CN116724831A - Image recognition technology-based watermelon industrial seedling raising method, device, equipment and medium - Google Patents
Image recognition technology-based watermelon industrial seedling raising method, device, equipment and medium Download PDFInfo
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- 241000219109 Citrullus Species 0.000 title claims abstract description 124
- 235000012828 Citrullus lanatus var citroides Nutrition 0.000 title claims abstract description 124
- 238000000034 method Methods 0.000 title claims abstract description 36
- 235000015097 nutrients Nutrition 0.000 claims abstract description 39
- 238000012258 culturing Methods 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 6
- 238000003860 storage Methods 0.000 claims description 137
- 239000012452 mother liquor Substances 0.000 claims description 72
- 239000007788 liquid Substances 0.000 claims description 31
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 20
- 238000003756 stirring Methods 0.000 claims description 17
- 239000003337 fertilizer Substances 0.000 claims description 13
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 claims description 6
- CSNNHWWHGAXBCP-UHFFFAOYSA-L Magnesium sulfate Chemical compound [Mg+2].[O-][S+2]([O-])([O-])[O-] CSNNHWWHGAXBCP-UHFFFAOYSA-L 0.000 claims description 6
- ZCCIPPOKBCJFDN-UHFFFAOYSA-N calcium nitrate Chemical compound [Ca+2].[O-][N+]([O-])=O.[O-][N+]([O-])=O ZCCIPPOKBCJFDN-UHFFFAOYSA-N 0.000 claims description 6
- FGIUAXJPYTZDNR-UHFFFAOYSA-N potassium nitrate Chemical compound [K+].[O-][N+]([O-])=O FGIUAXJPYTZDNR-UHFFFAOYSA-N 0.000 claims description 6
- 238000004590 computer program Methods 0.000 claims description 5
- PWHULOQIROXLJO-UHFFFAOYSA-N Manganese Chemical compound [Mn] PWHULOQIROXLJO-UHFFFAOYSA-N 0.000 claims description 3
- LQXZLHKVIRVBTC-UHFFFAOYSA-O [N+](=O)([O-])[O-].[NH4+].[P] Chemical compound [N+](=O)([O-])[O-].[NH4+].[P] LQXZLHKVIRVBTC-UHFFFAOYSA-O 0.000 claims description 3
- 239000003086 colorant Substances 0.000 claims description 3
- 229910052742 iron Inorganic materials 0.000 claims description 3
- 229910052943 magnesium sulfate Inorganic materials 0.000 claims description 3
- 235000019341 magnesium sulphate Nutrition 0.000 claims description 3
- 229910052748 manganese Inorganic materials 0.000 claims description 3
- 239000011572 manganese Substances 0.000 claims description 3
- 229910000402 monopotassium phosphate Inorganic materials 0.000 claims description 3
- 235000019796 monopotassium phosphate Nutrition 0.000 claims description 3
- GNSKLFRGEWLPPA-UHFFFAOYSA-M potassium dihydrogen phosphate Chemical compound [K+].OP(O)([O-])=O GNSKLFRGEWLPPA-UHFFFAOYSA-M 0.000 claims description 3
- 235000010333 potassium nitrate Nutrition 0.000 claims description 3
- 239000004323 potassium nitrate Substances 0.000 claims description 3
- LWIHDJKSTIGBAC-UHFFFAOYSA-K potassium phosphate Substances [K+].[K+].[K+].[O-]P([O-])([O-])=O LWIHDJKSTIGBAC-UHFFFAOYSA-K 0.000 claims description 3
- OTYBMLCTZGSZBG-UHFFFAOYSA-L potassium sulfate Chemical compound [K+].[K+].[O-]S([O-])(=O)=O OTYBMLCTZGSZBG-UHFFFAOYSA-L 0.000 claims description 3
- 229910052939 potassium sulfate Inorganic materials 0.000 claims description 3
- 235000011151 potassium sulphates Nutrition 0.000 claims description 3
- 230000008635 plant growth Effects 0.000 claims 4
- 239000000243 solution Substances 0.000 description 44
- 239000010413 mother solution Substances 0.000 description 9
- 241000196324 Embryophyta Species 0.000 description 2
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000035784 germination Effects 0.000 description 2
- 241000219104 Cucurbitaceae Species 0.000 description 1
- 206010013911 Dysgeusia Diseases 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 238000010923 batch production Methods 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 238000012364 cultivation method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 235000013305 food Nutrition 0.000 description 1
- 230000027939 micturition Effects 0.000 description 1
- 210000003097 mucus Anatomy 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 238000004904 shortening Methods 0.000 description 1
- 238000002791 soaking Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 239000000758 substrate Substances 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 235000015112 vegetable and seed oil Nutrition 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
- A01G22/00—Cultivation of specific crops or plants not otherwise provided for
- A01G22/05—Fruit crops, e.g. strawberries, tomatoes or cucumbers
-
- 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
- A01G31/00—Soilless cultivation, e.g. hydroponics
- A01G31/02—Special apparatus therefor
-
- 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
- A01G7/00—Botany in general
- A01G7/04—Electric or magnetic or acoustic treatment of plants for promoting growth
- A01G7/045—Electric or magnetic or acoustic treatment of plants for promoting growth with electric lighting
-
- 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
- G06V20/60—Type of objects
- G06V20/68—Food, e.g. fruit or vegetables
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/50—Constructional details
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- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Environmental Sciences (AREA)
- Multimedia (AREA)
- Physics & Mathematics (AREA)
- Botany (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Forests & Forestry (AREA)
