CN110956132B - Method for constructing weed control model based on unmanned aerial vehicle cooperation intelligence - Google Patents
Method for constructing weed control model based on unmanned aerial vehicle cooperation intelligence Download PDFInfo
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- 241000196324 Embryophyta Species 0.000 title claims abstract description 68
- 238000000034 method Methods 0.000 title claims abstract description 19
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 11
- 241000607479 Yersinia pestis Species 0.000 claims abstract description 8
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- 230000006378 damage Effects 0.000 claims description 10
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- 244000025254 Cannabis sativa Species 0.000 claims description 8
- 230000002363 herbicidal effect Effects 0.000 claims description 6
- 239000004009 herbicide Substances 0.000 claims description 6
- 238000010801 machine learning Methods 0.000 claims description 6
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- 238000003860 storage Methods 0.000 claims description 3
- 235000007164 Oryza sativa Nutrition 0.000 abstract description 3
- 238000004458 analytical method Methods 0.000 abstract description 3
- 235000009566 rice Nutrition 0.000 abstract description 3
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- 235000013339 cereals Nutrition 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000007726 management method Methods 0.000 description 2
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- 230000006870 function Effects 0.000 description 1
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- 238000004519 manufacturing process Methods 0.000 description 1
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Abstract
The invention relates to a method for constructing a weed control model based on unmanned aerial vehicle cooperative intelligence. According to the invention, an edge computing platform is constructed, and the edge computing unmanned aerial vehicle carrying the platform carries out real-time processing and analysis on the collected rice field image data, so that flight protection decision information is quickly obtained and transmitted to the plant protection unmanned aerial vehicle, and the plant protection unmanned aerial vehicle commands the unmanned aerial vehicle to carry out the water and straw pest flight protection operation, thereby greatly improving the flight protection efficiency of the water and straw pest.
Description
Technical Field
The invention relates to the technical field of unmanned aerial vehicle disaster prevention and control platforms, in particular to a method for constructing a grass disaster prevention and control model based on unmanned aerial vehicle cooperation intelligence.
Background
The plant protection unmanned aerial vehicle low-altitude spraying agent, seeds and powder is a novel technology which meets the requirements of modern agriculture and modern plant protection. Compared with the traditional manual sprayer, the agricultural plant protection unmanned aerial vehicle has the characteristics of simplicity in operation, high speed, high efficiency, low cost, uniformity in spraying, good atomization effect, large-scale operation and the like, can effectively reduce the labor intensity of farmers, improve the grain quality and yield, reduce the pollution of pesticides to field environment, and ensure the ecology and the grain production safety.
Aiming at larger-scale operation, an edge computing platform based on the unmanned aerial vehicle is constructed, the unmanned aerial vehicle is managed and planned more efficiently, and more accurate and optimized evaluation of a rice weed identification model and a weed severity level is more and more important.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a method for constructing a weed control model based on unmanned aerial vehicle cooperation intelligence.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
a method for constructing a weed control model based on unmanned aerial vehicle cooperative intelligence comprises the following steps:
step 1) training a data model: acquiring video and image data of a large number of common rice weeds, learning and training a rice weed identification model, sampling plant number and fresh quality data of the weeds, and learning and training a weed severity level evaluation model;
step 2) building an edge computing unmanned aerial vehicle: the data of the model trained in the step 1) is reduced and optimized, the model is migrated to an embedded edge computing platform with limited computing resources and storage resources, and the embedded edge computing platform is carried on a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3) unmanned aerial vehicle cooperation flight protection operation: the edge computing unmanned aerial vehicle acquires the paddy field area information and generates the flight protection decision information, the plant protection unmanned aerial vehicle establishes communication with the edge computing unmanned aerial vehicle, and receives and carries out the flight protection task of the water and straw injury according to the flight protection decision information of the edge computing unmanned aerial vehicle;
step 4) model re-optimization: and 3) importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice weed identification model and the weed severity level evaluation model, and re-migrating the optimized model to an embedded edge computing platform before the next flying prevention operation to realize closed-loop execution of the whole process.
