CN111280151B - Variable pesticide application control method based on cotton growth period recognition - Google Patents
Variable pesticide application control method based on cotton growth period recognition Download PDFInfo
- Publication number
- CN111280151B CN111280151B CN202010081848.7A CN202010081848A CN111280151B CN 111280151 B CN111280151 B CN 111280151B CN 202010081848 A CN202010081848 A CN 202010081848A CN 111280151 B CN111280151 B CN 111280151B
- Authority
- CN
- China
- Prior art keywords
- spraying
- spray
- cotton
- program
- variable
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0003—Atomisers or mist blowers
-
- A—HUMAN NECESSITIES
- A01—AGRICULTURE; FORESTRY; ANIMAL HUSBANDRY; HUNTING; TRAPPING; FISHING
- A01M—CATCHING, TRAPPING OR SCARING OF ANIMALS; APPARATUS FOR THE DESTRUCTION OF NOXIOUS ANIMALS OR NOXIOUS PLANTS
- A01M7/00—Special adaptations or arrangements of liquid-spraying apparatus for purposes covered by this subclass
- A01M7/0089—Regulating or controlling systems
-
- 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
- H04N23/55—Optical parts specially adapted for electronic image sensors; Mounting thereof
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N23/00—Cameras or camera modules comprising electronic image sensors; Control thereof
- H04N23/60—Control of cameras or camera modules
- H04N23/695—Control of camera direction for changing a field of view, e.g. pan, tilt or based on tracking of objects
Landscapes
- Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Multimedia (AREA)
- Signal Processing (AREA)
- Insects & Arthropods (AREA)
- Pest Control & Pesticides (AREA)
- Wood Science & Technology (AREA)
- Zoology (AREA)
- Environmental Sciences (AREA)
- Catching Or Destruction (AREA)
Abstract
The invention relates to a variable pesticide application control method based on cotton growth period identification, which comprises a cotton sensing device, a spraying variable spraying execution device and a pesticide application control system, wherein the spraying variable spraying execution device comprises a spraying control system and a spraying control system; the cotton sensing device comprises a camera, a lens and a signal transmission line; the variable spray executing device comprises a bearing device and a spraying device; the spraying device consists of a nozzle, a medicine storage box, an electromagnetic valve, a medicine conveying pipe and an air conveying auxiliary device; the pesticide application control system comprises a shooting control program, a classification identification program, a spraying parameter optimization program and a spraying execution control program; the method provided by the invention identifies the cotton in each growing period by using a convolutional neural network image identification technology, and then solves the optimal spraying parameter model through a Gaussian response surface and a genetic algorithm, so that variable pesticide application control based on the growing period identification can be realized to meet the requirement of accurate spraying, and the method has the characteristics of being suitable according to local conditions, suitable according to time conditions and suitable according to people.
Description
Technical Field
The invention belongs to the field of agricultural plant protection, and relates to a variable pesticide application control method based on cotton growth period identification, which can meet the requirement of accurate spraying and realize high-precision, low-cost and high-efficiency spraying on cotton.
Technical Field
Cotton is an annual woody plant, one of the most important fiber and oil crops in the world, and the cotton fiber accounts for 35% of the total fiber in the world, and has high commercial value. China is one of the biggest cotton producing countries in the world, and the cotton yield and quality have great significance to China.
Currently, one of the important challenges facing agriculture is the development of precision agricultural technologies. Under the background, the current situation of rough pesticide application of cotton is improved, the phenomena of yield reduction caused by insufficient spraying and land pollution caused by excessive pesticide application are reduced, and the realization of uniform deposition of pesticide liquid is the key of accurate spraying research of cotton.
