CN114419429B - Intelligent recommendation method based on crop leaf pathology - Google Patents
Intelligent recommendation method based on crop leaf pathology Download PDFInfo
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- CN114419429B CN114419429B CN202111490562.5A CN202111490562A CN114419429B CN 114419429 B CN114419429 B CN 114419429B CN 202111490562 A CN202111490562 A CN 202111490562A CN 114419429 B CN114419429 B CN 114419429B
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- 238000000034 method Methods 0.000 title claims abstract description 30
- 230000007170 pathology Effects 0.000 title claims abstract description 17
- 201000010099 disease Diseases 0.000 claims abstract description 146
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims abstract description 146
- 238000005516 engineering process Methods 0.000 claims abstract description 14
- 238000005507 spraying Methods 0.000 claims description 77
- 238000002347 injection Methods 0.000 claims description 54
- 239000007924 injection Substances 0.000 claims description 54
- 239000000243 solution Substances 0.000 claims description 39
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 26
- 239000007788 liquid Substances 0.000 claims description 13
- 239000007921 spray Substances 0.000 claims description 13
- 230000001133 acceleration Effects 0.000 claims description 3
- 241000238631 Hexapoda Species 0.000 description 8
- 238000010586 diagram Methods 0.000 description 6
- 238000004590 computer program Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 239000000575 pesticide Substances 0.000 description 4
- 239000003181 biological factor Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000002689 soil Substances 0.000 description 2
- 208000024891 symptom Diseases 0.000 description 2
- 208000035143 Bacterial infection Diseases 0.000 description 1
- 208000031888 Mycoses Diseases 0.000 description 1
- 241000244206 Nematoda Species 0.000 description 1
- 241000700605 Viruses Species 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000003111 delayed effect Effects 0.000 description 1
- 238000002845 discoloration Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 208000015181 infectious disease Diseases 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000017074 necrotic cell death Effects 0.000 description 1
- 235000015097 nutrients Nutrition 0.000 description 1
- 238000003909 pattern recognition Methods 0.000 description 1
- 230000035479 physiological effects, processes and functions Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000003612 virological effect Effects 0.000 description 1
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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
- A01G13/00—Protecting plants
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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
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- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A40/00—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production
- Y02A40/10—Adaptation technologies in agriculture, forestry, livestock or agroalimentary production in agriculture
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Abstract
The embodiment of the invention discloses an intelligent recommendation method based on crop leaf pathology, and relates to the technical field of image recognition. The method comprises the following steps: acquiring leaf images of target crops in real time; identifying whether the leaf image has crop leaf disease characteristics or not through an AI technology; if the leaf image is identified to have the leaf disease characteristics of crops, acquiring a solution corresponding to the disease characteristics according to the preset corresponding relation between the disease characteristics and the solution, and recommending the solution to a user. According to the method and the device, the solution can be recommended according to the identified disease characteristics of the crops, so that the time for searching the solution by agricultural workers when solving the disease problem can be reduced, the damage degree of the crops is reduced, and the crop yield is improved.
Description
Technical Field
The invention belongs to the technical field of image recognition, and particularly relates to an intelligent recommendation method based on crop leaf pathology.
Background
During the growth and development of crops, the normal growth and development are disturbed and destroyed due to the infection of biological factors or the influence of non-biological factors such as fungal diseases, bacterial diseases, viral diseases, nematode diseases and the like, and abnormal phenomena are shown on physiology and tissues inside and outside the crops, so that the yield is reduced, the quality is deteriorated and even the crops die, therefore, the crop diseases are required to be discovered in time and a solution is formulated, and the economic loss is reduced.