- Ecology (AREA)
- Biodiversity & Conservation Biology (AREA)
- Signal Processing (AREA)
- Hydroponics (AREA)
Abstract
The invention relates to a method, a device, equipment and a medium for factory seedling raising of watermelons based on an image recognition technology, which comprise the steps of placing watermelon seeds at fixed positions in a seedling raising tray; culturing the seeds in a preset environment for 7-10 days through a period of time; acquiring watermelon seedling leaf images at a plurality of preset time points in a preset time period acquired by a camera; preprocessing the image of the watermelon seedling leaf; determining a target color corresponding to a single watermelon seedling leaf according to the saturation of each pixel point in the single watermelon seedling leaf image; transmitting the target color corresponding to the single watermelon seedling leaf to an expert seedling model; and adjusting the proportion of each element in the nutrient solution and the proportion of red and blue light according to the target color corresponding to the single seedling leaf of the watermelon by using an expert seedling model. Through the mode, the problems of large labor occupation, large labor intensity, high seedling cost and the like in the watermelon seedling process can be solved.
Description
Technical Field
The invention relates to the field of watermelon seedling cultivation, in particular to a watermelon factory seedling cultivation method, device, equipment and medium based on an image recognition technology.
Background
Seedling raising refers to raising seedlings in a nursery, a hotbed or a greenhouse to a certain stage for being transplanted in the ground, and the method can greatly improve the germination rate and the final survival rate of the seeds.
Watermelon, annual herb of cucurbitaceae, summer fruit, pulp taste sweet, can cool down and remove summer heat; seed oil-containing, can be used as recreational food; the pericarp is medicinal and has effects of clearing heat, promoting urination and lowering blood pressure. Cultivated in all places of China, the variety is very numerous, and the forms of epicarp, pulp and seeds are various, and are most famous in Xinjiang, gansu Lanzhou, ningxia Zhongning one, shandong Texas, jiangsu Dongtai and the like.
The seedling raising of watermelons is a precondition for the cultivation of watermelons, and the cultivation of strong and disease-free watermelon seedlings is an important link for the cultivation of watermelons. At present, watermelon seedlings are generally grown by putting watermelon seeds into cold water, rubbing off mucus on the surfaces by hands, pouring the watermelon seeds into a container for soaking, draining water, putting the container into a constant temperature box for constant temperature germination acceleration, putting a seedling tray filled with seedling substrates into the container for seedling culture, and finally hardening seedlings after two true leaves with equal length. The traditional seedling raising method occupies a large amount of labor force, has high labor intensity and high seedling raising cost, and often causes a certain loss to seedling raising work when abnormal climate change is encountered.
Disclosure of Invention
The invention mainly solves the technical problems of providing a watermelon factory seedling method, device, equipment and medium based on an image recognition technology, which can solve the problems of large labor occupation, high labor intensity and high seedling cost in the watermelon seedling process and often bring a certain loss to seedling work when encountering abnormal climate change.
In order to solve the technical problems, the invention adopts a technical scheme that:
in a first aspect, an image recognition technology-based industrialized watermelon seedling method is provided, which is characterized by comprising the following steps:
placing watermelon seeds in a fixed position in a seedling tray;
culturing the seeds in a preset environment for 7-10 days through a period of time;
acquiring watermelon seedling leaf images at a plurality of preset time points in a preset time period acquired by a camera;
preprocessing the image of the watermelon seedling leaf;
determining a target color corresponding to a single watermelon seedling leaf according to the saturation of each pixel point in the single watermelon seedling leaf image;
transmitting the target color corresponding to the single watermelon seedling leaf to an expert seedling model;
and judging the proportion of each element in the nutrient solution and the proportion of red and blue light according to the target color corresponding to the single seedling leaf of the watermelon by using an expert seedling model.
Furthermore, the industrial watermelon seedling method based on the image recognition technology is characterized in that seeds are cultivated for 7-10 days in a preset environment for a period of time, wherein the preset environment comprises a temperature of 25 ℃ and a humidity of 70%.
Furthermore, the industrial watermelon seedling raising method based on the image recognition technology is characterized in that watermelon seedling leaf images of a plurality of preset time points in a preset time period acquired by a camera are acquired, wherein the watermelon seedling leaf images are continuously acquired in an image sequence through a high-definition camera.