Through the literature search of the prior art, the patent application number 201510727621.4 of the Chinese invention patent 'a high-efficiency cotton sprayer' provides a high-efficiency cotton sprayer, and the sprayer is additionally provided with an air curtain and a high-pressure spray head to enhance the fog drop penetrability so as to improve the liquid medicine attachment rate. Although the invention can uniformly spray the liquid medicine, has wide spraying range and high efficiency, and can be suitable for various growth periods of plants, the spraying parameters of each growth period are not automatically given, and the growth period needs to be artificially judged to adjust the spraying parameters. In addition, the invention patent of china 'a special high-efficient sprayer for machine-harvested cotton' patent application No. 201810407947.2 proposes a special high-efficient sprayer for machine-harvested cotton, which can help to adjust the height of the sprayer through a camera to reduce the damage to cotton plants in the spraying process, but it does not fully exert the advantages of the camera in the aspect of accurate spraying. Therefore, a variable pesticide application control method based on cotton growth period identification is urgently needed, variable pesticide application to cotton in different growth periods is realized, the control effect on cotton plant diseases and insect pests is enhanced, meanwhile, the pollution of pesticides to the land is reduced, and the yield and the quality of the cotton are improved.
Disclosure of Invention
Aiming at the defects of the existing cotton spraying technology, the invention provides a variable pesticide application control method based on cotton growth period identification, which can realize identification of the cotton growth period and adjust spraying parameters according to the identification result, thereby realizing accurate spraying of cotton in different growth periods.
The invention relates to a variable pesticide application control method based on cotton growth period identification, which comprises the following steps: cotton perception device, variable spraying execution device and control system that gives medicine to poor free of charge.
The cotton sensing device comprises a camera, a lens and a signal transmission line; the camera is an industrial camera meeting the field shooting environment and is used for shooting high-quality cotton plant images; the lens is arranged on the camera and used for adjusting the depth of field and the visual angle of the cotton plant image obtained by the camera; and one end of the signal transmission line is connected with the camera, and the other end of the signal transmission line is connected with the pesticide application control system and is used for transmitting the image shot by the camera to the pesticide application control system.
The variable spray execution device comprises a bearing device and a spraying device; the bearing device can be an unmanned aerial vehicle or a small tractor and is used for carrying the cotton sensing device, the pesticide application control system and the spraying device; the spraying device comprises a nozzle, a medicine storage box, an electromagnetic valve, a medicine conveying pipe and an air conveying auxiliary device; the nozzle is a nozzle with a proper angle and flow rate, is connected with the pesticide conveying pipe and is used for uniformly spraying pesticide liquid on cotton plants; the medicine storage box is fixed on the bearing device and used for storing liquid medicine required by spraying; the electromagnetic valve is arranged on the drug delivery pipe, is controlled by the drug delivery control system and is used for controlling the drug delivery flow; one end of the medicine conveying pipe is connected to the medicine storage box, the other end of the medicine conveying pipe is connected with the nozzle, and the electromagnetic valve is arranged in the middle of the medicine conveying pipe and used for conveying liquid medicine from the medicine storage box to the nozzle; the air supply auxiliary device is fixed on the bearing device and positioned in front of the nozzle, and the spray deposition effect is enhanced by generating an air curtain.
The pesticide application control system comprises a shooting control program, a classification identification program, a spraying parameter optimization program and a spraying execution control program; the shooting control program is used for controlling the camera to shoot and sending the image to the classification identification program; the classification identification program receives the image from the shooting control program and extracts proper characteristics from the image through an image identification technology based on a convolutional neural network to identify the cotton, so that the growing period of the cotton is judged and the cotton growing period identification result is sent to the spray execution program; the spraying parameter optimization program sets multiple levels for orthogonal test and establishes a Gaussian response surface according to three factors of flow, wind speed and carrier advancing speed for cotton in each growing period, and solves the optimal flow, wind speed and advancing speed for each growing period through a genetic algorithm to construct a spraying parameter model and load the model into a spraying execution program; the spraying execution program receives the cotton growth period information from the classification identification program and controls the variable spraying execution device to spray according to the optimal spraying parameter model of the corresponding growth period calculated by the spraying parameter optimization program.