At present, crop disease discovery schemes mainly rely on active discovery of artificial experience, and then develop/find solutions, but often experience and energy of crop growers are limited, crop diseases cannot be discovered timely, even crop diseases are seen, and what diseases are not known. With the development of science and computer technology, crop disease discovery methods and systems have been developed, and crop disease images are compared with preset disease images by adopting an image recognition technology, so that the types and symptoms of crops can be recognized. However, when the damage to the crops caused by the diseases is found, people are required to analyze and find a solution, which is very time-consuming, often the optimal time for treating the diseases is missed, the damage degree of the crops is increased, and the yield of the crops is reduced.
Disclosure of Invention
In view of the above, the embodiment of the invention provides an intelligent recommendation method based on crop leaf pathology, which is used for solving the problems that an existing crop disease discovery scheme cannot recommend a proper solution according to crop disease conditions and cannot quickly stop damage of diseases to crops. According to the identified crops and the disease characteristics thereof, the intelligent recommendation of the corresponding solution can reduce the time for agricultural workers to find the solution when solving the disease problem, reduce the damage degree of the crops and improve the crop yield.
The embodiment of the invention provides an intelligent recommendation method based on crop leaf pathology, which comprises the following steps:
acquiring leaf images of target crops in real time;
identifying whether the leaf image has crop leaf disease characteristics or not through an AI technology;
if the leaf image is identified to have the leaf disease characteristics of crops, acquiring a solution corresponding to the disease characteristics according to the preset corresponding relation between the disease characteristics and the solution, and recommending the solution to a user.
In an optional embodiment, after the identifying that the leaf image has the leaf disease feature of the crop, before acquiring the solution corresponding to the disease feature according to the preset correspondence between the disease feature and the solution, the method further includes:
and controlling a preset spraying device to spray liquid to the identified disease characteristic center so as to flush the disease position on the leaves of the target crops.
In an alternative embodiment, the capturing leaf images of the target crop in real time includes:
acquiring leaf images of the target crops in real time through an image acquisition system arranged beside the target crops; the image acquisition system comprises a waterproof camera and a 360-degree turntable, and the turntable can drive the waterproof camera to acquire leaf images of the target crops in all directions.
In an alternative embodiment, the controlling the preset spraying device to spray the liquid to the identified disease feature center includes:
controlling the center of an image acquired by the waterproof camera to be aligned with the center of the identified disease feature;
calculating the relative position of the spraying device preset at the top of the waterproof camera and the disease characteristic;
determining the injection speed of the injection device according to the relative positions of the injection device and the disease features; determining the spraying time of the spraying device according to the size of the disease characteristics;
the spraying device is controlled to spray the liquid to the characteristic center of the disease according to the determined spraying speed and spraying time.
In an alternative embodiment, the calculating the relative position of the spraying device preset on the top of the waterproof camera and the disease feature includes:
calculating the relative position of the spraying device and the disease feature according to the following first formula:
wherein ,L2 A horizontal distance representing the orifice of the spraying device to the disease feature; h represents a vertical height value of a nozzle of the spraying device to the disease feature; l represents the horizontal distance between the waterproof camera and the target crop; d represents the length of the waterproof camera in the shooting direction; r represents the radius of the waterproof camera lens; r represents the nozzle radius of the spraying device; θ represents the elevation angle of the waterproof camera.
In an alternative embodiment, the controlling the spraying speed of the spraying device according to the relative position of the spraying device and the disease feature includes:
calculating the injection speed of the injection device according to the following second formula:
wherein V represents the injection speed of the injection device and g represents the gravitational acceleration.
In an alternative embodiment, the determining the spraying time of the spraying device according to the size of the disease feature includes:
calculating the injection time of the injection device according to the following third formula:
wherein T represents the injection time of the injection device; t (T) max Representing a maximum injection time of the injection device; s represents the number of pixel points occupied by the disease features identified in the collected leaf image, and m represents the number of pixel points in each row of the collected leaf image; n represents the number of each column of pixel points in the acquired leaf image.
