is 1, the gray value of a sucker region is 2, the gray value of a coconut water region is 3, and the gray values of the rest regions are all 0; labeling rhizome tissues as label_1, labeling aspirator tissues as label_2, and labeling coconut water areas as label_3;
s503, collecting paired coconut seed projection surface array images and annotation files as training sets, and training a segmentation model based on a U-type convolutional neural network; the segmentation model is used for learning image features of the internal structure, the position and the form of germination points of coconut seeds;
s504, preprocessing the images of the training set under the condition that the input shape of the segmentation model is met;
s505, inputting a preprocessed U-type convolutional neural network, performing iterative training through random gradient descent, and performing similarity comparison on an output result and a coconut seed projection surface array image from the average interaction ratio and the Dice coefficient until the output result is higher than a fixed threshold value, and completing the training process; the fixed threshold is set to 0.9;
s506, predicting the input image by utilizing the trained network parameters, and counting the pixel number of each region in the prediction result; when the total number of pixels in a certain area is more than 100 x 100, the tissue is considered to exist; marking an input sample according to an output result, recording according to a three-bit 0/1 character string method, wherein 0 corresponds to the absence of the organization and 1 corresponds to the presence of the organization; the first position of the character string corresponds to the rhizome, the second position corresponds to the aspirator, and the last position corresponds to the coconut water; the character strings are classified and then provided with different differentiation labels, and the labels comprise germinated, ungerminated and damaged; wherein germinated tag codes correspond to 110, 101 and 111, ungerminated tag codes correspond to 001, and damaged tag codes correspond to 000;
and S6, giving categories to the coconut seed samples to be detected according to the processing results, sending control signals of the corresponding categories to the second mechanical arm, and placing the detected coconut seed samples to the corresponding areas of the sample storage areas by the second mechanical arm to finish classification of the coconut seeds.
2. The X-ray based coconut seed germination detection, sorting method of claim 1, wherein: the position information of the germinated sample storage boxes, the ungerminated sample storage boxes and the damaged sample storage boxes in the step S6 are respectively coded as 01, 10 and 11 in the computer system; after the computer system receives the tag code in step S506, control signals of corresponding categories are sent to the second robot according to the corresponding rules of 110-01, 101-01, 111-01, 001-10 and 000-11.
3. The X-ray based coconut seed germination detection, sorting method of claim 2, wherein: the coconut seed projection surface array image is 8-bit single-channel image data, and the size is 2512 x 3008 pixels.
4. The X-ray based coconut seed germination detection, sorting method of claim 3, wherein: the segmentation model in the step S503 includes four downsampling layers, four upsampling layers, and a softmax layer for classification; each downsampling layer comprises a full convolution relu activation layer, a batch normalization layer and a maximum pooling layer, and each upsampling layer comprises a full convolution relu activation layer, a deconvolution layer and a feature fusion layer.
5. The X-ray based coconut seed germination detection, sorting method of claim 4, wherein: the preprocessing in step S504 includes image size normalization, feature enhancement, and image rotation, and is calculated according to the receptive field of the segmentation model.
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