The working process of the variable pesticide application control method based on cotton growth period identification is as follows:
1) solving an optimal spray parameter model in each growth period: selecting three experimental factors of flow, wind speed and carrier advancing speed, setting a plurality of levels, and carrying out spray deposition experiments on cotton in each growing period to obtain the pesticide utilization rate and deposition uniformity (variation coefficient) of each group of experiments; according to the spraying requirements, corresponding weights are given to the pesticide utilization rate and the deposition uniformity according to different emphasis on pursuing efficient pesticide utilization or uniform liquid medicine deposition, so that a spraying objective function is constructed; a spray parameter optimization program in the pesticide application control system establishes a Gaussian response surface according to parameters and experimental results of a spray deposition experiment, then solves the optimal solution of each spray objective function through a genetic algorithm to obtain a spray model of each growth period under each spray requirement, and then transmits the model to a spray execution control program.
2) Training a CNN model for identifying the growing period of cotton: acquiring a large number of cotton pictures and calibrating the growing period of each picture; dividing all pictures into a training set and a test set according to a proper proportion; building a convolutional neural network and training a convolutional neural network model by taking training set cotton as input data; testing the performance of the convolutional neural network model by taking the test set as input data; properly adjusting the convolutional neural network structure according to the verification result of the test set and repeating the training and testing process to optimize the convolutional neural network structure; and finally loading the convolutional neural network model with better performance into a classification recognition program.
3) Cotton image acquisition: and the shooting control program in the pesticide application control system controls the camera to shoot cotton images and transmits the cotton images to the classification and identification program.
4) Identification of the growing period of cotton: the classification identification program in the pesticide application control system adopts a pre-trained convolutional neural network model to identify cotton images collected by a camera, and then sends the identified growing period information to the spray execution program.
5) And (3) spray execution decision: and the spray execution program in the pesticide application control system selects a corresponding spray model according to the identification result in the spray requirement, wherein the identification result is in the identification program for pursuing high-efficiency utilization of pesticide or pursuing uniform deposition of liquid medicine and classification, and the spray execution program controls the spray execution equipment to carry out spray operation.
6) Variable spraying operation: the variable spray execution device receives the control signal in the spray execution program, opens and closes the corresponding electromagnetic valve, adjusts the wind speed of the wind speed and wind conveying auxiliary device and the vehicle traveling speed, and sprays.
7) And (5) repeatedly executing the steps 3) -6), keeping the original spray model by the variable spray executing device to perform spray operation when the spray requirement and the growth period information do not change, and performing spray operation according to the new spray model by the variable spray executing device when the spray requirement or the growth period information change.
Compared with the prior art, the invention has the following innovation points:
1) compared with the non-differential spraying in each growth period, the spraying is carried out by identifying and adjusting the spraying parameters based on the growth periods, so that the pesticide utilization rate can be improved, the deposition uniformity is enhanced, the damage to the land is reduced, and the method is an effective way for realizing accurate spraying.
2) Compared with the domestic spraying machine, the image recognition technology based on the convolutional neural network is innovatively applied, and the function of the airborne camera is fully exerted.
3) The method for establishing the Gaussian response surface according to the spraying parameters and solving the optimal spraying parameter model by using the genetic algorithm is creatively provided and applied to spraying equipment.
4) Really realizes the purpose of adjusting to the human, the local and the time. The method can select an unmanned aerial vehicle or a tractor as a proper bearing device according to different cotton planting plots; different spraying models can be provided according to different emphasis such as effective utilization of pesticide by farmers or even deposition; the most appropriate spraying can be carried out on the cotton at the appropriate time by identifying the growing period.
Drawings
FIG. 1 is a schematic structural view of a variable pesticide application control system based on cotton growth period identification
FIG. 2 is a side view of a variable pesticide application control system device based on cotton growth period recognition
FIG. 3 is a control flow chart of a variable pesticide application control system based on cotton growth period identification
In the figure: 1. cotton sensing device 2, pesticide application control system 3, variable spray execution device 4, camera 5, lens 6, signal transmission line 7, carrying device 8, spraying device 9, nozzle 10, pesticide storage box 11, pesticide conveying pipe 12, electromagnetic valve 13 and air conveying auxiliary device
Detailed Description
The invention is further described below with reference to the accompanying drawings. As shown in fig. 1 and 2, a variable pesticide application control method based on cotton growth period identification comprises the following steps: the device comprises a cotton sensing device (1), a variable spray executing device (3) and a pesticide application control system (2).