Furthermore, the method for industrially culturing watermelon seedlings based on the image recognition technology is characterized by preprocessing the image of the watermelon seedling leaves, and comprising the following steps:
acquiring the saturation of each pixel point in a single watermelon seedling leaf image according to the watermelon seedling leaf image;
according to the saturation of each pixel point in the watermelon seedling blade image, calculating to obtain the color to be detected corresponding to the pixel point with the highest saturation in the watermelon seedling blade;
converting the color to be detected into an RGB color space to obtain the coordinate of the color to be detected in the RGB color space;
and determining a target color according to the coordinates of the color to be detected in the RGB color space.
Further, in the method for cultivating watermelon in factory based on image recognition technology, the determining the target color according to the coordinates of the color to be detected in RGB color space includes:
judging whether the coordinates of the color to be measured in an RGB color space are in a preset range or not;
and if the coordinate of the color to be measured in the RGB color space is in a preset range, determining the color to be measured as the target color.
Furthermore, the method for industrially culturing watermelon seedlings based on the image recognition technology is characterized by further comprising the following steps before the target color corresponding to the single watermelon seedling leaf is transferred to the expert seedling model:
collecting historical watermelon seedling leaf data, and obtaining a historical watermelon seedling leaf color plate, wherein the historical watermelon seedling leaf color plate comprises a plurality of color numbers and color information corresponding to the color numbers, and the color information comprises coordinates of colors corresponding to the color numbers in an RGB color space;
marking the proportion of each element in the nutrient solution required by the seedling culture of the watermelon in the color plate of the historical seedling leaf of the watermelon and the proportion of red and blue light to obtain the color plate of the target seedling leaf of the watermelon after marking, and obtaining the expert seedling model.
The second aspect provides a watermelon industrialized seedling raising device based on an image recognition technology, which is characterized by comprising a mother liquor storage module, a fertilizer mixing module, a liquid return storage module and a seedling raising module; the mother liquor storage module is communicated with the fertilizer mixing module; the fertilizer mixing module is communicated with the seedling raising module; the seedling raising module is communicated with the liquid return storage module; and the liquid return storage module is communicated with the fertilizer mixing module.
Further, in the above-mentioned watermelon industrial seedling raising device based on image recognition technology, it is characterized in that the mother liquor storage module includes a first mother liquor storage tank, a first diaphragm pump, a first electromagnetic flowmeter, a second mother liquor storage tank, a second diaphragm pump, a second electromagnetic flowmeter, a third mother liquor storage tank, a third diaphragm pump, a third electromagnetic flowmeter, a fourth mother liquor storage tank, a fourth diaphragm pump, a fourth electromagnetic flowmeter, a fifth mother liquor storage tank, a fifth diaphragm pump, a fifth electromagnetic flowmeter, a sixth mother liquor storage tank, a sixth diaphragm pump, a seventh electromagnetic flowmeter, an eighth mother liquor storage tank, an eighth diaphragm pump and an eighth electromagnetic flowmeter, the first mother liquor storage tank is communicated with the first diaphragm pump, the first diaphragm pump is communicated with the first electromagnetic flowmeter, the second mother liquor storage tank is communicated with the second diaphragm pump, the fourth diaphragm pump is communicated with the second electromagnetic flowmeter, the second diaphragm pump is communicated with the third electromagnetic flowmeter, the fourth diaphragm pump is communicated with the fifth diaphragm pump, the fourth diaphragm pump is communicated with the fourth electromagnetic flowmeter, the fifth diaphragm pump is communicated with the fourth electromagnetic flowmeter is communicated with the fifth electromagnetic flowmeter, the sixth diaphragm pump is communicated with the sixth electromagnetic flowmeter, the sixth electromagnetic flowmeter is communicated with the nutrient solution storage tank, the seventh mother solution storage tank is communicated with the seventh diaphragm pump, the seventh diaphragm pump is communicated with the seventh electromagnetic flowmeter, the seventh electromagnetic flowmeter is communicated with the nutrient solution storage tank, the eighth mother solution storage tank is communicated with the eighth diaphragm pump, the eighth diaphragm pump is communicated with the eighth electromagnetic flowmeter, and the eighth electromagnetic flowmeter is communicated with the nutrient solution storage tank.
Further, in the industrial watermelon seedling raising device based on the image recognition technology, the stirring module comprises a nutrient solution storage tank, a ninth diaphragm pump, a ninth electromagnetic flowmeter, a stirring motor, a stirring rod, a first liquid level sensor, a first element sensor, a tenth diaphragm pump, a tenth electromagnetic flowmeter and an electric control proportional valve, wherein the ninth diaphragm pump is communicated with a clear water pipe, the ninth diaphragm pump is communicated with the ninth electromagnetic flowmeter, the ninth electromagnetic flowmeter is communicated with the nutrient solution storage tank, the electric control proportional valve is communicated with the tenth diaphragm pump, the tenth diaphragm pump is communicated with the tenth electromagnetic flowmeter, the tenth electromagnetic flowmeter is communicated with the water planting tank, the first element sensor is positioned at the bottom of the nutrient solution storage tank, and the stirring motor is connected with the stirring rod.