In an alternative embodiment, before the determining the spraying time of the spraying device according to the size of the disease feature, the method further includes:
acquiring the current water storage capacity of a water storage tank connected with the spraying device;
calculating the maximum injection time of the injection device according to the following fourth formula:
wherein ,Qmax Is the current water storage capacity of a water storage tank connected with the spraying device.
In an alternative embodiment, after the controlling the spraying device to spray the disease feature center with the specified liquid according to the determined spraying speed and spraying time, further comprising:
when the actual spraying time reaches the spraying time, acquiring the leaf image of the disease characteristic part again;
identifying and judging whether the disease features of the disease feature part are eliminated or not through an AI technology;
if the disease characteristics of the disease characteristic part are eliminated, exiting the process;
and if the disease characteristics of the disease characteristic part are not eliminated, executing the step of acquiring the solution recommended to the user according to the preset corresponding relation between the disease characteristics and the solution.
According to the intelligent recommendation method based on the leaf pathology of the crops, whether the leaf images of the crops are provided with the leaf disease features of the crops or not is identified through an AI pattern identification technology, and if the leaf images of the crops are provided with the leaf disease features of the crops, a solution corresponding to the leaf disease features is obtained according to the preset corresponding relation between the leaf disease features and the solution and recommended to a user. According to the identified crops and the disease characteristics thereof, the intelligent recommendation of the corresponding solution can reduce the time for agricultural workers to find the solution when solving the disease problem, reduce the damage degree of the crops and improve the crop yield.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an embodiment of a method for intelligent recommendation based on leaf pathology of crops, provided by an embodiment of the present invention;
fig. 2 is a flowchart of an embodiment of an intelligent recommendation method based on leaf pathology of crops according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be understood that the described embodiments are merely some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Fig. 1 is a flowchart of an embodiment of an intelligent recommendation method based on leaf pathology of crops according to an embodiment of the present invention. Referring to fig. 1, the method includes the following steps S101-S103:
s101: leaf images of the target crop are acquired in real time.
In this embodiment, the leaf image of the target crop can be collected by the camera, where the camera may be a panoramic camera, a waterproof camera, etc., so that whether there is a disease in the crop can be conveniently analyzed according to the leaf image.
As an alternative embodiment, this step S101 includes: acquiring leaf images of the target crops in real time through an image acquisition system arranged beside the target crops; the image acquisition system comprises a waterproof camera and a 360-degree turntable, and the turntable can drive the waterproof camera to acquire leaf images of the target crops in all directions.
In this embodiment, start the image acquisition system that sets up in target crops next door, 360 degrees revolving stage can drive waterproof camera carries out image acquisition to crops according to from left to right from the top down's order, has guaranteed to carry out image acquisition to the crops leaf, can not miss any leaf.
S102: and identifying whether the leaf image has the crop leaf disease characteristics or not through an AI technology, and executing S103 if the leaf image has the crop leaf disease characteristics.
In this embodiment, the computer vision technology is fast developed, and the corresponding AI identification technology is also developed rapidly, so that it can know whether the disease features exist in the crop leaves in the leaf image according to the leaf features caused by crop diseases, i.e. symptoms (such as discoloration, necrosis, decay, deformity, etc.).
S103: and acquiring a solution corresponding to the disease characteristic according to the preset corresponding relation between the disease characteristic and the solution, and recommending the solution to a user.
In this embodiment, the preset disease characteristics and solutions may be: when the corresponding relation between the insect eyes and pesticide spraying exists on the leaves of the target crops, a solution for spraying the pesticide as soon as possible can be provided, so that a crop worker does not need to spend a great deal of time to find the solution, the damage degree of the crops can be reduced as much as possible, and the crop yield is improved.
As an optional embodiment, before the step S103, the method further includes: and controlling a preset spraying device to spray liquid to the identified disease characteristic center so as to flush the disease position on the leaves of the target crops.