As shown in fig. 1 and 2, the cotton sensing device (1) comprises a camera (4), a lens (5) and a signal transmission line (6); the camera (4) is an industrial camera meeting the field shooting environment and is used for shooting high-quality cotton plant images; the lens (5) is arranged on the camera (4) and is used for adjusting the depth of field and the visual angle of the cotton plant image obtained by the camera (4); and one end of the signal transmission line (6) is connected with the camera (4), and the other end of the signal transmission line is connected with the pesticide application control system (2) and is used for transmitting the image shot by the camera (4) to the pesticide application control system (2).
As shown in fig. 1 and 2, the variable spray actuator (3) comprises a bearing device (7) and a spraying device (8); the carrying device (7) can be an unmanned aerial vehicle or a small tractor and is used for carrying the cotton sensing device (1), the pesticide application control system (2) and the spraying device (8); the spraying device (8) comprises a nozzle (9), a medicine storage box (10), an electromagnetic valve (12), a medicine conveying pipe (11) and an air conveying auxiliary device (13); the nozzle (9) is a nozzle (9) with a proper angle and flow rate, is connected with the pesticide delivery pipe (11) and is used for uniformly spraying pesticide liquid on cotton plants; the medicine storage box (10) is fixed on the bearing device (7) and is used for storing liquid medicine required by spraying; the electromagnetic valve (12) is arranged on the drug delivery pipe (11), is controlled by the drug delivery control system (2), and is used for controlling the flow of drug delivery; one end of the medicine conveying pipe (11) is connected to the medicine storage box (10) and the other end is connected with the nozzle (9), and the electromagnetic valve (12) is installed in the middle and used for conveying liquid medicine from the medicine storage box (10) to the nozzle (9); the air supply auxiliary device (13) is fixed on the bearing device (7) and is positioned in front of the nozzle (9), and the spray deposition effect is enhanced by generating an air curtain.
As shown in fig. 1, 2 and 3, the drug administration control system (2) comprises a shooting control program, a classification identification program, a spraying parameter optimization program and a spraying execution control program; the shooting control program is used for controlling the camera (4) to shoot and sending the images to the classification identification program; the classification identification program receives the image from the shooting control program and extracts proper characteristics from the image through an image identification technology based on a convolutional neural network to identify the cotton, so that the growing period of the cotton is judged and the cotton growing period identification result is sent to the spray execution program; the spraying parameter optimization program sets multiple levels for orthogonal test and establishes a Gaussian response surface according to three factors of flow, wind speed and carrier advancing speed for cotton in each growing period, and solves the optimal flow, wind speed and advancing speed for each growing period through a genetic algorithm to construct a spraying parameter model and load the model into a spraying execution program; the spraying execution program receives the cotton growth period information from the classification identification program and controls the variable spraying execution device (3) to spray according to the optimal spraying parameter model of the corresponding growth period calculated by the spraying parameter optimization program.
As shown in fig. 1, 2 and 3, the working process of the variable pesticide application control method based on cotton growth period identification is as follows:
solving an optimal spray parameter model in each growth period: selecting three experimental factors of flow, wind speed and carrier advancing speed
1) Setting a plurality of levels to carry out spray deposition experiments on cotton in each growing period to obtain the pesticide utilization rate and the deposition uniformity (coefficient of variation) of each group of experiments; according to the spraying requirements, corresponding weights are given to the pesticide utilization rate and the deposition uniformity according to different emphasis on pursuing efficient pesticide utilization or uniform liquid medicine deposition, so that a spraying objective function is constructed; a spray parameter optimization program in the pesticide application control system (2) establishes a Gaussian response surface according to parameters and experimental results of a spray deposition experiment, then solves the optimal solution of each spray objective function through a genetic algorithm to obtain a spray model of each growth period under each spray requirement, and then transmits the model to a spray execution control program.