Further, the device of growing seedlings of watermelon batch production based on image recognition technique is characterized in that, grow seedlings the module and include support, blue vegetation lamp, red vegetation lamp, camera and water planting groove of growing seedlings, blue vegetation lamp is fixed in water planting groove top, red vegetation lamp is fixed in water planting groove top, the camera is fixed in water planting groove top central point put, the camera is high definition pixel camera, water planting groove right-hand member and mixed fertile module intercommunication, water planting groove left end and back liquid storage module intercommunication.
Furthermore, the watermelon industrialized seedling raising device based on the image recognition technology is characterized in that the liquid return storage module comprises a second liquid level sensor, a second element sensor, an eleventh diaphragm pump, an eleventh electromagnetic flowmeter and a liquid return storage tank, wherein the second element sensor is fixed below the liquid return storage tank, the eleventh diaphragm pump is communicated with the eleventh electromagnetic flowmeter, and the eleventh electromagnetic flowmeter is communicated with the nutrient solution storage tank.
Further, in the watermelon industrialized seedling raising device based on the image recognition technology, a potassium nitrate solution is stored in the first mother liquor storage tank, a calcium nitrate solution is stored in the second mother liquor storage tank, a monopotassium phosphate solution is stored in the third mother liquor storage tank, a magnesium sulfate solution is stored in the fourth mother liquor storage tank, an ammonium nitrate phosphorus solution is stored in the fifth mother liquor storage tank, a chelated iron solution is stored in the sixth mother liquor storage tank, a chelated manganese solution is stored in the seventh mother liquor storage tank, and a potassium sulfate solution is stored in the eighth mother liquor storage tank.
In a third aspect, an electronic device is provided, comprising a processor and a memory; the memory is configured to store executable instructions and the processor is configured to read the executable instructions from the memory, the processor being configured to implement the method of the first aspect described above when the executable instructions are executed by the processor.
In a fourth aspect, a computer readable storage medium is provided, wherein the storage medium stores a computer program, which when executed by a processor, causes the processor to implement the method of the first aspect.
In summary, the beneficial effects of the invention are as follows: the invention adopts the industrial seedling method, and the mechanical, standardized and automatic management can precisely control the proportion of each element and the proportion of red and blue light in the nutrient solution of the watermelon seedling environment, thereby saving labor force, shortening the watermelon seedling period, improving the seedling efficiency, reducing the seedling cost, and simultaneously ensuring that the watermelon seedling is not influenced by environmental changes such as weather, light, soil and the like.
Drawings
FIG. 1 is a schematic flow chart of a watermelon industrial seedling method based on an image recognition technology.
Fig. 2 is a schematic structural diagram of a watermelon factory seedling raising device based on an image recognition technology.
The components in the drawings are marked as follows: 1. a first mother liquor storage tank; 2. a first diaphragm pump; 3. a first electromagnetic flowmeter; 4. a second mother liquor storage tank; 5. a second diaphragm pump; 6. a second electromagnetic flowmeter; 7. a third mother liquor storage tank; 8. a third diaphragm pump; 9. a third electromagnetic flowmeter; 10. a fourth mother liquor storage tank; 11. a fourth diaphragm pump; 12. a fourth electromagnetic flowmeter; 13. a fifth mother liquor storage tank; 14. a fifth diaphragm pump; 15. a fifth electromagnetic flowmeter; 16. a sixth mother liquor storage tank; 17. a sixth diaphragm pump; 18. a sixth electromagnetic flowmeter; 19. a seventh mother liquor storage tank; 20. a seventh diaphragm pump; 21. a seventh electromagnetic flowmeter; 22. an eighth mother liquor storage tank; 23. an eighth diaphragm pump; 24 eighth electromagnetic flowmeter; 25. a ninth diaphragm pump; 26. a ninth electromagnetic flowmeter; 27. a clear water pipe; 28. a stirring motor; 29. a first liquid level sensor; 30. a first element sensor; 31. a stirring rod; 32. a nutrient solution storage tank; 33. an electric control proportional valve; 34. a tenth electromagnetic flowmeter; 35. a tenth diaphragm pump; 36. blue plant growing lamps; 37. a camera; 38. red plant growing lamps; 39. a seedling raising bracket; 40. a second liquid level sensor; 41. a liquid return storage tank; 42. a second element sensor; 43. an eleventh diaphragm pump; 44. eleventh electromagnetic flowmeter.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making clear and defining the scope of the present invention.
The embodiment of the disclosure provides a method, a device, equipment and a medium for factory seedling cultivation of watermelons based on an image recognition technology, and the method for factory seedling cultivation of watermelons based on the image recognition technology is described below with reference to fig. 1.
