Rice planting environment monitoring method and system based on unmanned aerial vehicle technology

Abstract

The invention discloses a rice planting environment monitoring method and system based on an unmanned aerial vehicle technology. The method comprises the steps of field investigation, autonomous planning of a monitoring route, collection and processing of monitoring data, construction of a rice field three-dimensional model and optimization of a rice field growth environment. The invention belongs to the technical field of smart agriculture, and particularly relates to a rice planting environment monitoring method and system based on an unmanned aerial vehicle technology. According to the scheme, a target value is calculated to plan actions so as to form a basic route, the route is optimized through a blocking mechanism, blocking can be relieved randomly when an unmanned aerial vehicle is trapped so as to guarantee normal flight, and the unmanned aerial vehicle can fly normally. Performing monitoring and risk assessment on the edge blind area of the rice field; plane points are found out through a rice field image shot by a multi-view camera, seed points are selected through Harris response values, the area of the seed points is increased according to different feature information in the rice field image, an initial plane is extracted, rice field reconstruction is conducted based on grammar rule segmentation, and a richer and more accurate data basis is provided for rice field three-dimensional reconstruction.

Description

Technical Field

[0001] The present invention belongs to the technical field of smart agriculture, and specifically is a rice planting environment monitoring method and system based on unmanned aerial vehicle technology. Background Art

[0002] The rice planting environment monitoring method based on drone technology is a method of using drones equipped with sensors to take aerial photos and collect data of rice planting areas. By analyzing and processing the collected data, the growth status of rice and the situation of pests and diseases can be timely understood. The purpose is to use the monitoring information so that growers can conduct field management more scientifically and accurately, thereby improving the yield and quality of rice, reducing costs and reducing resource waste. However, the existing rice planting environment monitoring method based on drone technology has the following technical problems: drones are difficult to independently plan efficient, comprehensive and emergency-responsive flight routes in rice field monitoring, cannot break free of obstacles when trapped in special situations, and are prone to collision risks when entering visual blind spots, causing unnecessary economic losses; the spatial structure and morphology of rice fields are unclear, making it difficult to conduct comprehensive data collection, and unable to obtain accurate rice field environmental conditions, resulting in difficulty in detecting potential environmental risks and anomalies, which is not conducive to protecting the rice field ecosystem. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a rice planting environment monitoring method and system based on drone technology, which aims at the technical problems that drones are difficult to independently plan efficient, comprehensive and emergency-responsive flight routes in rice field monitoring, cannot break free from obstacles when trapped in special circumstances, enter visual blind spots and cause collision risks, resulting in unnecessary economic losses. The method and system adopt the method of calculating target values ​​to reasonably plan actions to form basic routes, utilize the blocking mechanism to prompt drones to explore unexplored areas to optimize routes, can randomly unblock drones when they are trapped to ensure normal flight, and can monitor blind spots at the edge of rice fields. measurement and risk assessment; in order to address the technical problems that the spatial structure and morphology of rice fields are unclear, it is difficult to conduct comprehensive data collection, and it is impossible to obtain accurate rice field environmental conditions, which makes it difficult to detect potential environmental risks and anomalies, and is not conducive to protecting the rice field ecosystem, the rice field images taken by a multi-camera are used to find 2D plane points, and the seed points are selected by the Harris response value. The area of ​​the seed points is increased and the initial plane is extracted according to the different feature information in the rice field image. The rice fields are reconstructed based on grammatical rule segmentation, and highly compatible mobile nodes are designed to adapt to the actual environment and integrate sensor networks to provide a richer and more accurate data basis for the three-dimensional reconstruction of rice fields.

[0004] The technical solution adopted by the present invention is as follows: The rice planting environment monitoring method based on drone technology provided by the present invention comprises the following steps:

[0005] Step S1: field survey, specifically determining the rice field monitoring area and entering it into the drone monitoring system;

[0006] Step S2: Autonomously plan the monitoring route, specifically, calculate the target value to determine the drone action, form the basis of the rice field monitoring route, set up a blocking mechanism to encourage the drone to explore the unexplored area to form a better route, and monitor the blind area at the edge of the rice field and calculate the risk assessment index by judging the navigation status of the drone and randomly removing the nearby blocking area when trapped;

[0007] Step S3: monitoring data collection and processing, specifically, the drone monitors according to the planned route, obtains rice field image data and stores it;

[0008] Step S4: constructing a three-dimensional model of a rice field, specifically, using the rice field image taken by a multi-camera to find 2D plane points, selecting seed points through Harris response values, increasing the seed point area and extracting the initial plane according to different feature information in the rice field image, reconstructing the rice field based on grammatical rule segmentation, designing highly compatible mobile nodes to adapt to the actual environment and integrating sensor networks, and providing a richer and more accurate data basis for three-dimensional reconstruction of rice fields;

[0009] Step S5: Optimizing the rice field growth environment, specifically adjusting the rice planting layout according to the three-dimensional model of the rice field.