2) Training a CNN model for identifying the growing period of cotton: acquiring a large number of cotton pictures and calibrating the growing period of each picture; dividing all pictures into a training set and a test set according to a proper proportion; building a convolutional neural network and training a convolutional neural network model by taking training set cotton as input data; testing the performance of the convolutional neural network model by taking the test set as input data; properly adjusting the convolutional neural network structure according to the verification result of the test set and repeating the training and testing process to optimize the convolutional neural network structure; and finally loading the convolutional neural network model with better performance into a classification recognition program.
3) Cotton image acquisition: the shooting control program in the pesticide application control system (2) controls the camera (4) to shoot cotton images and transmits the cotton images to the classification and identification program.
4) Identification of the growing period of cotton: the classification and identification program in the pesticide application control system (2) adopts a pre-trained convolutional neural network model to identify cotton images collected by the camera (4), and then sends the identified growing period information to the spray execution program.
5) And (3) spray execution decision: and the spray execution program in the pesticide application control system (2) selects a corresponding spray model according to the recognition result in the identification program which emphasizes on pursuing high-efficiency utilization of pesticide or emphasizing on pursuing uniform deposition of the pesticide liquid and classification according to the spray requirement and controls the variable spray execution equipment to carry out spraying operation.
6) Variable spraying operation: the variable spray execution device (3) receives a control signal in the spray execution program, opens and closes the corresponding electromagnetic valve (12), adjusts the wind speed of the wind speed and the traveling speed of the carrier of the wind conveying auxiliary device (13), and sprays.
7) And (3) repeating the steps 3) -6), keeping the original spray model by the variable spray executing device (3) to carry out spray operation when the spray demand and the growth period information do not change, and carrying out spray operation by the variable spray executing device (3) according to the new spray model when the spray demand or the growth period information change.
Claims (1)
1. A variable pesticide application control method based on cotton growth period identification is characterized in that: comprises a variable pesticide application control system and a variable pesticide application control process;
the variable pesticide application control system comprises a cotton sensing device, a variable spraying execution device and a pesticide application control system;
the cotton sensing device comprises a camera, a lens and a signal transmission line; the camera is an industrial camera meeting the field shooting environment and is used for shooting high-quality cotton plant images; the lens is arranged on the camera and used for adjusting the depth of field and the visual angle of the cotton plant image obtained by the camera; one end of the signal transmission line is connected with the camera, and the other end of the signal transmission line is connected with the pesticide application control system and is used for transmitting the image shot by the camera to the pesticide application control system;
the variable spray execution device comprises a bearing device and a spraying device; the bearing device can be an unmanned aerial vehicle or a small tractor and is used for carrying the cotton sensing device, the pesticide application control system and the spraying device; the spraying device comprises a nozzle, a medicine storage box, an electromagnetic valve, a medicine conveying pipe and an air conveying auxiliary device; the nozzle is a nozzle with a proper angle and flow rate, is connected with the pesticide conveying pipe and is used for uniformly spraying pesticide liquid on cotton plants; the medicine storage box is fixed on the bearing device and used for storing liquid medicine required by spraying; the electromagnetic valve is arranged on the drug delivery pipe, is controlled by the drug delivery control system and is used for controlling the drug delivery flow; one end of the medicine conveying pipe is connected to the medicine storage box, the other end of the medicine conveying pipe is connected with the nozzle, and the electromagnetic valve is arranged in the middle of the medicine conveying pipe and used for conveying liquid medicine from the medicine storage box to the nozzle; the air supply auxiliary device is fixed on the bearing device and positioned in front of the nozzle, and the spray deposition effect is enhanced by generating an air curtain;