In an embodiment of the present disclosure, a watermelon industrial seedling method based on an image recognition technology may be performed by an electronic device, which may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a wearable device, etc., and a fixed terminal such as a digital TV, a desktop computer, a smart home device, etc.
In the embodiment of the disclosure, a watermelon factory seedling method based on an image recognition technology can comprise the following steps:
s100, placing watermelon seeds in a seedling tray at a fixed position;
s200, culturing seeds for 7-10 days under a preset environment for a period of time.
In an embodiment of the present disclosure, the preset environment includes a temperature of 25 ℃ and a humidity of 70%.
S300, acquiring watermelon seedling leaf images of a plurality of preset time points in a preset time period acquired by a camera.
In the embodiment of the disclosure, after the image of the watermelon seedling leaf is obtained, the photographed image can be further processed, so that the image quality is improved, and the method is not limited.
In the embodiment of the disclosure, the watermelon seedling leaf images are continuously collected in an image sequence through a high-definition camera.
S400, preprocessing the watermelon seedling leaf image.
In an embodiment of the present disclosure, the pretreatment of the watermelon seedling leaf image comprises:
acquiring the saturation of each pixel point in a single watermelon seedling leaf image according to the watermelon seedling leaf image;
according to the saturation of each pixel point in the image of the watermelon seedling blade, calculating to obtain the color to be detected corresponding to the pixel point with the highest saturation in the image of the watermelon seedling blade;
converting a color to be detected into an RGB color space to obtain the coordinate of the color to be detected in the RGB color space;
and determining the target color according to the coordinates of the color to be detected in the RGB color space.
In an embodiment of the present disclosure, determining a target color according to coordinates of a color to be measured in an RGB color space includes:
judging whether the coordinates of the color to be measured in the RGB color space are in a preset range or not;
and if the coordinate of the color to be measured in the RGB color space is in a preset range, determining the color to be measured as the target color.
S500, determining the target color corresponding to the single watermelon seedling leaf according to the saturation of each pixel point in the single watermelon seedling leaf image.
S600, transmitting the target color corresponding to the single watermelon seedling leaf to an expert seedling model.
In the embodiment of the disclosure, before the target color corresponding to the single watermelon seedling leaf is transferred to the expert seedling model, the method further comprises:
collecting historical watermelon seedling leaf data, and obtaining a historical watermelon seedling leaf color board, wherein the historical watermelon seedling leaf color board comprises a plurality of color numbers and color information corresponding to the color numbers, and the color information comprises coordinates of colors corresponding to the color numbers in an RGB color space;
marking the proportion of each element in the nutrient solution required by the seedling culture of the watermelon in the color plate of the historical seedling leaf of the watermelon and the proportion of red and blue light to obtain the color plate of the target seedling leaf of the watermelon after marking, and obtaining the expert seedling model.
S700, judging the proportion of each element in the nutrient solution and the proportion of red and blue light according to the target color corresponding to the single watermelon seedling leaf through an expert seedling model.
In the embodiment of the disclosure, when the watermelon factory seedling raising device based on the image recognition technology is determined to need to adjust the proportion of each element or the proportion of red and blue light in the nutrient solution, the electronic equipment creates a control instruction and transmits the control instruction to the factory seedling raising device, and the factory seedling raising device adjusts the proportion of each element or the proportion of red and blue light in the nutrient solution according to instruction information contained in the control instruction.
The instruction information included in the control instruction at least includes one of a ratio of elements of the nutrient solution and a ratio of red light to blue light, which is not limited herein.
The following describes a watermelon factory seedling raising device based on an image recognition technology according to an embodiment of the disclosure with reference to fig. 2.
In the embodiment of the disclosure, the watermelon factory seedling raising device based on the image recognition technology can comprise a mother liquor storage module, a fertilizer mixing module, a liquid return storage module and a seedling raising module; the mother liquor storage module is communicated with the fertilizer mixing module; the fertilizer mixing module is communicated with the seedling raising module; the seedling raising module is communicated with the liquid return storage module; and the liquid return storage module is communicated with the fertilizer mixing module.