In this embodiment, the sprayed liquid may be water or a preset nutrient solution, a pesticide, or the like, and once the leaf disease feature of the target crop is detected, the sprayed liquid may be treated in advance, so as to treat the crop virus as soon as possible. For example, when insects are present on the leaves, the insects can be rinsed with water, allowed to fall off the leaves, the damage of the insects to the leaves is delayed, and then a solution is given to spray pesticides as soon as possible, thereby solving the diseases of the leaves.
According to the intelligent recommendation method based on the leaf pathology of the crops, whether the leaf images of the crops collected have leaf disease features of the crops is identified through an AI pattern recognition technology, and if so, a solution corresponding to the leaf disease features is obtained and recommended to a user according to the preset corresponding relation between the leaf disease features and the solution. According to the identified crops and the disease characteristics thereof, the intelligent recommendation of the corresponding solution can reduce the time for agricultural workers to find the solution when solving the disease problem, reduce the damage degree of the crops and improve the crop yield.
Fig. 2 is a flowchart of an embodiment of an intelligent recommendation method based on leaf pathology of crops according to an embodiment of the present invention. Referring to fig. 2, the method includes the following steps S201 to S209:
s201: the leaf image of the target crop is acquired in real time by an image acquisition system arranged beside the target crop.
The image acquisition system comprises a waterproof camera and a 360-degree turntable, and the turntable can drive the waterproof camera to acquire leaf images of the target crops in all directions.
S202: and identifying whether the leaf image has the crop leaf disease characteristics or not through an AI technology, and executing S203 if the leaf image has the crop leaf disease characteristics.
S203: and controlling the center of the image acquired by the waterproof camera to be aligned with the center of the identified disease feature.
In the embodiment, the camera is aligned with the center of the identified disease feature, so that clearer and more detailed disease features can be acquired, and the leaf disease can be analyzed more accurately later; meanwhile, the spraying device arranged at the top of the camera is convenient to align to the disease part better, so that the effect of spraying liquid subsequently is better.
S204: and calculating the relative position of the spraying device preset at the top of the waterproof camera and the disease characteristic.
Preferably, the relative position of the spraying device to the disease feature is calculated according to the following first formula:
wherein ,L2 A horizontal distance representing the orifice of the spraying device to the disease feature; h represents a vertical height value of a nozzle of the spraying device to the disease feature; l represents the horizontal distance between the waterproof camera and the target crop; d represents the length of the waterproof camera in the shooting direction; r represents the radius of the waterproof camera lens; r represents the nozzle radius of the spraying device; θ represents the elevation angle of the waterproof camera.
In this embodiment, according to the radius of the injection port of the injection device, the radius of the lens of the waterproof camera, and the horizontal distance between the waterproof camera and the crop, the waterproof camera is aligned to the angle of elevation behind the center point of the disease feature, so as to obtain the horizontal distance and the vertical height required by the water sprayed by the water spraying device to the disease feature of the crop, thereby ensuring that the sprayed water column can be sprayed to the center position of the disease feature.
S205: determining the injection speed of the injection device according to the relative positions of the injection device and the disease features; and determining the spraying time of the spraying device according to the size of the disease characteristic.
Preferably, the injection speed of the injection device is calculated according to the following second formula:
wherein V represents the injection speed of the injection device, g is the gravity acceleration, and the value is 9.80m/s 2 。
In this embodiment, the spraying speed of the water spraying device is obtained according to the horizontal distance and the vertical height required by the water sprayed by the water spraying device to the disease features of the crops, so that the spraying speed is controlled to ensure that the sprayed water column can reach the corresponding height and distance, and the disease features are cleaned.
Preferably, the injection time of the injection device is calculated according to the following third formula:
wherein T represents the injection time of the injection device; t (T) max Representing a maximum injection time of the injection device; s represents the number of pixel points occupied by the disease features identified in the collected leaf image, and m represents the number of pixel points in each row of the collected leaf image; n represents the number of each column of pixel points in the acquired leaf image.