the pesticide application control system comprises a shooting control program, a classification identification program, a spraying parameter optimization program and a spraying execution control program; the shooting control program is used for controlling the camera to shoot and sending the image to the classification identification program; the classification identification program receives the image from the shooting control program and extracts proper characteristics from the image through an image identification technology based on a convolutional neural network to identify the cotton, so that the growing period of the cotton is judged and the cotton growing period identification result is sent to the spray execution program; the spraying parameter optimization program sets multiple levels for orthogonal test and establishes a Gaussian response surface according to three factors of flow, wind speed and carrier advancing speed for cotton in each growing period, and solves the optimal flow, wind speed and advancing speed for each growing period through a genetic algorithm to construct a spraying parameter model and load the model into a spraying execution program; the spraying execution program receives the cotton growth period information from the classification identification program and controls the variable spraying execution device to spray according to the optimal spraying parameter model of the corresponding growth period calculated by the spraying parameter optimization program;
the variable pesticide application process comprises the following steps:
1) solving an optimal spray parameter model in each growth period: selecting three experimental factors of flow, wind speed and carrier advancing speed, setting a plurality of levels, and carrying out spray deposition experiments on cotton in each growing period to obtain the pesticide utilization rate and deposition uniformity of each group of experiments; according to the spraying requirements, corresponding weights are given to the pesticide utilization rate and the deposition uniformity according to different emphasis on pursuing efficient pesticide utilization or uniform liquid medicine deposition, so that a spraying objective function is constructed; a spray parameter optimization program in the pesticide application control system establishes a Gaussian response surface according to parameters and experimental results of a spray deposition experiment, then solves the optimal solution of each spray objective function through a genetic algorithm to obtain a spray model of each growth period under each spray requirement, and then transmits the model to a spray execution control program;
2) training a CNN model for identifying the growing period of cotton: acquiring a large number of cotton pictures and calibrating the growing period of each picture; dividing all pictures into a training set and a test set according to a proper proportion; building a convolutional neural network and training a convolutional neural network model by taking training set cotton as input data; testing the performance of the convolutional neural network model by taking the test set as input data; properly adjusting the convolutional neural network structure according to the verification result of the test set and repeating the training and testing process to optimize the convolutional neural network structure; finally, loading the convolutional neural network model with better performance into a classification identification program;
3) cotton image acquisition: a shooting control program in the pesticide application control system controls a camera to shoot cotton images and transmits the cotton images to a classification identification program;
4) identification of the growing period of cotton: a classification identification program in the pesticide application control system adopts a pre-trained convolutional neural network model to identify cotton images collected by a camera, and then identified growing period information is sent to a spray execution program;
5) and (3) spray execution decision: the spray execution program in the pesticide application control system selects a corresponding spray model according to the spray requirement, wherein the spray execution program focuses on pursuing high-efficiency utilization of pesticides or on pursuing uniform deposition of liquid medicine and recognition results in the classification recognition program and controls spray execution equipment to carry out spray operation;
6) variable spraying operation: the variable spray execution device receives a control signal in the spray execution program, opens and closes a corresponding electromagnetic valve, adjusts the wind speed of the wind speed and wind conveying auxiliary device and the vehicle travelling speed, and sprays;
7) and (5) repeatedly executing the steps 3) -6), keeping the original spray model by the variable spray executing device to perform spray operation when the spray requirement and the growth period information do not change, and performing spray operation according to the new spray model by the variable spray executing device when the spray requirement or the growth period information change.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010081848.7A CN111280151B (en) | 2020-02-06 | 2020-02-06 | Variable pesticide application control method based on cotton growth period recognition |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202010081848.7A CN111280151B (en) | 2020-02-06 | 2020-02-06 | Variable pesticide application control method based on cotton growth period recognition |