In the embodiment of the disclosure, the watermelon industrialized seedling raising device based on the image recognition technology, the mother liquor storage module comprises a first mother liquor storage tank 1, a first diaphragm pump 2, a first electromagnetic flowmeter 3, a second mother liquor storage tank 4, a second diaphragm pump 5, a second electromagnetic flowmeter 6, a third mother liquor storage tank 7, a third diaphragm pump 8, a third electromagnetic flowmeter 9, a fourth mother liquor storage tank 10, a fourth diaphragm pump 11, a fourth electromagnetic flowmeter 12, a fifth mother liquor storage tank 13, a fifth diaphragm pump 14, a fifth electromagnetic flowmeter 15, a sixth mother liquor storage tank 16, a sixth diaphragm pump 17, a sixth electromagnetic flowmeter 18, a seventh mother liquor storage tank 19, a seventh diaphragm pump 20, a seventh electromagnetic flowmeter 21, an eighth mother liquor storage tank 22, an eighth diaphragm pump 23 and an eighth electromagnetic flowmeter 24, wherein the first mother liquor storage tank 1 is communicated with the first diaphragm pump 2, the first diaphragm pump 2 is communicated with the first electromagnetic flowmeter 3, the first electromagnetic flowmeter 3 is communicated with the nutrient solution storage tank 32, the second mother solution storage tank 4 is communicated with the second diaphragm pump 5, the second diaphragm pump 5 is communicated with the second electromagnetic flowmeter 6, the second electromagnetic flowmeter 6 is communicated with the nutrient solution storage tank 32, the third mother solution storage tank 7 is communicated with the third diaphragm pump 8, the third diaphragm pump 8 is communicated with the third electromagnetic flowmeter 9, the third electromagnetic flowmeter 9 is communicated with the nutrient solution storage tank 32, the fourth mother solution storage tank 10 is communicated with the fourth diaphragm pump 11, the fourth diaphragm pump 11 is communicated with the fourth electromagnetic flowmeter 12, the fourth electromagnetic flowmeter 12 is communicated with the nutrient solution storage tank 32, the fifth mother solution storage tank 13 is communicated with the fifth diaphragm pump 14, the fifth diaphragm pump 14 is communicated with the fifth electromagnetic flowmeter 15, the fifth electromagnetic flowmeter 15 is communicated with the nutrient solution storage tank 32, the sixth mother solution storage tank 16 is communicated with the sixth diaphragm pump 17, the sixth diaphragm pump 17 is communicated with the sixth electromagnetic flowmeter 18, the sixth electromagnetic flowmeter 18 is communicated with the nutrient solution storage tank 32, the seventh mother solution storage tank 19 is communicated with the seventh diaphragm pump 20, the seventh diaphragm pump 20 is communicated with the seventh electromagnetic flowmeter 21, the seventh electromagnetic flowmeter 21 is communicated with the nutrient solution storage tank 32, the eighth mother solution storage tank 22 is communicated with the eighth diaphragm pump 23, the eighth diaphragm pump 23 is communicated with the eighth electromagnetic flowmeter 24, and the eighth electromagnetic flowmeter 24 is communicated with the nutrient solution storage tank 32.
In the embodiment of the disclosure, the stirring module comprises a nutrient solution storage tank 32, a ninth diaphragm pump 25, a ninth electromagnetic flowmeter 26, a stirring motor 28, a stirring rod 31, a first liquid level sensor 29, a first element sensor 30, a tenth diaphragm pump 35, a tenth electromagnetic flowmeter 34 and an electric control proportional valve 33, wherein the ninth diaphragm pump 25 is communicated with a clear water pipe 27, the ninth diaphragm pump 25 is communicated with the ninth electromagnetic flowmeter 26, the ninth electromagnetic flowmeter 26 is communicated with the nutrient solution storage tank 32, the electric control proportional valve 33 is communicated with the tenth diaphragm pump 35, the tenth diaphragm pump 35 is communicated with the tenth electromagnetic flowmeter 34, the tenth electromagnetic flowmeter 34 is communicated with a hydroponic tank 45, the first element sensor 29 is positioned at the bottom of the nutrient solution storage tank 32, and the stirring motor 28 is connected with the stirring rod 31.
In the embodiment of the disclosure, a watermelon factorization device of growing seedlings based on image recognition technology, the module of growing seedlings includes that support 39, blue vegetation lamp 36, red vegetation lamp 38, camera 37 and hydroponic tank 45 grow seedlings, blue vegetation lamp 36 is fixed in hydroponic tank 45 top, red vegetation lamp 38 is fixed in hydroponic tank 45 top, camera 37 is fixed in hydroponic tank 45 top central point put, camera 37 is high definition pixel camera, hydroponic tank 45 right-hand member and mixed fertile module intercommunication, shui Peicao left end and liquid storage module intercommunication return.
In the embodiment of the disclosure, the watermelon factory seedling raising device based on the image recognition technology, the liquid return storage module comprises a second liquid level sensor 40, a second element sensor 42, an eleventh diaphragm pump 43, an eleventh electromagnetic flowmeter 44 and a liquid return storage tank 41, the second element sensor 40 is fixed below the liquid return storage tank 41, the eleventh diaphragm pump 44 is communicated with the liquid return storage tank 41, the eleventh diaphragm pump 42 is communicated with the eleventh electromagnetic flowmeter 43, and the eleventh electromagnetic flowmeter 43 is communicated with the nutrient solution storage tank 32.
