CN108782145A - A kind of intelligence orchard management system - Google Patents
A kind of intelligence orchard management system Download PDFInfo
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- CN108782145A CN108782145A CN201810539891.6A CN201810539891A CN108782145A CN 108782145 A CN108782145 A CN 108782145A CN 201810539891 A CN201810539891 A CN 201810539891A CN 108782145 A CN108782145 A CN 108782145A
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- 239000002420 orchard Substances 0.000 title claims abstract description 53
- 230000007613 environmental effect Effects 0.000 claims abstract description 15
- 238000012544 monitoring process Methods 0.000 claims abstract description 14
- 230000002262 irrigation Effects 0.000 claims abstract description 9
- 238000003973 irrigation Methods 0.000 claims abstract description 9
- 238000004891 communication Methods 0.000 claims abstract description 4
- 230000011218 segmentation Effects 0.000 claims description 38
- 238000001514 detection method Methods 0.000 claims description 22
- 230000004888 barrier function Effects 0.000 claims description 12
- 235000013399 edible fruits Nutrition 0.000 claims description 6
- 230000004927 fusion Effects 0.000 claims description 6
- 238000000605 extraction Methods 0.000 claims description 5
- 241000196324 Embryophyta Species 0.000 claims 1
- 244000182264 Lucuma nervosa Species 0.000 claims 1
- 230000009286 beneficial effect Effects 0.000 abstract description 3
- 241000220225 Malus Species 0.000 description 48
- 238000000034 method Methods 0.000 description 6
- 240000001923 Thevetia neriifolia Species 0.000 description 2
- 230000003044 adaptive effect Effects 0.000 description 1
- 239000012491 analyte Substances 0.000 description 1
- 235000021016 apples Nutrition 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005286 illumination Methods 0.000 description 1
- 230000001788 irregular Effects 0.000 description 1
- VIKNJXKGJWUCNN-XGXHKTLJSA-N norethisterone Chemical compound O=C1CC[C@@H]2[C@H]3CC[C@](C)([C@](CC4)(O)C#C)[C@@H]4[C@@H]3CCC2=C1 VIKNJXKGJWUCNN-XGXHKTLJSA-N 0.000 description 1
Classifications
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- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01G—HORTICULTURE; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPS OR SEAWEED; FORESTRY; WATERING
- A01G25/00—Watering gardens, fields, sports grounds or the like
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/24—Devices for picking apples or like fruit
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D46/00—Picking of fruits, vegetables, hops, or the like; Devices for shaking trees or shrubs
- A01D46/30—Robotic devices for individually picking crops
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/90—Dynamic range modification of images or parts thereof
- G06T5/92—Dynamic range modification of images or parts thereof based on global image properties
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/11—Region-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/188—Vegetation
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01D—HARVESTING; MOWING
- A01D75/00—Accessories for harvesters or mowers
- A01D75/18—Safety devices for parts of the machines
- A01D75/185—Avoiding collisions with obstacles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
- G06T2207/30188—Vegetation; Agriculture
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- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Environmental Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Multimedia (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Robotics (AREA)
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Abstract
The present invention provides a kind of intelligent orchard management systems, apple is planted in orchard, including environment monitoring sensor, signal transmitting apparatus and managing device, the signal transmitting apparatus establishes the communication connection between environment monitoring sensor and managing device by mobile network, the environment monitoring sensor is used to monitor the environmental parameter in apple orchard, and the environmental parameter is sent to managing device by signal transmitting apparatus, the managing device includes irrigation rig and picker, the irrigation rig is used to be irrigated according to environmental parameter, the picker is for picking transparent apple.Beneficial effects of the present invention are：A kind of intelligent orchard management system is provided, realizes orchard from the intelligent management irrigated to picking.
Description
Technical field
The present invention relates to agricultural management technical fields, and in particular to a kind of intelligence orchard management system.
Background technology
Fruit tree is generally planted in orchard, and a modernization, intelligentized orchard management system how are established, for realizing
From the overall process of orchard monitoring, irrigation to picking, it is of great significance for the agricultural modernization to establish a state.
Yield highest fruit one of of the apple as China accurately and effectively identifies tree crown apple, realizes and pick early period
The detection of suspicious barrier promotes the intelligence of agricultural equipment for improving the picking efficiency and reliability of apple-picking device
Picking is horizontal, there is highly important realistic meaning and wide application prospect.
Invention content
In view of the above-mentioned problems, the present invention is intended to provide a kind of intelligence orchard management system.
