CN110956132A - Method for intelligently constructing weed control model based on unmanned aerial vehicle cooperation - Google Patents
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- 241000209094 Oryza Species 0.000 claims abstract description 34
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- RZVHIXYEVGDQDX-UHFFFAOYSA-N 9,10-anthraquinone Chemical compound C1=CC=C2C(=O)C3=CC=CC=C3C(=O)C2=C1 RZVHIXYEVGDQDX-UHFFFAOYSA-N 0.000 claims abstract description 13
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Abstract
The invention relates to a method for establishing a weed control model based on unmanned aerial vehicle collaborative 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 acquired rice field image data, so that flight control decision information is quickly obtained and transmitted to the plant protection unmanned aerial vehicle to command the plant protection unmanned aerial vehicle to carry out rice weed flight control operation, and the rice weed flight control efficiency is greatly improved.
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 intelligently constructing a weed prevention and control model based on unmanned aerial vehicle cooperation.
Background
The low-altitude spraying of the medicament, the seeds and the powder by the plant protection unmanned aerial vehicle is a novel technology which is suitable for 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 simple operation, high speed, high efficiency, low cost, uniform spraying, good atomization effect, large-scale operation and the like, can effectively reduce the labor intensity of farmers, improve the quality and the yield of grains, reduce the pollution of pesticides to the field environment, and ensure the safety of ecology and grain production.
And 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 a rice weed identification model and a weed severity grade are evaluated more accurately and optimally, so that the method is more and more important.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a method for establishing a weed control model based on unmanned aerial vehicle collaborative intelligence.
In order to achieve the technical purpose and achieve the technical effect, the invention is realized by the following technical scheme:
a method for establishing a weed control model based on unmanned aerial vehicle collaborative 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 recognition model, sampling weed plant number and fresh quality data, and learning and training a weed severity grade evaluation model;
step 2), building an edge computing unmanned aerial vehicle: simplifying and optimizing data of the model trained in the step 1), transferring the model to an embedded edge computing platform with limited computing resources and storage resources, and carrying the embedded edge computing platform to a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3), unmanned aerial vehicle cooperative flight prevention operation: the edge calculation unmanned aerial vehicle acquires information of a rice field area and generates flight prevention decision information, the plant protection unmanned aerial vehicle establishes communication with the edge calculation unmanned aerial vehicle, and receives and carries out a flight prevention task of rice weeds according to the flight prevention decision information of the edge calculation unmanned aerial vehicle;
step 4), model re-optimization: importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice and weed identification model and the weed severity level evaluation model, and re-transferring the optimized model to an embedded edge computing platform before the next flying prevention operation so as to realize the closed-loop execution of the whole process.
Further, in step 3), the step of acquiring the information of the rice field area by the edge computing unmanned aerial vehicle includes the following steps:
step 3.1), the edge computing unmanned aerial vehicle flies around the whole paddy field area to determine the paddy field area range;
step 3.2) dividing the whole rice field area into a plurality of virtual grid areas and numbering the grid areas;
step 3.3) taking off the unmanned aerial vehicle again by the edge computing platform, photographing, computing the weed condition in the grid area through the edge computing platform carried by the unmanned aerial vehicle, and determining the weed type and the serious grade of the weed;
step 3.4) the edge computing unmanned aerial vehicle transmits the grid number, the grid area position, the weed type and the weed severity level as flight control decision information to the plant protection unmanned aerial vehicle, determines the flight trajectory of the plant protection unmanned aerial vehicle, and sprays the herbicide to the designated position when the plant protection unmanned aerial vehicle carries the corresponding herbicide to fly to the designated position according to the weed severity level and the flight control trajectory;
and 3.5) the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue to take pictures and compute, and the step 3.4) is executed in a circulating mode according to the path until all grid areas are executed.
Further, in the step 1), a TensorFlow machine learning framework is utilized to learn and train a rice weed identification model and a weed severity level evaluation model on a cloud server.
Further, in the step 1), firstly, the identification of the common weeds in the operation area is completed, 9 points are taken from each virtual grid area by using a 9-point sampling method, and each virtual grid area is investigated for 0.25m2Counting the number of weeds and the fresh quality, and training by using a TensorFlow machine learning frameworkA rice phytotoxicity severity grade evaluation model.