In this embodiment, according to the size of the disease feature on the crop leaf, the water spraying time of the water spraying device is obtained, so that the disease feature on the crop leaf is guaranteed to be effectively washed, the disease feature identified as the cause of soil or insects on the crop leaf is further prevented, the reliability of the system is guaranteed, and the disease feature caused by the insects can be properly solved.
As an optional embodiment, before determining the spraying time of the spraying device according to the size of the disease feature, the method further includes: acquiring the current water storage capacity of a water storage tank connected with the spraying device; calculating the maximum injection time of the injection device according to the fourth formula:
wherein ,Qmax Is the current water storage capacity of a water storage tank connected with the spraying device.
S206: the spraying device is controlled to spray the liquid to the characteristic center of the disease according to the determined spraying speed and spraying time.
S207: and when the actual spraying time reaches the spraying time, acquiring the leaf image of the disease characteristic part again.
In this embodiment, if the water spraying time reaches T, the disease feature cannot be removed yet, and the disease feature is a serious disease feature, then the disease feature identified by AI is subjected to feature analysis and a corresponding solution is obtained and transmitted to the user, so as to reduce the damage degree of crops and improve the yield.
S208: identifying and judging whether the disease features of the disease feature part are eliminated or not through an AI technology; if yes, the exit flow is executed, otherwise S209 is executed.
S209: and acquiring a solution corresponding to the disease characteristic according to the preset corresponding relation between the disease characteristic and the solution, and recommending the solution to a user.
According to the intelligent recommendation method based on the crop leaf pathology, when the crop leaf is identified to have diseases, the water spraying time of the water spraying device is obtained according to the size of the disease features on the crop leaf, so that the disease features on the crop leaf are guaranteed to be effectively washed, the identified disease features caused by soil or insects on the crop leaf are prevented, the reliability of a system is guaranteed, and the disease features caused by the insects can be properly solved.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof. The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the scope of the present invention should be included in the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (4)
1. The intelligent recommendation method based on the crop leaf pathology is characterized by comprising the following steps of:
acquiring leaf images of target crops in real time;
identifying whether the leaf image has crop leaf disease characteristics or not through an AI technology;
if the leaf image is identified to have the leaf disease characteristics of the crops, controlling a preset spraying device to spray liquid to the center of the identified leaf disease characteristics so as to flush the disease position on the leaves of the target crops;
acquiring a solution corresponding to the disease characteristic according to a preset corresponding relation between the disease characteristic and the solution, and recommending the solution to a user;
wherein, the real-time acquisition of leaf images of the target crop includes:
acquiring leaf images of the target crops in real time through an image acquisition system arranged beside the target crops; the image acquisition system comprises a waterproof camera and a 360-degree turntable, and the turntable can drive the waterproof camera to acquire leaf images of the target crops in all directions;
the control of the preset spraying device to spray liquid to the identified disease characteristic center comprises the following steps:
controlling the center of an image acquired by the waterproof camera to be aligned with the center of the identified disease feature;
calculating the relative position of the spraying device preset at the top of the waterproof camera and the disease characteristic;
determining the injection speed of the injection device according to the relative positions of the injection device and the disease features; determining the spraying time of the spraying device according to the size of the disease characteristics;
controlling the spraying device to spray the liquid to the disease characteristic center according to the determined spraying speed and spraying time;
the calculation of the relative position of the spraying device arranged at the top of the waterproof camera in advance and the disease feature comprises the following steps:
calculating the relative position of the spraying device and the disease feature according to the following first formula:
wherein ,
a horizontal distance representing the orifice of the spraying device to the disease feature; />
A vertical height value representative of the spray orifice of the spray device to the disease feature; />
Representing a horizontal distance between the waterproof camera and the target crop; />
Representing the length of the waterproof camera in the shooting direction; />
Representing the radius of the waterproof camera lens; />
Representing a spout radius of the spraying device; />
Representing the elevation angle of the waterproof camera, < >>
；
Wherein, according to the size of disease characteristic, confirm the injection time of injection device, include:
calculating the injection time of the injection device according to the following third formula:
wherein ,
representing the injection time of the injection device; />
Representing a maximum injection time of the injection device; />
Representing the number of pixels occupied by the disease features identified in the acquired leaf image,/->
Representing the number of each row of pixel points in the acquired leaf image; />
And representing the number of each column of pixel points in the acquired leaf image.