[0010] Furthermore, in step S1, the field survey includes the following steps:

[0011] Step S11: Determine the monitoring target and scope, clarify the size of the rice planting area to be monitored, conduct on-site field investigations, understand the topography and surrounding environment, and evaluate the safety and feasibility of UAV flight;

[0012] Step S12: Based on the survey results, the boundaries and approximate range of the monitoring area are preliminarily planned on the map, and the final rice field monitoring area information is entered into the UAV monitoring system to prepare for subsequent flight monitoring.

[0013] Furthermore, in step S2, the autonomously planning the monitoring route includes the following steps:

[0014] Step S21: Calculate the target value, which is used to select the action for the drone. The drone determines the next state and action based on the target value, and selects the action that is expected to bring the greatest return. This is the basis for forming the rice field monitoring route. The formula used is as follows:

[0015] ;

[0016] In the formula, y represents the target value, represents the reward obtained at time t+1, γ represents the discount factor, Indicates the state of the drone at the next moment. represents the parameters of the Q network at time t, represents the parameters of the target network, represents the corresponding drone action that maximizes the Q function;

[0017] Step S22: Setting a blocking mechanism, which is used to mark the explored rice field monitoring area. When the UAV selects an action according to the target value, the blocking mechanism will restrict the UAV from entering the explored area, prompting the UAV to explore the unexplored area, thereby forming a more efficient and better route;

[0018] The judgment function is used to determine whether the drone can navigate normally. If the drone is stuck in the current state, the adjacent blocked areas will be randomly unblocked to prevent the drone from being trapped during the monitoring process. The judgment function is set and the formula used is as follows:

[0019] ;

[0020] In the formula, Represents the judgment function. When the judgment function value is 1, it means that the drone is in a normal navigation state; when the judgment function value is -1, it means that the drone is stuck in the current state. u represents the index of the rice field monitoring area, and N represents the set of blocked areas.

[0021] Step S23: Monitoring the blind spot at the edge of the paddy field. When the UAV reaches the edge area and enters the visual blind spot, the edge blind spot risk assessment index is calculated. The formula used is as follows:

[0022] ;

[0023] In the formula, B represents the edge blind area risk assessment index, δ represents the adjustment coefficient, represents the visual visibility probability in the edge area, D represents the distance from the drone to the edge of the rice field, and M represents the density of obstacles in the blind area;

[0024] Step S24: Monitoring route formation, based on the selection of actions and the risk assessment of the blind spot at the edge of the rice field, continuous exploration and adjustment are carried out to form an autonomously planned monitoring route, while avoiding wandering in place, improving learning efficiency and the rationality of route planning.

[0025] Furthermore, in step S3, the monitoring data collection and processing includes the following steps:

[0026] Step S31: the drone monitors the rice field according to the autonomously planned monitoring route, and uses a multi-camera system to photograph the rice field to obtain image data of the rice field;

[0027] Step S32: denoising, enhancing contrast, and adjusting brightness of the collected image data to improve image quality;

[0028] Step S33: extracting features from the rice field image, including the contour, color features and texture features of the rice field;

[0029] Step S34: target recognition, identifying the rice field area and rice plants according to the extracted features, and marking them;

[0030] Step S35: storing the processed image data.

[0031] Furthermore, in step S4, constructing the three-dimensional model of the paddy field includes the following steps:

[0032] Step S41: Find plane points according to the point cloud data, use the rice field image taken by the multi-camera, calculate the depth information of each pixel in the image through the stereo matching algorithm, generate the corresponding 3D point cloud data, classify according to the dimensional features and find the corresponding 2D plane points;

[0033] The dimensional features represent the shape features and height features of different rice field monitoring areas;

[0034] Step S42: extracting the initial plane of the rice field, selecting seed points from the 2D plane points by Harris response values, using the information in the rice field image to determine the dimensional features, point surface distance and growth angle information to increase the area of ​​the seed points, and stopping the growth when the curvature of the seed point is greater than a preset threshold, and extracting the initial plane;

[0035] The dimension features represent features related to the size, size and proportion of the paddy field, and help determine the appropriate range and proportion for initial plane extraction, ensuring that the extracted plane matches the size of the actual paddy field;

[0036] The point surface distance represents the distance information between the seed point and the surface of the surrounding object, which is used to understand the spatial relationship between the rice field and the surrounding environment;

[0037] The growth angle information determines the angle and method of seed point expansion, which directly affects the formation and range of the initial plane;

[0038] The seed point serves as a starting point to guide the direction and range of the entire monitoring expansion;

[0039] The seed point area is continuously expanded to determine the size of the plane area to be segmented, thereby shaping the scale of the initial plane;