Further, in the step 2), the trained model is subjected to data simplification and optimization through an MCU (microprogrammed control unit) level AI inference framework Tengine-Lite, and is transferred to an embedded edge computing platform based on an open source edge computing framework OpenEdge, and then the edge computing platform is carried on an open source pixhawk quad-rotor unmanned aerial vehicle to form an edge computing unmanned aerial vehicle.
The invention has the beneficial effects that:
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 acquired rice field image data, so that flight control decision information is quickly obtained and transmitted to the plant protection unmanned aerial vehicle to command the plant protection unmanned aerial vehicle to carry out rice weed flight control operation, and the rice weed flight control efficiency is greatly improved.
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FIG. 1 is a schematic flow diagram of the present invention;
fig. 2 is a diagram of an execution process of cooperative flight control operation of the unmanned aerial vehicle according to the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
As shown in fig. 1, a method for constructing a weed control model based on unmanned aerial vehicle collaborative intelligence, the method 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 recognition model, sampling weed plant number and fresh quality data, and learning and training a weed severity grade evaluation model;
step 2), building an edge computing unmanned aerial vehicle: simplifying and optimizing data of the model trained in the step 1), transferring the model to an embedded edge computing platform with limited computing resources and storage resources, and carrying the embedded edge computing platform to a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3), unmanned aerial vehicle cooperative flight prevention operation: the edge calculation unmanned aerial vehicle acquires information of a rice field area and generates flight prevention decision information, the plant protection unmanned aerial vehicle establishes communication with the edge calculation unmanned aerial vehicle, and receives and carries out a flight prevention task of rice weeds according to the flight prevention decision information of the edge calculation unmanned aerial vehicle;
step 4), model re-optimization: importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice and weed identification model and the weed severity level evaluation model, and re-transferring the optimized model to an embedded edge computing platform before the next flying prevention operation so as to realize the closed-loop execution of the whole process.
In the step 3), the edge computing unmanned aerial vehicle acquiring the rice field area information comprises the following steps:
step 3.1), the edge computing unmanned aerial vehicle flies around the whole paddy field area to determine the paddy field area range;
step 3.2) dividing the whole rice field area into a plurality of virtual grid areas and numbering the grid areas;
step 3.3) taking off the unmanned aerial vehicle again by the edge computing platform, photographing, computing the weed condition in the grid area through the edge computing platform carried by the unmanned aerial vehicle, and determining the weed type and the serious grade of the weed;
step 3.4) the edge computing unmanned aerial vehicle transmits grid numbers, grid area positions, weed types and weed severity levels as flight control decision information to the plant protection unmanned aerial vehicle, and determines flight tracks 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 corresponding herbicides to fly to a designated position and sprays the herbicides according to the weed severity levels and the flight control tracks;
and 3.5) the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue to take pictures and compute, and the step 3.4) is executed in a circulating mode according to the path until all grid areas are executed.
In the step 1), a TensorFlow machine learning framework is utilized to learn and train a rice weed recognition model and a weed severity level evaluation model on a cloud server.
In the step 1), the identification of the common weeds in the operation area is firstly completed, the identification types can be determined according to different geographic positions,for example, there are 32 weeds in Suzhou region, and when the weeds are used in Suzhou region, the 32 weeds can be preferentially identified, and 9 points are taken for each virtual grid area by using a 9-point sampling method, and each virtual grid area is surveyed by 0.25m2And counting the number of the weeds and the freshness, and training a rice phytotoxicity severity grade evaluation model by using a TensorFlow machine learning framework.