In the embodiment of the disclosure, a watermelon factory seedling raising device based on an image recognition technology is characterized in that a potassium nitrate solution is stored in a first mother liquor storage tank 1, a calcium nitrate solution is stored in a second mother liquor storage tank 4, a monopotassium phosphate solution is stored in a third mother liquor storage tank 7, a magnesium sulfate solution is stored in a fourth mother liquor storage tank 10, an ammonium nitrate phosphorus solution is stored in a fifth mother liquor storage tank 13, a chelated iron solution is stored in a sixth mother liquor storage tank 16, a chelated manganese solution is stored in a seventh mother liquor storage tank 19, and a potassium sulfate solution is stored in an eighth mother liquor storage tank 22.
The method of any embodiment in fig. 1 can be executed by the watermelon factory seedling raising device based on the image recognition technology, and the execution mode and the beneficial effects are similar, and are not repeated here.
The embodiment of the disclosure also provides an electronic device, including: at least one processor, at least one memory, and computer program instructions stored in the memory, which when executed by the processor, implement an image recognition technology-based watermelon industrialized seedling method as described in the embodiments.
The embodiment of the disclosure also provides a storage medium, on which computer program instructions are stored, which is characterized in that when the computer program instructions are executed by a processor, the method for cultivating watermelon seedlings in a factory based on the image recognition technology is realized.
The foregoing description is only illustrative of the present invention and is not intended to limit the scope of the invention, and all equivalent structures or equivalent processes or direct or indirect application in other related technical fields are included in the scope of the present invention.
Claims (10)
1. The industrial watermelon seedling raising method based on the image recognition technology is characterized by comprising the following steps of:
placing watermelon seeds in a fixed position in a seedling tray;
culturing the seeds in a preset environment for 7-10 days through a period of time;
acquiring watermelon seedling leaf images at a plurality of preset time points in a preset time period acquired by a camera;
preprocessing the image of the watermelon seedling leaf;
determining a target color corresponding to a single watermelon seedling leaf according to the saturation of each pixel point in the single watermelon seedling leaf image;
transmitting the target color corresponding to the single watermelon seedling leaf to an expert seedling model;
and adjusting the proportion of each element in the nutrient solution and the proportion of red and blue light according to the target color corresponding to the single seedling leaf of the watermelon by using an expert seedling model.
2. The method for industrially culturing the watermelon seedlings based on the image recognition technology according to claim 1, wherein the preset environment comprises a temperature of 25 ℃ and a humidity of 70%; and continuously acquiring the image sequence of the watermelon seedling leaf images through a high-definition camera.
3. The method for industrial seedling raising of watermelons based on the image recognition technology according to claim 1, wherein the preprocessing of the image of the seedling leaves of watermelons comprises:
acquiring the saturation of each pixel point in a single watermelon seedling leaf image according to the watermelon seedling leaf image;
according to the saturation of each pixel point in the watermelon seedling blade image, calculating to obtain the color to be detected corresponding to the pixel point with the highest saturation in the watermelon seedling blade;
converting the color to be detected into an RGB color space to obtain the coordinate of the color to be detected in the RGB color space;
and determining a target color according to the coordinates of the color to be detected in the RGB color space.
4. The method for industrial seedling cultivation of watermelon based on image recognition technology according to claim 3, wherein said determining the target color according to the coordinates of the color to be detected in RGB color space comprises:
judging whether the coordinates of the color to be measured in an RGB color space are in a preset range or not;
and if the coordinate of the color to be measured in the RGB color space is in a preset range, determining the color to be measured as the target color.
5. The method for industrial seedling raising of watermelons based on the image recognition technology according to claim 1, wherein before transferring the target color corresponding to the single seedling leaf of watermelons to the expert seedling model, further comprising:
collecting historical watermelon seedling leaf data, and obtaining a historical watermelon seedling leaf color plate, wherein the historical watermelon seedling leaf color plate comprises a plurality of color numbers and color information corresponding to the color numbers, and the color information comprises coordinates of colors corresponding to the color numbers in an RGB color space;
marking the proportion of each element in the nutrient solution required by the seedling culture of the watermelon in the color plate of the historical seedling leaf of the watermelon and the proportion of red and blue light to obtain the color plate of the target seedling leaf of the watermelon after marking, and obtaining the expert seedling model.