2. The intelligent recommendation method based on leaf pathology of crops according to claim 1, characterized in that said controlling the injection speed of said injection device according to the relative position of said injection device and said disease feature comprises:
calculating the injection speed of the injection device according to the following second formula:
wherein ,
indicating the injection speed of the injection device, +.>
Gravitational acceleration.
3. The intelligent recommendation method based on leaf pathology of crops according to claim 2, further comprising, before said determining the injection time of said injection means according to the size of said disease feature:
acquiring the current water storage capacity of a water storage tank connected with the spraying device;
calculating the maximum injection time of the injection device according to the following fourth formula:
wherein ,
is the current water storage capacity of a water storage tank connected with the spraying device.
4. The intelligent recommendation method based on leaf pathology of crops according to claim 1, further comprising, after said controlling spraying means sprays said disease feature center with a specified spraying speed and spraying time to specify a liquid:
when the actual spraying time reaches the spraying time, acquiring the leaf image of the disease characteristic part again;
identifying and judging whether the disease features of the disease feature part are eliminated or not through an AI technology;
if the disease characteristics of the disease characteristic part are eliminated, exiting the process;
and if the disease characteristics of the disease characteristic part are not eliminated, executing the step of acquiring the solution recommended to the user according to the preset corresponding relation between the disease characteristics and the solution.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110162087A (en) * | 2019-05-17 | 2019-08-23 | 安徽舒州农业科技有限责任公司 | A kind of method and device of plant protection drone automatic identification corps diseases |
| CN110326483A (en) * | 2019-08-07 | 2019-10-15 | 徐州嘉农农业发展有限公司 | A kind of crop planting pest control method |
| CN113723157A (en) * | 2020-12-15 | 2021-11-30 | 京东数字科技控股股份有限公司 | Crop disease identification method and device, electronic equipment and storage medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110178605B (en) * | 2019-06-28 | 2021-03-30 | 重庆文理学院 | Automatic sprinkling system of accurate pesticide to kiwi fruit leaf disease |
| CN112286263A (en) * | 2019-07-24 | 2021-01-29 | 中国移动通信有限公司研究院 | Crop regulation and control method, device and system and communication equipment |
| CN111046204A (en) * | 2019-12-16 | 2020-04-21 | 北京植得智能互联科技有限公司 | Plant disease and insect pest recognition and control system |
| CN111596652A (en) * | 2019-12-30 | 2020-08-28 | 山东省科学院自动化研究所 | Pesticide spraying intelligent agricultural machinery path finding navigation control method and system |
| CN111563625A (en) * | 2020-05-08 | 2020-08-21 | 山东贵合信息科技有限公司 | Method, device and medium for spraying pesticide to crops based on Internet of things |
| CN113379769A (en) * | 2021-06-30 | 2021-09-10 | 苏州芯景科技有限公司 | Intelligent defense platform for crop diseases and insect pests |
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110162087A (en) * | 2019-05-17 | 2019-08-23 | 安徽舒州农业科技有限责任公司 | A kind of method and device of plant protection drone automatic identification corps diseases |
| CN110326483A (en) * | 2019-08-07 | 2019-10-15 | 徐州嘉农农业发展有限公司 | A kind of crop planting pest control method |
| CN113723157A (en) * | 2020-12-15 | 2021-11-30 | 京东数字科技控股股份有限公司 | Crop disease identification method and device, electronic equipment and storage medium |
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