The purpose of the present invention is realized using following technical scheme：
A kind of intelligent orchard management system is provided, plants apple in orchard, including environment monitoring sensor, signal pass
Defeated device and managing device, the signal transmitting apparatus are established by mobile network between environment monitoring sensor and managing device
Communication connection, the environment monitoring sensor is used to monitor environmental parameter in apple orchard, and the environmental parameter is passed through
Signal transmitting apparatus is sent to managing device, and the managing device includes irrigation rig and picker, and the irrigation rig is used
It is irrigated according to environmental parameter, the picker is for picking transparent apple.
Beneficial effects of the present invention are：A kind of intelligent orchard management system is provided, realizes orchard from irrigating to adopting
The intelligent management plucked.
Description of the drawings
Using attached drawing, the invention will be further described, but the embodiment in attached drawing does not constitute any limit to the present invention
System without creative efforts, can also obtain for those of ordinary skill in the art according to the following drawings
Other attached drawings.
Fig. 1 is the structural schematic diagram of the present invention；
Reference numeral：
Environment monitoring sensor 1, signal transmitting apparatus 2, managing device 3.
Specific implementation mode
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of intelligent orchard management system of the present embodiment, plantation apple in orchard, including environmental monitoring
Sensor 1, signal transmitting apparatus 2 and managing device 3, the signal transmitting apparatus 2 are established environmental monitoring by mobile network and are passed
Communication between sensor 1 and managing device 3, the environment monitoring sensor 1 are used to monitor the environmental parameter in apple orchard, and will
The environmental parameter is sent to managing device 3 by signal transmitting apparatus 2, and the managing device 3 includes irrigation rig and picking
Device, the irrigation rig are used to be irrigated according to environmental parameter, and the picker is for picking transparent apple.
A kind of intelligent orchard management system is present embodiments provided, intelligence pipe of the orchard from irrigating picking is realized
Reason.
Preferably, the picker includes obstacle detector and apple-picking device, the detection of obstacles dress
It sets and is detected for the barrier to apple orchard, the apple-picking device is for picking transparent apple；The barrier
Hinder analyte detection device for being detected to the barrier in apple orchard：The ambient image for acquiring apple orchard, by the image in apple orchard
Lab color spaces are transformed into, barrier is detected according to red green component a in Lab color spaces, set detection threshold value EH,
EH ∈ [- 20, -25], if the red green component a < EH of pixel, as background parts, if the red green component a >=EH of pixel,
Then as barrier.
This preferred embodiment picker is considered as background during detection of obstacles, by green portion, and remaining is regarded
Make barrier, meet the environmental characteristic in apple orchard, realize quick detection of obstacles, to obstacle during apple-picking
Object is hidden.
Preferably, the apple-picking device includes that collecting unit, detection unit, cutting unit, filter unit, feature carry
Unit, recognition unit and manipulator are taken, the collecting unit is for acquiring tree crown Apple image, and the detection unit will be for that will set
Hat Apple image is transformed into Lab color spaces, is detected to apple, and the cutting unit is used for transformed tree crown apple
Image is split, and for being filtered to the image after segmentation, the feature extraction unit is used for the filter unit
The feature of apple is extracted according to the image after being filtered, the recognition unit is identified apple according to feature,
The manipulator picks apple according to recognition result.
In image acquisition process, since branch, trunk, leaf block, in the image of acquisition apple mostly loses carefully
Contour feature is saved, object edge situation is also remote undesirable, and prodigious difficulty is brought for tree crown apple identification, due to planting garden
The contextual factors such as sky, illumination, house, shade influence, the fruit shape feature of apple also tends to be submerged in all kinds of backgrounds or image
In noise.This preferred embodiment apple-picking device realizes apple by converting unit, the processing of cutting unit, filter unit
The accurate extraction of fruit feature is laid a good foundation for accurately identifying for tree crown apple.
Preferably, the detection unit is used to tree crown Apple image being transformed into Lab color spaces, is examined to apple
It surveys：Apple is detected according to red green component a in Lab color spaces, setting detection threshold value EM, EM ∈ [- 5, -6.5], if picture
The red green component a < EM of vegetarian refreshments, then as background parts, if the red green component a >=EM of pixel, as apple can be picked
Fruit；
The process of ripe apples is regarded as by green to red process, this meets the spy of red green component in Lab color spaces very much
Point, this preferred embodiment detection unit make full use of this promise that can pick the accurate detection of apple, pass through adjusting detection threshold
Value, can adjust the maturity that can pick apple, and detection threshold value is bigger, indicate that the maturity that can pick apple is higher.
Preferably, the cutting unit includes gray processing unit and Threshold segmentation unit, and the gray processing unit is used for will
Red green component image is converted into gray level image in Lab color spaces, and the Threshold segmentation unit is used to carry out threshold to gray level image
Value segmentation, apple is partitioned into from complicated background；The Threshold segmentation unit includes the first cutting unit, the second cutting unit
With fusion cutting unit, first cutting unit is for obtaining first object region, and second cutting unit is for obtaining
Second target area, the fusion cutting unit are used to determine target according to first object region and the second target area.