[0040] The seed point curvature is used to measure the curvature direction of the seed point, and the monitoring area is controlled by comparing it with a preset threshold to ensure that the extracted plane meets the curvature requirement;

[0041] The preset threshold is a limit value used to determine when the curvature growth of the seed point stops;

[0042] Step S421: Selection of seed points. First, determine the adjacent nodes of each plane point, calculate the covariance matrix and Harris response value to determine the importance of the point, and use the Delaunay triangulation method and α-shape algorithm to determine whether the point is on the boundary. Finally, determine whether to select the point as a seed point, perform the first judgment step, and calculate the Harris response value. The formula used is as follows:

[0043] ;

[0044] ;

[0045] In the formula, Num represents the number of adjacent nodes, M represents the covariance matrix, and i represents the index of the adjacent node. represents the coordinate value of the i-th adjacent node on the x-axis, represents the coordinate value of the i-th adjacent node on the y-axis, represents the coordinate value of the i-th adjacent node on the z-axis, R represents the Harris response value, det(M) represents the determinant of the covariance matrix, tr() represents the trace of the covariance matrix, when the Harris response value is greater than or equal to the preset response threshold, it is judged as a seed point for preliminary prediction; when the Harris response value is less than the preset response threshold, it is judged as a non-seed point;

[0046] The next judgment step is to use the Delaunay triangulation method and the α-shape algorithm to determine whether the initially predicted seed point is on the boundary. When the point is not on the boundary, the initially predicted seed point is classified as a seed point; when the point is on the boundary, the initially predicted seed point is classified as a non-seed point.

[0047] Step S422: Calculate the area growth process of the seed point, the formula used is as follows:

[0048] ;

[0049] ;

[0050] In the formula, It represents the value after the area of ​​the seed point increases at time t+1. represents the value of the seed point area at time t, k represents the growth coefficient, represents the rice field image information at time t, LGT represents the dimensional feature, which is used to determine the basic unit and proportion of the seed point area growth, k1 represents the weight of the dimensional feature in the rice field image information, d represents the point surface distance, which is used to control the range and boundary of the seed point area growth according to the distance from other points, k2 represents the weight of the point surface distance in the rice field image information, ξ represents the growth angle information, which is used to guide the extension angle of the seed point area growth, and k3 represents the weight of the growth angle information in the rice field image information;

[0051] Step S43: Reconstruct the paddy field based on grammatical rule segmentation, segment the paddy field area using actual point cloud data, and divide the initial paddy field plane into different paddy field blocks, and then describe and segment these paddy field blocks by defining grammatical rules, so as to extract and reconstruct each area of ​​the paddy field, limit the number of polygonal patches, and improve the reconstruction effect of sharp feature areas;

[0052] The grammatical rules represent a rule system for describing the segmentation and reconstruction of rice field areas;

[0053] The sharp feature areas refer to the boundaries of rice fields, the junctions between ridges and rice fields, the junctions between irrigation channels and rice fields, and the edges of larger stones in rice fields;

[0054] Step S44: Design highly compatible mobile nodes. Considering that parameters in the actual environment change relatively stably, design highly compatible mobile nodes that can adapt well in the actual rice field environment and can dynamically and well integrate the sensor network to solve the data loss problem caused by failed nodes, improve network reliability, and provide a richer and more accurate data basis for the three-dimensional reconstruction of rice fields.

[0055] Furthermore, in step S5, the optimization of the rice field growth environment is specifically to analyze the undulations and slopes in the terrain according to the three-dimensional reconstruction model of the rice field, improve irrigation and drainage conditions, adjust the rice planting layout, carry out scientific planting planning, and judge the areas prone to breeding of diseases and pests according to the model, and take preventive measures in advance.

[0056] The rice planting environment monitoring system based on drone technology provided by the present invention includes a field survey module, an autonomous monitoring route planning module, a monitoring data collection and processing module, a rice field three-dimensional model building module and a rice field growth environment optimization module;

[0057] The field survey module specifically determines the rice field monitoring area and enters it into the drone monitoring system;

[0058] The autonomous planning monitoring route module specifically calculates the target value to determine the drone action, forms the basis of the rice field monitoring route, sets a blocking mechanism to encourage the drone to explore unexplored areas to form a better route, and monitors the blind spots on the edge of the rice field and calculates risk assessment indicators by judging the navigation status of the drone and randomly removing nearby blocking areas when trapped;

[0059] The monitoring data acquisition and processing module is specifically a drone that monitors according to the planned route, obtains rice field image data and stores it;

[0060] The module for constructing a three-dimensional model of a rice field specifically uses a rice field image taken by a multi-camera to find 2D plane points, selects seed points through Harris response values, increases the area of ​​the seed points and extracts the initial plane according to different feature information in the rice field image, reconstructs the rice field based on grammatical rule segmentation, designs highly compatible mobile nodes to adapt to the actual environment and integrates a sensor network, and provides a richer and more accurate data basis for three-dimensional reconstruction of rice fields;

[0061] The module for optimizing the rice field growth environment specifically adjusts the rice planting layout according to the three-dimensional model of the rice field.