Further, in the step 3), the step of obtaining the paddy field area information by the edge computing unmanned aerial vehicle includes the following steps:
step 3.1), calculating the flight of the unmanned aerial vehicle around the whole paddy field area by the edge, and determining the range of the paddy field area;
step 3.2) dividing the whole paddy field area into a plurality of virtual grid areas and numbering the virtual grid areas;
step 3.3) taking off the edge computing unmanned aerial vehicle again, photographing and computing the weed conditions in the grid area through an edge computing platform carried by the edge computing unmanned aerial vehicle, and determining the weed types and the weed severity level;
step 3.4), the edge computing unmanned aerial vehicle transmits the grid number, the grid area position, the weed type and the grass damage severity level as flight protection decision information to the plant protection unmanned aerial vehicle, determines the flight track of the plant protection unmanned aerial vehicle, and sprays the herbicide according to the grass damage severity level and the flight protection track when the plant protection unmanned aerial vehicle flies to a designated position with the corresponding herbicide;
step 3.5), the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue photographing and computing, and the step 3.4) is executed in a circulating way until all the grid areas are completed.
Further, in the step 1), a rice weed identification model and a weed severity level evaluation model are learned and trained on a cloud server by using a TensorFlow machine learning framework.
Further, in the step 1), the identification of the common weeds in the operation area is completed first, 9 points are taken for each virtual grid area by using a 9-point sampling method, and each point is investigated by 0.25m 2 Counting the number of plants and fresh quality of weeds, and training a water straw pest severity level evaluation model by using a TensorFlow machine learning framework.
In step 2), the trained model is subjected to data reduction and optimization through an MCU-level AI reasoning framework tennine-Lite, and is migrated to an embedded edge computing platform based on an open source edge computing framework OpenEdge, and then the edge computing platform is carried on the open source pixhawk four-rotor unmanned aerial vehicle to form the edge computing unmanned aerial vehicle.
The beneficial effects of the invention are as follows:
according to the invention, an edge computing platform is constructed, and the edge computing unmanned aerial vehicle carrying the platform carries out real-time processing and analysis on the collected rice field image data, so that flight protection decision information is quickly obtained and transmitted to the plant protection unmanned aerial vehicle, and the plant protection unmanned aerial vehicle commands the unmanned aerial vehicle to carry out the water and straw pest flight protection operation, thereby greatly improving the flight protection efficiency of the water and straw pest.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
fig. 2 is a diagram illustrating a cooperative flight protection operation execution process of the unmanned aerial vehicle according to the present invention.
Detailed Description
The invention will be described in detail below with reference to the drawings in combination with embodiments.
As shown in fig. 1, a method for constructing a weed control model based on unmanned aerial vehicle cooperative intelligence includes the following steps:
step 1) training a data model: acquiring video and image data of a large number of common rice weeds, learning and training a rice weed identification model, sampling plant number and fresh quality data of the weeds, and learning and training a weed severity level evaluation model;
step 2) building an edge computing unmanned aerial vehicle: the data of the model trained in the step 1) is reduced and optimized, the model is migrated to an embedded edge computing platform with limited computing resources and storage resources, and the embedded edge computing platform is carried on a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3) unmanned aerial vehicle cooperation flight protection operation: the edge computing unmanned aerial vehicle acquires the paddy field area information and generates the flight protection decision information, the plant protection unmanned aerial vehicle establishes communication with the edge computing unmanned aerial vehicle, and receives and carries out the flight protection task of the water and straw injury according to the flight protection decision information of the edge computing unmanned aerial vehicle;
step 4) model re-optimization: and 3) importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice weed identification model and the weed severity level evaluation model, and re-migrating the optimized model to an embedded edge computing platform before the next flying prevention operation to realize closed-loop execution of the whole process.