Publications (2)
Publication Number | Publication Date |
---|---|
CN111280151A CN111280151A (en) | 2020-06-16 |
CN111280151B true CN111280151B (en) | 2021-10-26 |
Family
ID=71026756
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202010081848.7A Active CN111280151B (en) | 2020-02-06 | 2020-02-06 | Variable pesticide application control method based on cotton growth period recognition |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN111280151B (en) |
Families Citing this family (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN112136637A (en) * | 2020-09-27 | 2020-12-29 | 安阳工学院 | Self-adaptive spraying method of cotton defoliant |
CN113008742B (en) * | 2021-02-23 | 2022-08-19 | 中国农业大学 | Method and system for detecting deposition amount of fog drops |
CN113678809A (en) * | 2021-09-18 | 2021-11-23 | 江苏省农业科学院泰州农科所 | Longxiang taro pesticide spraying equipment for preventing taro epidemic disease and preventing method thereof |
Family Cites Families (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN103493796A (en) * | 2013-10-17 | 2014-01-08 | 山东农业大学 | Multi-row electric spraying device for cotton operation |
CN108541683A (en) * | 2018-04-18 | 2018-09-18 | 济南浪潮高新科技投资发展有限公司 | A kind of unmanned plane pesticide spraying system based on convolutional neural networks chip |
US10713484B2 (en) * | 2018-05-24 | 2020-07-14 | Blue River Technology Inc. | Semantic segmentation to identify and treat plants in a field and verify the plant treatments |
CN109122633B (en) * | 2018-06-25 | 2023-10-03 | 华南农业大学 | Plant protection unmanned aerial vehicle accurate variable spraying device for neural network decision-making and control method |
CN109284771B (en) * | 2018-08-03 | 2020-09-15 | 中国农业大学 | Tomato growth model determination method and device |
CN110262435A (en) * | 2019-07-16 | 2019-09-20 | 河海大学常州校区 | Smart greenhouse control system and method based on big data analysis |
-
2020
- 2020-02-06 CN CN202010081848.7A patent/CN111280151B/en active Active
Also Published As
Publication number | Publication date |
---|---|
CN111280151A (en) | 2020-06-16 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN111280151B (en) | Variable pesticide application control method based on cotton growth period recognition | |
CN109122633B (en) | Plant protection unmanned aerial vehicle accurate variable spraying device for neural network decision-making and control method | |
CN106200683B (en) | Unmanned plane plant protection system and plant protection method | |
CN110956132B (en) | Method for constructing weed control model based on unmanned aerial vehicle cooperation intelligence | |
CN109601517B (en) | Device and method for spraying pesticide to various weeds | |
CN104764533A (en) | Intelligent agricultural system based on unmanned aerial vehicle image collecting and thermal infrared imager | |
CN106681400B (en) | A kind of variable spraying system suitable for unmanned plane | |
CN108990944A (en) | Unmanned aerial vehicle remote sensing spray integral method and device based on the fusion of visible light thermal infrared images | |
CN110140704A (en) | A kind of intelligent pesticide spraying method and system for plant protection drone | |
CN112189646A (en) | Greenhouse intelligent plant protection device and method based on crop phenotype machine vision discrimination | |
CN113158750A (en) | Self-feedback learning evaluation method of plant growth model based on convolutional neural network | |
CN208271033U (en) | Multi-source Information Fusion intelligent decision autonomous flight plant protection drone | |
CN111766820A (en) | Intelligent fine management control system applied to intelligent agriculture | |
CN104865935A (en) | Intelligent agricultural system based on unmanned-aerial-vehicle image collection | |
CN107085669B (en) | Pesticide application parameter decision method based on crop planting density and different growth periods | |
CN110533547A (en) | Fruits and vegetables water-fertilizer conditioning method and device and computer readable storage medium | |
CN110915782A (en) | Adjustable plant pesticide spraying system and pesticide spraying method | |
CN113331160B (en) | Special accurate medicine system that spouts of tobacco | |
Hong et al. | Adaptive target spray system based on machine vision for plant protection UAV | |
CN112783228A (en) | Neural network-based large-scale greenhouse PD control system and method | |
CN116724885A (en) | Pulse airflow type layered intelligent pollination device and method for target tomatoes | |
CN106292372A (en) | A kind of urban afforestation based on Internet of Things management system | |
CN204707823U (en) | A kind of travelling car of intelligent weeder | |
TWI682351B (en) | Intelligent scheduling system and method | |
CN112385632A (en) | Variable spraying control system and control method of plant protection unmanned aerial vehicle based on LQR (Long Range response) controller |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
GR01 | Patent grant | ||
GR01 | Patent grant |