[0062] The beneficial results achieved by the present invention using the above scheme are as follows:

[0063] (1) In order to solve the technical problems that UAVs are difficult to autonomously plan efficient, comprehensive and emergency-responsive flight routes in rice field monitoring, and are unable to break free from obstacles when trapped in special situations, entering visual blind spots and causing collision risks, resulting in unnecessary economic losses, we use the calculation of target values ​​to reasonably plan actions to form basic routes, use the blocking mechanism to encourage UAVs to explore unexplored areas to optimize routes, and can randomly unblock UAVs when they are trapped to ensure normal flight, as well as monitor and conduct risk assessment on blind spots at the edge of rice fields;

[0064] (2) In order to solve the technical problem that the spatial structure and morphology of rice fields are unclear, it is difficult to collect comprehensive data and obtain accurate environmental conditions of rice fields, which makes it difficult to detect potential environmental risks and anomalies, and is not conducive to protecting the rice field ecosystem, the rice field images taken by multi-camera are used to find 2D plane points, and the seed points are selected by Harris response values. The area of ​​the seed points is increased and the initial plane is extracted according to the different feature information in the rice field images. The rice fields are reconstructed based on grammatical rule segmentation. Highly compatible mobile nodes are designed to adapt to the actual environment and integrate sensor networks to provide a richer and more accurate data basis for the three-dimensional reconstruction of rice fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of the process of the rice planting environment monitoring method based on UAV technology provided by the present invention;

[0066] Figure 2 A schematic diagram of a rice planting environment monitoring system based on drone technology provided by the present invention;

[0067] Figure 3 is a schematic flow chart of step S2;

[0068] Figure 4 It is a schematic diagram of the process of step S4.

[0069] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0070] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0071] In the description of the present invention, it should be understood that terms such as “upper”, “lower”, “front”, “back”, “left”, “right”, “top”, “bottom”, “inside” and “outside” indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore should not be understood as limiting the present invention.

[0072] Example 1, see Figure 1 The present invention provides a rice planting environment monitoring method based on drone technology, which comprises the following steps:

[0073] Step S1: field survey, specifically determining the rice field monitoring area and entering it into the drone monitoring system;

[0074] Step S2: Autonomously plan the monitoring route, specifically, calculate the target value to determine the drone action, form the basis of the rice field monitoring route, set up a blocking mechanism to encourage the drone to explore the unexplored area to form a better route, and monitor the blind area at the edge of the rice field and calculate the risk assessment index by judging the navigation status of the drone and randomly removing the nearby blocking area when trapped;

[0075] Step S3: monitoring data collection and processing, specifically, the drone monitors according to the planned route, obtains rice field image data and stores it;

[0076] Step S4: constructing a three-dimensional model of a rice field, specifically, using the rice field image taken by a multi-camera to find 2D plane points, selecting seed points through Harris response values, increasing the seed point area and extracting the initial plane according to different feature information in the rice field image, reconstructing the rice field based on grammatical rule segmentation, designing highly compatible mobile nodes to adapt to the actual environment and integrating sensor networks, and providing a richer and more accurate data basis for three-dimensional reconstruction of rice fields;

[0077] Step S5: Optimizing the rice field growth environment, specifically adjusting the rice planting layout according to the three-dimensional model of the rice field.

[0078] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the field survey includes the following steps:

[0079] Step S11: Determine the monitoring target and scope, clarify the size of the rice planting area to be monitored, conduct on-site field investigations, understand the topography and surrounding environment, and evaluate the safety and feasibility of UAV flight;

[0080] Step S12: Based on the survey results, the boundaries and approximate range of the monitoring area are preliminarily planned on the map, and the final rice field monitoring area information is entered into the UAV monitoring system to prepare for subsequent flight monitoring.

[0081] Example 3, see Figure 1 and Figure 3 This embodiment is based on the above embodiment. In step S2, the autonomously planning the monitoring route includes the following steps:

[0082] Step S21: Calculate the target value, which is used to select the action for the drone. The drone determines the next state and action based on the target value, and selects the action that is expected to bring the greatest return. This is the basis for forming the rice field monitoring route. The formula used is as follows:

[0083] ;

[0084] In the formula, y represents the target value, represents the reward obtained at time t+1, γ represents the discount factor, Indicates the state of the drone at the next moment. represents the parameters of the Q network at time t, represents the parameters of the target network, represents the corresponding drone action that maximizes the Q function;