In the step 3), the step of obtaining the paddy field area information by the edge computing unmanned aerial vehicle comprises the following steps:
step 3.1), calculating the flight of the unmanned aerial vehicle around the whole paddy field area by the edge, and determining the range of the paddy field area;
step 3.2) dividing the whole paddy field area into a plurality of virtual grid areas and numbering the virtual grid areas;
step 3.3) taking off the edge computing unmanned aerial vehicle again, photographing and computing the weed conditions in the grid area through an edge computing platform carried by the edge computing unmanned aerial vehicle, and determining the weed types and the weed severity level;
step 3.4), the edge computing unmanned aerial vehicle transmits the grid number, the grid area position, the weed type and the grass damage severity level as the flying protection decision information to the plant protection unmanned aerial vehicle, and determines the flying track of the plant protection unmanned aerial vehicle, as shown in fig. 2, in the embodiment, the MG-1P plant protection unmanned aerial vehicle is adopted, and the plant protection unmanned aerial vehicle carries the corresponding herbicide to fly to a designated position to spray the herbicide according to the grass damage severity level and the flying protection track;
step 3.5), the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue photographing and computing, and the step 3.4) is executed in a circulating way until all the grid areas are completed.
In the step 1), a rice weed identification model and a weed severity level evaluation model are learned and trained on a cloud server by using a TensorFlow machine learning framework.
In the step 1), the identification of the common weeds in the operation area is finished firstly, specifically, identification types can be determined according to different geographic positions, for example, 32 types of the common weeds in the Suzhou area can be identified preferentially when the method is used in the Suzhou area, 9 points are taken for each virtual grid area by using a 9-point sampling method, and each point is investigated by 0.25m 2 Counting the number of plants and fresh quality of weeds, and training a water straw pest severity level evaluation model by using a TensorFlow machine learning framework.
In the step 2), the trained model is subjected to data reduction and optimization through an MCU-level AI reasoning framework tennine-Lite, and is migrated to an embedded edge computing platform based on an open source edge computing framework OpenEdge, then the edge computing platform is carried on the open source pixhawk four-rotor unmanned aerial vehicle to form the edge computing unmanned aerial vehicle, the OpenEdge framework expands computing capacity to a network edge terminal, low-delay computing services are provided, the low-delay computing services comprise AI reasoning, intelligent analysis, function computation and the like, and management and issuing updating of a rice weed identification model and a weed severity level evaluation model can be realized by the cooperation of the OpenEdge and the existing water straw pest flying prevention cloud management suite, and the acquired rice field image data is processed and analyzed in real time at the edge computing unmanned aerial vehicle, so that flying prevention decision information is obtained rapidly.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The method for constructing the grass damage control model based on unmanned aerial vehicle cooperative intelligence is characterized by comprising the following steps of:
step 1) training a data model: acquiring video and image data of a large number of common rice weeds, learning and training a rice weed identification model, sampling plant number and fresh quality data of the weeds, and learning and training a weed severity level evaluation model;
step 2) building an edge computing unmanned aerial vehicle: the data of the model trained in the step 1) is reduced and optimized, the model is migrated to an embedded edge computing platform with limited computing resources and storage resources, and the embedded edge computing platform is carried on a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3) unmanned aerial vehicle cooperation flight protection operation: the edge computing unmanned aerial vehicle acquires the paddy field area information and generates the flight protection decision information, the plant protection unmanned aerial vehicle establishes communication with the edge computing unmanned aerial vehicle, and receives and carries out the flight protection task of the water and straw injury according to the flight protection decision information of the edge computing unmanned aerial vehicle;
in the step 3), the step of obtaining the paddy field area information by the edge computing unmanned aerial vehicle comprises the following steps:
step 3.1), calculating the flight of the unmanned aerial vehicle around the whole paddy field area by the edge, and determining the range of the paddy field area;
step 3.2) dividing the whole paddy field area into a plurality of virtual grid areas and numbering the virtual grid areas;
step 3.3) taking off the edge computing unmanned aerial vehicle again, photographing and computing the weed conditions in the grid area through an edge computing platform carried by the edge computing unmanned aerial vehicle, and determining the weed types and the weed severity level;
step 3.4), the edge computing unmanned aerial vehicle transmits the grid number, the grid area position, the weed type and the grass damage severity level as flight protection decision information to the plant protection unmanned aerial vehicle, determines the flight track of the plant protection unmanned aerial vehicle, and sprays the herbicide according to the grass damage severity level and the flight protection track when the plant protection unmanned aerial vehicle flies to a designated position with the corresponding herbicide;
step 3.5), the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue photographing and computing, and the step 3.4) is executed in a circulating way until all the grid areas are completed;
step 4) model re-optimization: and 3) importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice weed identification model and the weed severity level evaluation model, and re-migrating the optimized model to an embedded edge computing platform before the next flying prevention operation to realize closed-loop execution of the whole process.