6. The watermelon factory seedling raising device based on the image recognition technology is characterized by comprising a mother liquor storage module, a fertilizer mixing module, a liquid return storage module and a seedling raising module; the mother liquor storage module is communicated with the fertilizer mixing module; the fertilizer mixing module is communicated with the seedling raising module; the seedling raising module is communicated with the liquid return storage module; the liquid return storage module is communicated with the fertilizer mixing module; the mother liquor storage module comprises a first mother liquor storage tank, a first diaphragm pump, a first electromagnetic flowmeter, a second mother liquor storage tank, a second diaphragm pump, a second electromagnetic flowmeter, a third mother liquor storage tank, a third diaphragm pump, a third electromagnetic flowmeter, a fourth mother liquor storage tank, a fourth diaphragm pump, a fourth electromagnetic flowmeter, a fifth mother liquor storage tank, a fifth diaphragm pump, a fifth electromagnetic flowmeter, a sixth mother liquor storage tank, a sixth diaphragm pump, a sixth electromagnetic flowmeter, a seventh mother liquor storage tank, a seventh electromagnetic flowmeter, an eighth mother liquor storage tank, an eighth diaphragm pump and an eighth electromagnetic flowmeter, wherein the first mother liquor storage tank is communicated with the first diaphragm pump, the first electromagnetic flowmeter is communicated with a nutrient solution storage tank, the second mother liquor storage tank is communicated with the second diaphragm pump, the second diaphragm pump is communicated with the second electromagnetic flowmeter, the second electromagnetic flowmeter is communicated with the nutrient solution storage tank, the third mother liquor storage tank is communicated with the sixth electromagnetic flowmeter, the fourth diaphragm pump is communicated with the fourth electromagnetic flowmeter, the fourth electromagnetic flowmeter is communicated with the fourth electromagnetic flowmeter, the seventh mother liquor storage tank is communicated with a seventh diaphragm pump, the seventh diaphragm pump is communicated with a seventh electromagnetic flowmeter, the seventh electromagnetic flowmeter is communicated with the nutrient solution storage tank, the eighth mother liquor storage tank is communicated with an eighth diaphragm pump, the eighth diaphragm pump is communicated with an eighth electromagnetic flowmeter, and the eighth electromagnetic flowmeter is communicated with the nutrient solution storage tank.
7. The watermelon factory seedling raising device based on the image recognition technology according to claim 6, wherein the stirring module comprises a nutrient solution storage tank, a ninth diaphragm pump, a ninth electromagnetic flowmeter, a stirring motor, a stirring rod, a first liquid level sensor, a first element sensor, a tenth diaphragm pump, a tenth electromagnetic flowmeter and an electric control proportional valve, wherein the ninth diaphragm pump is communicated with a clear water pipe, the ninth diaphragm pump is communicated with the ninth electromagnetic flowmeter, the ninth electromagnetic flowmeter is communicated with the nutrient solution storage tank, the electric control proportional valve is communicated with the tenth diaphragm pump, the tenth diaphragm pump is communicated with the tenth electromagnetic flowmeter, the tenth electromagnetic flowmeter is communicated with a water culture tank, the first element sensor is positioned at the bottom of the nutrient solution storage tank, and the stirring motor is connected with the stirring rod; the seedling raising module comprises a seedling raising support, a blue plant growth lamp, a red plant growth lamp, a camera and a water planting groove, wherein the blue plant growth lamp is fixed above the water planting groove, the red plant growth lamp is fixed above the water planting groove, the camera is fixed at the central position above the water planting groove, the camera is a high-definition pixel camera, the right end of the water planting groove is communicated with the fertilizer mixing module, and the left end of the water planting groove is communicated with the liquid return storage module; the liquid return storage module comprises a second liquid level sensor, a second element sensor, an eleventh diaphragm pump, an eleventh electromagnetic flowmeter and a liquid return storage tank, wherein the second element sensor is fixed below the liquid return storage tank, the eleventh diaphragm pump is communicated with the eleventh electromagnetic flowmeter, and the eleventh electromagnetic flowmeter is communicated with the nutrient solution storage tank.
8. The device for industrial seedling cultivation of watermelons based on the image recognition technology according to claim 6, wherein the first mother liquor storage tank is internally provided with a potassium nitrate solution, the second mother liquor storage tank is internally provided with a calcium nitrate solution, the third mother liquor storage tank is internally provided with a monopotassium phosphate solution, the fourth mother liquor storage tank is internally provided with a magnesium sulfate solution, the fifth mother liquor storage tank is internally provided with an ammonium nitrate phosphorus solution, the sixth mother liquor storage tank is internally provided with a chelated iron solution, the seventh mother liquor storage tank is internally provided with a chelated manganese solution, and the eighth mother liquor storage tank is provided with a potassium sulfate solution.
9. An electronic device comprising a processor and a memory; the memory is configured to store executable instructions and the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement the method of any of the preceding claims 1-5.
10. A computer readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, causes the processor to implement the method of any of the preceding claims 1-5.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117461500A (en) * | 2023-12-27 | 2024-01-30 | 北京市农林科学院智能装备技术研究中心 | Plant factory system, method, device, equipment and medium for accelerating crop breeding |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117461500A (en) * | 2023-12-27 | 2024-01-30 | 北京市农林科学院智能装备技术研究中心 | Plant factory system, method, device, equipment and medium for accelerating crop breeding |
| CN117461500B (en) * | 2023-12-27 | 2024-04-02 | 北京市农林科学院智能装备技术研究中心 | Plant factory system, method, device, equipment and medium for accelerating crop breeding |
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