First cutting unit is for obtaining first object region：If size is the gray level image W of M × N, number of greyscale levels
Gray value for L, pixel (m, n) is hm, n), m ∈ { 1,2 ..., M }, n ∈ { 1,2 ..., N }, 0≤h (m, n)≤L-1, profit
The first segmentation function of image is determined with following formula：In formula, CSW
Indicate the first segmentation function of image, i=h (m, n), piIndicate that gray value is that the pixel of h (m, n) accounts for total pixel number amount in image
Number, t indicate the first segmentation threshold；The first segmentation function is maximized, best first segmentation threshold t ' is obtained： It will be greater than the pixel of best first segmentation threshold
Point is used as first object region, is less than the pixel of best first segmentation threshold as background.
Second cutting unit is for obtaining the second target area：Calculate 5 × 5 neighbours centered on pixel (m, n)
The average gray value k (m, n) in domain, 0≤k (m, n)≤L-1 determine the second segmentation function of image using following formula： In formula, ZCWIndicate the second segmentation of image
Function, j=k (m, n), p (i, j) indicate that grey scale pixel value is i in image and the average gray value of 5 × 5 neighborhood of pixel is j's
The number of pixel accounts for the ratio of the total pixel of image, and (r, s) indicates the second segmentation threshold；The second segmentation function is maximized, is obtained
To best second segmentation threshold (r ', s ')：It will be big
In best second segmentation threshold pixel as the second target area, be less than the pixel of best second segmentation threshold as the back of the body
Scape.
The fusion cutting unit is used to determine target according to first object region and the second target area：By first object
The intersection of region and the second target area is as target, i.e. tree crown apple.
Under normal circumstances, it is converted into the image after gray scale and shows have no the characteristics of rule.To from the irregular ash of milli
It is adaptive in degree image to isolate picking target, it is necessary to the reasonable effective threshold segmentation method of selection.This preferred embodiment point
The method that unit uses twice threshold segmentation is cut, target, i.e. tree crown apple is accurately determined.
Orchard is managed using intelligent orchard management system of the invention, 5 apple orchards is chosen and is tested, respectively
For apple orchard 1, apple orchard 2, apple orchard 3, apple orchard 4, apple orchard 5, management cost and the efficiency of management are counted, with artificial
Management is compared, and generation has the beneficial effect that shown in table：
| The efficiency of management improves | Management cost reduces | |
| Apple orchard 1 | 29% | 27% |
| Apple orchard 2 | 27% | 26% |
| Apple orchard 3 | 26% | 26% |
| Apple orchard 4 | 25% | 24% |
| Apple orchard 5 | 24% | 22% |
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation for protecting range, although being explained in detail to the present invention with reference to preferred embodiment, the ordinary skill apple orchard of this field
It should be appreciated that can be modified or replaced equivalently to technical scheme of the present invention, without departing from technical solution of the present invention
Spirit and scope.
Claims (6)
1. a kind of intelligence orchard management system, which is characterized in that plant apple, including environment monitoring sensor, letter in orchard
Number transmitting device and managing device, the signal transmitting apparatus establish environment monitoring sensor and managing device by mobile network
Between communication connection, the environment monitoring sensor is used to monitor environmental parameter in apple orchard, and by the environmental parameter
It is sent to managing device by signal transmitting apparatus, the managing device includes irrigation rig and picker, the irrigation dress
It sets for being irrigated according to environmental parameter, the picker is for picking transparent apple.
2. intelligence orchard management system according to claim 1, which is characterized in that the picker includes barrier
Detection device and apple-picking device, the obstacle detector is for being detected the barrier in apple orchard, the apple
Fruit picker is for picking transparent apple；The obstacle detector is for examining the barrier in apple orchard
It surveys：The image in apple orchard is transformed into Lab color spaces by the ambient image for acquiring apple orchard, according to red green in Lab color spaces
Component a is detected barrier, setting detection threshold value EH, EH ∈ [- 20, -25], will if the red green component a < EH of pixel
It is as background parts, if the red green component a >=EH of pixel, as barrier.
3. intelligence orchard management system according to claim 2, which is characterized in that the apple-picking device includes adopting
Collect unit, detection unit, cutting unit, filter unit, feature extraction unit, recognition unit and manipulator, the collecting unit
For acquiring tree crown Apple image, the detection unit is used to tree crown Apple image being transformed into Lab color spaces, to apple into
Row detection, for being split to transformed tree crown Apple image, the filter unit is used for segmentation the cutting unit
Image afterwards is filtered, and the feature extraction unit is used to carry out the feature of apple according to the image after being filtered
Extraction, the recognition unit are identified apple according to feature, and the manipulator picks apple according to recognition result.