[0085] Step S22: Setting a blocking mechanism, which is used to mark the explored rice field monitoring area. When the UAV selects an action according to the target value, the blocking mechanism will restrict the UAV from entering the explored area, prompting the UAV to explore the unexplored area, thereby forming a more efficient and better route;

[0086] The judgment function is used to determine whether the drone can navigate normally. If the drone is stuck in the current state, the adjacent blocked areas will be randomly unblocked to prevent the drone from being trapped during the monitoring process. The judgment function is set and the formula used is as follows:

[0087] ;

[0088] In the formula, Represents the judgment function. When the judgment function value is 1, it means that the drone is in a normal navigation state; when the judgment function value is -1, it means that the drone is stuck in the current state. u represents the index of the rice field monitoring area, and N represents the set of blocked areas.

[0089] Step S23: Monitoring the blind spot at the edge of the paddy field. When the UAV reaches the edge area and enters the visual blind spot, the edge blind spot risk assessment index is calculated. The formula used is as follows:

[0090] ;

[0091] In the formula, B represents the edge blind area risk assessment index, δ represents the adjustment coefficient, represents the visual visibility probability in the edge area, D represents the distance from the drone to the edge of the rice field, and M represents the density of obstacles in the blind area;

[0092] Step S24: Monitoring route formation, based on the selection of actions and the risk assessment of the blind spot at the edge of the rice field, continuous exploration and adjustment are carried out to form an autonomously planned monitoring route, while avoiding wandering in place, improving learning efficiency and the rationality of route planning.

[0093] By executing the above operations, the calculated target value is used to reasonably plan the action to form the basic route, the blocking mechanism is used to encourage the UAV to explore the unexplored area to optimize the route, and the blockage can be randomly unblocked when the UAV is trapped to ensure normal flight, as well as monitoring and risk assessment of the blind spot at the edge of the rice field. The technical problems that it is difficult for UAVs to autonomously plan efficient, comprehensive and emergency-responsive flight routes in rice field monitoring, and that they are unable to break free from obstacles when trapped in special circumstances and enter visual blind spots, causing collision risks and resulting in unnecessary economic losses, are solved.

[0094] Example 4, see Figure 1 This embodiment is based on the above embodiment. In step S3, the monitoring data collection and processing includes the following steps:

[0095] Step S31: the drone monitors the rice field according to the autonomously planned monitoring route, and uses a multi-camera system to photograph the rice field to obtain image data of the rice field;

[0096] Step S32: denoising, enhancing contrast, and adjusting brightness of the collected image data to improve image quality;

[0097] Step S33: extracting features from the rice field image, including the contour, color features and texture features of the rice field;

[0098] Step S34: target recognition, identifying the rice field area and rice plants according to the extracted features, and marking them;

[0099] Step S35: storing the processed image data.

[0100] Example 5, see Figure 1 and Figure 4 This embodiment is based on the above embodiment. In step S4, constructing the three-dimensional model of the rice field includes the following steps:

[0101] Step S41: Find plane points according to the point cloud data, use the rice field image taken by the multi-camera, calculate the depth information of each pixel in the image through the stereo matching algorithm, generate the corresponding 3D point cloud data, classify according to the dimensional features and find the corresponding 2D plane points;

[0102] The dimensional features represent the shape features and height features of different rice field monitoring areas;

[0103] Step S42: extracting the initial plane of the rice field, selecting seed points from the 2D plane points by Harris response values, using the information in the rice field image to determine the dimensional features, point surface distance and growth angle information to increase the area of ​​the seed points, and stopping the growth when the curvature of the seed point is greater than a preset threshold, and extracting the initial plane;

[0104] The dimension features represent features related to the size, size and proportion of the paddy field, and help determine the appropriate range and proportion for initial plane extraction, ensuring that the extracted plane matches the size of the actual paddy field;

[0105] The point surface distance represents the distance information between the seed point and the surface of the surrounding object, which is used to understand the spatial relationship between the rice field and the surrounding environment;

[0106] The growth angle information determines the angle and method of seed point expansion, which directly affects the formation and range of the initial plane;

[0107] The seed point serves as a starting point to guide the direction and range of the entire monitoring expansion;

[0108] The seed point area is continuously expanded to determine the size of the plane area to be segmented, thereby shaping the scale of the initial plane;

[0109] The seed point curvature is used to measure the curvature direction of the seed point, and the monitoring area is controlled by comparing it with a preset threshold to ensure that the extracted plane meets the curvature requirement;

[0110] The preset threshold is a limit value used to determine when the curvature growth of the seed point stops;

[0111] Step S421: Selection of seed points. First, determine the adjacent nodes of each plane point, calculate the covariance matrix and Harris response value to determine the importance of the point, and use the Delaunay triangulation method and α-shape algorithm to determine whether the point is on the boundary. Finally, determine whether to select the point as a seed point, perform the first judgment step, and calculate the Harris response value. The formula used is as follows:

[0112] ;

[0113] ;

[0114] In the formula, Num represents the number of adjacent nodes, M represents the covariance matrix, and i represents the index of the adjacent node. represents the coordinate value of the i-th adjacent node on the x-axis, represents the coordinate value of the i-th adjacent node on the y-axis, represents the coordinate value of the i-th adjacent node on the z-axis, R represents the Harris response value, det(M) represents the determinant of the covariance matrix, tr() represents the trace of the covariance matrix, when the Harris response value is greater than or equal to the preset response threshold, it is judged as a seed point for preliminary prediction; when the Harris response value is less than the preset response threshold, it is judged as a non-seed point;

[0115] The next judgment step is to use the Delaunay triangulation method and the α-shape algorithm to determine whether the initially predicted seed point is on the boundary. When the point is not on the boundary, the initially predicted seed point is classified as a seed point; when the point is on the boundary, the initially predicted seed point is classified as a non-seed point.

[0116] Step S422: Calculate the area growth process of the seed point, the formula used is as follows:

[0117] ;

[0118] ;

[0119] In the formula, It represents the value after the area of ​​the seed point increases at time t+1. represents the value of the seed point area at time t, k represents the growth coefficient, represents the rice field image information at time t, LGT represents the dimensional feature, which is used to determine the basic unit and proportion of the seed point area growth, k1 represents the weight of the dimensional feature in the rice field image information, d represents the point surface distance, which is used to control the range and boundary of the seed point area growth according to the distance from other points, k2 represents the weight of the point surface distance in the rice field image information, ξ represents the growth angle information, which is used to guide the extension angle of the seed point area growth, and k3 represents the weight of the growth angle information in the rice field image information;

[0120] Step S43: Reconstruct the paddy field based on grammatical rule segmentation, segment the paddy field area using actual point cloud data, and divide the initial paddy field plane into different paddy field blocks, and then describe and segment these paddy field blocks by defining grammatical rules, so as to extract and reconstruct each area of ​​the paddy field, limit the number of polygonal patches, and improve the reconstruction effect of sharp feature areas;

[0121] The grammatical rules represent a rule system for describing the segmentation and reconstruction of rice field areas;

[0122] The sharp feature areas refer to the boundaries of rice fields, the junctions between ridges and rice fields, the junctions between irrigation channels and rice fields, and the edges of larger stones in rice fields;

[0123] Step S44: Design highly compatible mobile nodes. Considering that parameters in the actual environment change relatively stably, design highly compatible mobile nodes that can adapt well in the actual rice field environment and can dynamically and well integrate the sensor network to solve the data loss problem caused by failed nodes, improve network reliability, and provide a richer and more accurate data basis for the three-dimensional reconstruction of rice fields.

[0124] By performing the above operations, the rice field images taken by a multi-camera are used to find 2D plane points, the seed points are selected by the Harris response value, the seed point area is increased and the initial plane is extracted according to the different feature information in the rice field image, the rice field is reconstructed based on the grammatical rule segmentation, and highly compatible mobile nodes are designed to adapt to the actual environment and integrate the sensor network to provide a richer and more accurate data basis for the three-dimensional reconstruction of the rice field. The technical problems such as the unclear spatial structure and morphology of the rice field, the difficulty in comprehensive data collection, and the inability to obtain accurate rice field environmental conditions, which make it difficult to detect potential environmental risks and anomalies and are not conducive to protecting the rice field ecosystem, are solved.

[0125] Example 6, see Figure 1This embodiment is based on the above embodiment. In step S5, the optimization of the rice field growth environment is specifically to analyze the undulations and slopes in the terrain according to the three-dimensional reconstruction model of the rice field, improve the irrigation and drainage conditions, adjust the rice planting layout, carry out scientific planting planning, and judge the areas prone to breeding of diseases and insect pests according to the model, and take preventive measures in advance.

[0126] Embodiment 7, see Figure 2 , this embodiment is based on the above embodiment, and the rice planting environment monitoring system based on drone technology provided by the present invention includes a field survey module, an autonomous planning monitoring route module, a monitoring data collection and processing module, a rice field three-dimensional model construction module and a rice field growth environment optimization module;

[0127] The field survey module specifically determines the rice field monitoring area and enters it into the drone monitoring system;

[0128] The autonomous planning monitoring route module specifically calculates the target value to determine the drone action, forms the basis of the rice field monitoring route, sets a blocking mechanism to encourage the drone to explore unexplored areas to form a better route, and monitors the blind spots on the edge of the rice field and calculates risk assessment indicators by judging the navigation status of the drone and randomly removing nearby blocking areas when trapped;

[0129] The monitoring data acquisition and processing module is specifically a drone that monitors according to the planned route, obtains rice field image data and stores it;

[0130] The module for constructing a three-dimensional model of a rice field specifically uses a rice field image taken by a multi-camera to find 2D plane points, selects seed points through Harris response values, increases the area of ​​the seed points and extracts the initial plane according to different feature information in the rice field image, reconstructs the rice field based on grammatical rule segmentation, designs highly compatible mobile nodes to adapt to the actual environment and integrates a sensor network, and provides a richer and more accurate data basis for three-dimensional reconstruction of rice fields;

[0131] The module for optimizing the rice field growth environment specifically adjusts the rice planting layout according to the three-dimensional model of the rice field.