2. The method for constructing a weed control model based on unmanned aerial vehicle collaborative intelligence according to claim 1, wherein in the step 1), a rice weed recognition model and a weed severity level evaluation model are learned and trained on a cloud server by using a TensorFlow machine learning framework.
3. The method for constructing a weed control model based on unmanned aerial vehicle collaborative intelligence according to claim 2, wherein in the step 1), the identification of common weeds in an operation area is completed first, 9 points are taken for each virtual grid area by using a 9-point sampling method, and each point is investigated by 0.25m 2 Counting the number of plants and fresh quality of weeds, and training a water straw pest severity level evaluation model by using a TensorFlow machine learning framework.
4. The method for constructing the weed control model based on the unmanned aerial vehicle collaborative intelligence according to claim 1, wherein in the step 2), the trained model is subjected to data reduction and optimization through an MCU-level AI reasoning framework tennine-Lite, and is migrated to an embedded edge computing platform based on an open source edge computing framework OpenEdge, and then the edge computing platform is carried on the open source pixhawk quadrotor unmanned aerial vehicle to form the edge computing unmanned aerial vehicle.
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| CN112528912A (en) * | 2020-12-19 | 2021-03-19 | 扬州大学 | Crop growth monitoring embedded system and method based on edge calculation |
| CN113126650A (en) * | 2021-03-03 | 2021-07-16 | 华南农业大学 | Automatic weeding operation method for unmanned aerial vehicle |
| CN113255932A (en) * | 2021-06-01 | 2021-08-13 | 开放智能机器（上海）有限公司 | Federal learning training platform and method based on terminal equipment |
| CN115390504A (en) * | 2022-09-29 | 2022-11-25 | 重庆电子工程职业学院 | Wisdom farming system based on 5G thing networking |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104392228A (en) * | 2014-12-19 | 2015-03-04 | 中国人民解放军国防科学技术大学 | Unmanned aerial vehicle image target class detection method based on conditional random field model |
| WO2017074966A1 (en) * | 2015-10-26 | 2017-05-04 | Netradyne Inc. | Joint processing for embedded data inference |
| JP2017159750A (en) * | 2016-03-08 | 2017-09-14 | 国立大学法人京都大学 | Unmanned aircraft position estimation method and system |
| CN109446987A (en) * | 2018-10-29 | 2019-03-08 | 北京麦飞科技有限公司 | Method based on PCA and PNN algorithm detection rice pest grade |
| CN109871029A (en) * | 2019-02-21 | 2019-06-11 | 华南农业大学 | A kind of plant protection drone flight course planning optimization method based on image processing techniques |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104392228A (en) * | 2014-12-19 | 2015-03-04 | 中国人民解放军国防科学技术大学 | Unmanned aerial vehicle image target class detection method based on conditional random field model |
| WO2017074966A1 (en) * | 2015-10-26 | 2017-05-04 | Netradyne Inc. | Joint processing for embedded data inference |
| JP2017159750A (en) * | 2016-03-08 | 2017-09-14 | 国立大学法人京都大学 | Unmanned aircraft position estimation method and system |
| CN109446987A (en) * | 2018-10-29 | 2019-03-08 | 北京麦飞科技有限公司 | Method based on PCA and PNN algorithm detection rice pest grade |
| CN109871029A (en) * | 2019-02-21 | 2019-06-11 | 华南农业大学 | A kind of plant protection drone flight course planning optimization method based on image processing techniques |
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