4. intelligence orchard management system according to claim 3, which is characterized in that the detection unit is used for tree crown
Apple image is transformed into Lab color spaces, is detected to apple：Apple is carried out according to red green component a in Lab color spaces
Detection, setting detection threshold value EM, EM ∈ [- 5, -6.5], if the red green component a < EM of pixel, as background parts, if
The red green component a >=EM of pixel, then as apple can be picked.
5. intelligence orchard management system according to claim 4, which is characterized in that the cutting unit includes gray processing
Unit and Threshold segmentation unit, the gray processing unit are used to convert red green component image in Lab color spaces to gray-scale map
Picture, the Threshold segmentation unit are used to be partitioned into apple from complicated background into row threshold division to gray level image；The threshold
It includes the first cutting unit, the second cutting unit and fusion cutting unit to be worth cutting unit, and first cutting unit is for obtaining
First object region, second cutting unit is taken to be used for foundation for obtaining the second target area, the fusion cutting unit
First object region and the second target area determine target；
First cutting unit is for obtaining first object region：If size is gray level image W, the number of greyscale levels L of M × N,
The gray value of pixel (m, n) is h (m, n), m ∈ { 1,2 ..., M }, n ∈ { 1,2 ..., N }, 0≤h (m, n)≤L-1, under utilization
Formula determines the first segmentation function of image：In formula, CSWIt indicates
First segmentation function of image, i=h (m, n), piIndicate that gray value is that the pixel of h (m, n) accounts for total pixel number amount in image
Number, t indicate the first segmentation threshold；The first segmentation function is maximized, best first segmentation threshold t ' is obtained： It will be greater than the pixel of best first segmentation threshold
Point is used as first object region, is less than the pixel of best first segmentation threshold as background.
6. intelligence orchard management system according to claim 5, which is characterized in that second cutting unit is for obtaining
Take the second target area：Calculate the average gray value k (m, n), 0≤k (m, n) of 5 × 5 neighborhoods centered on pixel (m, n)
≤ L-1 determines the second segmentation function of image using following formula： In formula, ZCWIndicate that the second segmentation function of image, j=k (m, n), p (i, j) indicate
The number for the pixel that grey scale pixel value is i in image and the average gray value of 5 × 5 neighborhood of pixel is j accounts for the total pixel of image
The ratio of point, (r, s) indicate the second segmentation threshold；Maximize the second segmentation function, obtain best second segmentation threshold (r ',
s′)：
The pixel of best second segmentation threshold be will be greater than as the second target area, the pixel for being less than best second segmentation threshold is made
For background；
The fusion cutting unit is used to determine target according to first object region and the second target area：By first object region
Intersection with the second target area is as target, i.e. tree crown apple.
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|---|---|---|---|
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116616045A (en) * | 2023-06-07 | 2023-08-22 | 山东农业工程学院 | Picking method and picking system based on plant growth |
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| CN105719282A (en) * | 2016-01-16 | 2016-06-29 | 常州大学 | Fruit, branch and leaf region obtaining method of red apple images in garden |
| CN106228555A (en) * | 2016-07-22 | 2016-12-14 | 湖南文理学院 | Thresholding Method for Grey Image Segmentation based on Masi entropy measure |
| CN108076830A (en) * | 2016-11-22 | 2018-05-29 | 祝凤娟 | A kind of apple picking robot system based on machine vision |
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Effective date of registration: 20201021 Address after: Team 7, Xuqiao village, Lijun Town, Yongning County, Yinchuan City, Ningxia Hui Autonomous Region, 750100 Applicant after: Ningxia Yuefeng ecological agriculture Polytron Technologies Inc. Address before: 518000 West Tower 1708, Nanshan Software Park, Nanshan Digital Culture Industry Base, 10128 Shennan Avenue, Nanshan Street, Nanshan District, Shenzhen City, Guangdong Province Applicant before: SHENZHEN CHUANGYI INDUSTRIAL TECHNOLOGY Co.,Ltd. |
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Effective date of registration: 20211206 Address after: No. 109, manlai village group, manmengyang village committee, mengzha Town, Menghai County, Xishuangbanna Dai Autonomous Prefecture, Yunnan Province Patentee after: Xishuangbanna Yuefeng Yipin golden berry Agricultural Technology Co.,Ltd. Address before: 750100 team 7, Xuqiao village, Lijun Town, Yongning County, Yinchuan City, Ningxia Hui Autonomous Region Patentee before: Ningxia Yuefeng ecological agriculture Polytron Technologies Inc. |