[0132] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0133] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

[0134] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A rice planting environment monitoring method based on drone technology, characterized in that: The method comprises the following steps: Step S1: field survey, specifically determining the rice field monitoring area and entering it into the drone monitoring system; Step S2: Autonomously plan the monitoring route, specifically, calculate the target value to determine the drone action, form the basis of the rice field monitoring route, set up a blocking mechanism to encourage the drone to explore the unexplored area to form a better route, and monitor the blind area at the edge of the rice field and calculate the risk assessment index by judging the navigation status of the drone and randomly removing the nearby blocking area when trapped; Step S3: monitoring data collection and processing, specifically the drone monitors according to the planned route, obtains rice field image data and stores it; Step S4: construct a three-dimensional model of the rice field, specifically, find 2D plane points using the rice field images taken by the multi-camera, select seed points through the Harris response value, increase the seed point area and extract the initial plane according to different feature information in the rice field image, reconstruct the rice field based on grammatical rule segmentation, design highly compatible mobile nodes to adapt to the actual environment and integrate the sensor network, and provide a data basis for the three-dimensional reconstruction of the rice field; Step S5: Optimizing the rice field growth environment, specifically adjusting the rice planting layout according to the three-dimensional model of the rice field.

2. The method for monitoring rice planting environment based on drone technology according to claim 1, characterized in that: In step S2, the autonomously planned monitoring route includes the following steps: Step S21: Calculate the target value for selecting the action for the drone. The formula used is as follows: ； In the formula, y represents the target value, represents the reward obtained at time t+1, γ represents the discount factor, Indicates the state of the drone at the next moment. represents the parameters of the Q network at time t, represents the parameters of the target network, represents the corresponding drone action that maximizes the Q function; Step S22: Setting a blocking mechanism, which is used to mark the explored rice field monitoring area. When the drone selects an action according to the target value, the blocking mechanism will restrict the drone from entering the explored area, prompting the drone to explore the unexplored area and form a better route; The judgment function is used to determine whether the drone can navigate normally. The judgment function is set and the formula used is as follows: ； In the formula, Represents the judgment function. When the judgment function value is 1, it means that the drone is in a normal navigation state; when the judgment function value is -1, it means that the drone is stuck in the current state. u represents the index of the rice field monitoring area, and N represents the set of blocked areas. Step S23: Monitoring the blind spot at the edge of the paddy field. When the UAV reaches the edge area and enters the visual blind spot, the edge blind spot risk assessment index is calculated. The formula used is as follows: ； In the formula, B represents the edge blind area risk assessment index, δ represents the adjustment coefficient, represents the visual visibility probability in the edge area, D represents the distance from the drone to the edge of the rice field, and M represents the density of obstacles in the blind area; Step S24: The monitoring route is formed. According to the selection of actions and the risk assessment of the blind spot at the edge of the rice field, continuous exploration and adjustment are carried out to form an autonomously planned monitoring route to avoid wandering in place.