In the step 2), the trained model is subjected to data simplification and optimization through an MCU (microprogrammed control unit) level AI reasoning framework Tengine-Lite, the model is transferred to an embedded edge computing platform based on an open source edge computing framework OpenEdge, the edge computing platform is carried on an open source pixhawk quad-rotor unmanned aerial vehicle to form an edge computing unmanned aerial vehicle, the OpenEdge framework expands computing capability to a network edge terminal and provides low-delay computing services, the computing services comprise AI inference, intelligent analysis, function computation and the like, the rice weed damage prevention cloud management suite is matched with the OpenEdge and the existing rice weed damage prevention cloud management suite, the rice weed identification model and the weed damage severity evaluation model can be managed and issued and updated through the cloud, and the acquired rice field image data are processed and analyzed in real time at the edge computing unmanned aerial vehicle terminal, so that the flight prevention decision information is obtained quickly.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A method for establishing a weed control model based on unmanned aerial vehicle collaborative intelligence is characterized by comprising 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 recognition model, sampling weed plant number and fresh quality data, and learning and training a weed severity grade evaluation model;
step 2), building an edge computing unmanned aerial vehicle: simplifying and optimizing data of the model trained in the step 1), transferring the model to an embedded edge computing platform with limited computing resources and storage resources, and carrying the embedded edge computing platform to a corresponding unmanned aerial vehicle to form an edge computing unmanned aerial vehicle;
step 3), unmanned aerial vehicle cooperative flight prevention operation: the edge calculation unmanned aerial vehicle acquires information of a rice field area and generates flight prevention decision information, the plant protection unmanned aerial vehicle establishes communication with the edge calculation unmanned aerial vehicle, and receives and carries out a flight prevention task of rice weeds according to the flight prevention decision information of the edge calculation unmanned aerial vehicle;
step 4), model re-optimization: importing the image data acquired by the flying prevention operation in the step 3) into a cloud server, re-optimizing the rice and weed identification model and the weed severity level evaluation model, and re-transferring the optimized model to an embedded edge computing platform before the next flying prevention operation so as to realize the closed-loop execution of the whole process.
2. The method for constructing a model for preventing and controlling weeds based on collaborative intelligence of unmanned aerial vehicles according to claim 1, wherein the step 3) of obtaining information of the paddy field area by the edge computing unmanned aerial vehicle comprises the following steps:
step 3.1), the edge computing unmanned aerial vehicle flies around the whole paddy field area to determine the paddy field area range;
step 3.2) dividing the whole rice field area into a plurality of virtual grid areas and numbering the grid areas;
step 3.3) taking off the unmanned aerial vehicle again by the edge computing platform, photographing, computing the weed condition in the grid area through the edge computing platform carried by the unmanned aerial vehicle, and determining the weed type and the serious grade of the weed;
step 3.4) the edge computing unmanned aerial vehicle transmits the grid number, the grid area position, the weed type and the weed severity level as flight control decision information to the plant protection unmanned aerial vehicle, determines the flight trajectory of the plant protection unmanned aerial vehicle, and sprays the herbicide to the designated position when the plant protection unmanned aerial vehicle carries the corresponding herbicide to fly to the designated position according to the weed severity level and the flight control trajectory;
and 3.5) the edge computing unmanned aerial vehicle flies to the next grid area according to the path to continue to take pictures and compute, and the step 3.4) is executed in a circulating mode according to the path until all grid areas are executed.
3. The method for establishing the weed control model based on unmanned aerial vehicle collaborative intelligence according to claim 2, wherein in the step 1), a TensorFlow machine learning framework is utilized to learn and train a rice weed identification model and a weed severity level evaluation model on a cloud server.
4. The method for establishing the model for preventing and treating weeds based on unmanned aerial vehicle collaborative intelligence according to claim 3, wherein in the step 1), the identification of common weeds in the operation area is firstly completed, 9 points are taken from each virtual grid area by using a 9-point sampling method, and each point is surveyed by 0.25m2And counting the number of the weeds and the freshness, and training a rice phytotoxicity severity grade evaluation model by using a TensorFlow machine learning framework.
5. The method for establishing the phytotoxicity prevention and control model based on unmanned aerial vehicle collaborative intelligence according to claim 1, wherein in step 2), the trained model is subjected to data reduction and optimization through an MCU-level AI inference framework Tengine-Lite, and is transferred to an embedded edge computing platform based on an open source edge computing framework OpenEdge, and then the edge computing platform is mounted on an open source pixhawk quad-rotor 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 |
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