3. The rice planting environment monitoring method based on drone technology according to claim 1 is characterized by: In step S4, the construction of the three-dimensional model of the paddy field includes the following steps: Step S41: Find plane points according to the point cloud data, use the rice field image taken by the multi-camera, calculate the depth information of each pixel in the image through the stereo matching algorithm, generate the corresponding 3D point cloud data, classify according to the dimensional features and find the corresponding 2D plane points; Step S42: extracting the initial plane of the rice field, selecting seed points in the 2D plane points by Harris response values, using the information in the rice field image to determine the dimensional characteristics, point surface distance and growth angle information to increase the area of ​​the seed points, and stopping the growth when the curvature of the seed point is greater than a preset threshold, and extracting the initial plane of the rice field, including the following steps: Step S421: Select the seed point, perform the first judgment step, and calculate the Harris response value. The formula used is as follows: ； ； In the formula, Num represents the number of adjacent nodes, M represents the covariance matrix, and i represents the index of the adjacent node. represents the coordinate value of the i-th adjacent node on the x-axis, represents the coordinate value of the i-th adjacent node on the y-axis, represents the coordinate value of the i-th adjacent node on the z-axis, R represents the Harris response value, det(M) represents the determinant of the covariance matrix, tr() represents the trace of the covariance matrix, when the Harris response value is greater than or equal to the preset response threshold, it is judged as a seed point for preliminary prediction; when the Harris response value is less than the preset response threshold, it is judged as a non-seed point; The next judgment step is to use the Delaunay triangulation method and the α-shape algorithm to determine whether the initially predicted seed point is on the boundary. When the point is not on the boundary, the initially predicted seed point is classified as a seed point; when the point is on the boundary, the initially predicted seed point is classified as a non-seed point. Step S422: Calculate the area growth process of the seed point, the formula used is as follows: ； ； In the formula, It represents the value after the area of ​​the seed point increases at time t+1. represents the value of the seed point area at time t, k represents the growth coefficient, represents the rice field image information at time t, LGT represents the dimensional feature, which is used to determine the basic unit and proportion of the seed point area growth, k1 represents the weight of the dimensional feature in the rice field image information, d represents the point surface distance, which is used to control the range and boundary of the seed point area growth according to the distance from other points, k2 represents the weight of the point surface distance in the rice field image information, ξ represents the growth angle information, which is used to guide the extension angle of the seed point area growth, and k3 represents the weight of the growth angle information in the rice field image information; Step S43: Reconstructing the paddy field based on grammatical rule segmentation, using actual point cloud data to segment the paddy field area, and dividing the initial paddy field plane into different paddy field blocks, and then defining grammatical rules to describe and segment these paddy field blocks, thereby realizing extraction and reconstruction of various areas of the paddy field; Step S44: Design highly compatible mobile nodes. Considering that parameters in the actual environment change relatively stably, design highly compatible mobile nodes to dynamically integrate the sensor network in the actual rice field environment, solve the data loss problem caused by failed nodes, and provide a richer and more accurate data basis for the three-dimensional reconstruction of rice fields.

4. The method for monitoring rice planting environment based on drone technology according to claim 1, characterized in that: In step S1, the field survey includes the following steps: Step S11: Determine the monitoring target and scope, clarify the size of the rice planting area to be monitored, conduct on-site field investigations, understand the topography and surrounding environment, and evaluate the safety and feasibility of UAV flight; Step S12: Based on the survey results, the boundaries and scope of the monitoring area are preliminarily planned on the map, and the final rice field monitoring area information is entered into the UAV monitoring system to prepare for subsequent flight monitoring.

5. The method for monitoring rice planting environment based on drone technology according to claim 1, characterized in that: In step S3, the monitoring data collection and processing includes the following steps: Step S31: the drone monitors the rice field according to the autonomously planned monitoring route, and uses a multi-camera system to photograph the rice field to obtain image data of the rice field; Step S32: denoising, enhancing contrast, and adjusting brightness of the collected image data to improve image quality; Step S33: extracting features from the rice field image; Step S34: target recognition, identifying the rice field area and rice plants according to the extracted features, and marking them; Step S35: storing the processed image data.

6. The method for monitoring rice planting environment based on drone technology according to claim 1, characterized in that: In step S5, the optimization of the rice field growth environment is specifically to analyze the undulations and slopes in the terrain according to the three-dimensional reconstruction model of the rice field, improve the irrigation and drainage conditions, adjust the rice planting layout, carry out scientific planting planning, and judge the areas prone to breeding of diseases and pests according to the model, and take preventive measures in advance.

7. A rice planting environment monitoring system based on drone technology, used to implement a rice planting environment monitoring method based on drone technology as claimed in any one of claims 1 to 6, characterized in that: It includes a field survey module, an autonomous planning monitoring route module, a monitoring data collection and processing module, a rice field three-dimensional model building module and a rice field growth environment optimization module.

8. The rice planting environment monitoring system based on drone technology according to claim 7 is characterized by: The field survey module specifically determines the rice field monitoring area and enters it into the drone monitoring system; The autonomous planning monitoring route module specifically calculates the target value to determine the drone action, forms the basis of the rice field monitoring route, sets a blocking mechanism to encourage the drone to explore unexplored areas to form a better route, and monitors the blind spots on the edge of the rice field and calculates risk assessment indicators by judging the navigation status of the drone and randomly removing nearby blocking areas when trapped; The monitoring data acquisition and processing module is specifically a drone that monitors according to the planned route, obtains rice field image data and stores it; The module for constructing a three-dimensional model of a rice field specifically uses a rice field image taken by a multi-camera to find 2D plane points, selects seed points through Harris response values, increases the area of ​​the seed points and extracts the initial plane according to different feature information in the rice field image, reconstructs the rice field based on grammatical rule segmentation, designs highly compatible mobile nodes to adapt to the actual environment and integrates a sensor network, and provides a data basis for three-dimensional reconstruction of the rice field; The module for optimizing the rice field growth environment specifically adjusts the rice planting layout according to the three-dimensional model of the rice field.

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