An intelligent spraying path planning system and method

Abstract

The present invention discloses an intelligent spraying path planning system and method, which relates to the technical field of spraying path planning. The system is composed of several functional modules, and each functional module runs in sequence, namely, the information acquisition module, the data analysis and processing module, the path planning module, the spraying adjustment module, and the evaluation and adjustment module. The technical key points are as follows: The system integrates functions of information acquisition, path planning, real-time recognition, and dynamic adjustment, realizes the full intelligence of orchard spraying operations, improves the accuracy and efficiency of spraying operations, ensures the high efficiency and accuracy of spraying operations by automatically adjusting the estimated spraying flow rate and the number of nozzles opened for each nozzle, and at the same time enables the fruit trees to be fully covered by the drug. By precisely controlling the amount of drug sprayed and the spraying flow rate, the excessive use and dispersion of the drug are reduced, and the environmental pollution is lowered.

Description

Technical Field

[0001] The present invention relates to the technical field of spraying path planning, and particularly to an intelligent spraying path planning system and method. Background Art

[0002] Spraying path planning refers to the process of pre-planning the driving path of spraying equipment (such as drones, intelligent mobile spraying robots, etc.) in agricultural operations to efficiently and accurately complete the spraying task. This process is mainly based on the characteristics of the operation area, the performance of the spraying equipment, and the operation requirements, and determines the optimal driving path through algorithms and technical means; there is a close relationship between spraying path planning and agricultural technology extension services. As an important technology for intelligent agricultural operations, the efficient and accurate characteristics of spraying path planning can significantly improve the utilization efficiency of pesticides; agricultural technology extension services are an important means to popularize advanced agricultural technologies to farmers and help them improve agricultural production efficiency; through agricultural technology extension services, farmers can understand the advantages and application methods of spraying path planning technology and apply it in actual production to improve agricultural production efficiency and quality; at the same time, agricultural technology extension services can also continuously optimize and improve spraying path planning technology according to the actual needs and feedback of farmers to make it more adaptable to the actual needs of agricultural production; therefore, spraying path planning belongs to the concrete content of agricultural technology extension services.

[0003] The existing application with the application number 201910169359.4 and the title of "Orchard Intelligent Self-propelled Spraying System and Its Control Method" points out that the system includes a spraying vehicle, a positioning base station, a handheld monitoring terminal, and a driving controller. The spraying vehicle is used to spray pesticides or foliar fertilizers on fruit trees, and the positioning base station is set at the center of the orchard, and the driving controller is set on the spraying vehicle; the driving controller is used to control the automatic driving and spraying operations of the spraying vehicle. The control method of the present invention includes: a). Surveying operation points; b). Path planning; c). Autonomous driving and spraying; d). Automatic drug addition operation; The spraying system and control method of the present invention enable the spraying vehicle to drive along the planned path and perform spraying pesticides or foliar fertilizers operations, avoiding the harm of sprayed agriculture to human health, avoiding heatstroke of personnel, improving spraying efficiency, and reducing labor costs; however, it cannot control the dosage of pesticides and ensure that all fruit trees or plants in the orchard are comprehensively sprayed.

[0004] Combined with the above documents and the prior art, the environment in the existing orchard is complex, and there may be a large number of obstacles such as fruit tree branches and trunks. Some obstacles will affect the moving path of the spraying equipment. Traditional spraying equipment often cannot flexibly avoid obstacles, resulting in poor spraying effects or equipment damage. When traditional spraying pressure equipment is performing spraying operations, it often relies on manual experience or fixed settings to control the spraying amount, and it is difficult to flexibly adjust according to the actual situation of the orchard. If the amount of medicine sprayed is excessive or the spraying range is too large, not only will the medicine be wasted, but it may also pollute the orchard environment and the surrounding ecosystem. If the amount of medicine sprayed is less or the spraying range is too small, the coverage rate of the liquid medicine on the fruit trees will be reduced, and a good insecticidal effect cannot be achieved. At this time, multiple spraying treatments are required, which greatly affects the spraying efficiency. Summary of the Invention

[0005] (1) Technical problems to be solved

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent spraying path planning system and method. The system integrates functions of information collection, path planning, real-time recognition, and dynamic adjustment, realizes the full intelligence of orchard spraying operations, improves the accuracy and efficiency of spraying operations, ensures the high efficiency and accuracy of spraying operations by automatically adjusting the estimated spraying flow rate and the number of nozzles opened for each nozzle, and at the same time enables the fruit trees to be fully covered with medicine. By precisely controlling the spraying amount and spraying flow rate, the excessive use and dispersion of medicine are reduced, the environmental pollution is lowered, and the problems raised in the background art are solved.

[0007] (2) Technical solutions

[0008] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0009] An intelligent spraying path planning system, comprising:

[0010] An information collection module, under the condition of obtaining a two-dimensional map of the target orchard, marking a dividing line on the side of any fruit tree planting area in the target orchard; using image processing technology and edge detection algorithms to obtain the actual pest area; and mapping the actual pest area to the dividing line to generate a standard pest area;

[0011] A data analysis and processing module, collecting the area and vegetation quantity of the actual pest area, synchronously obtaining the area of the standard pest area, and calculating the vegetation density and pest degree value in the actual pest area through analysis and calculation; characterized in that it further comprises:

[0012] The path planning module extracts the coordinate set formed by the corner coordinates of each standard pest area under the condition that the entrance of the target orchard is determined as the origin coordinates, and uses a path planning algorithm to generate a traversal planning path. During the process of the spraying end executing the traversal planning path, a real-time recognition mechanism is executed by using the probe parts equipped on the spraying end, and a dynamic fine-tuning instruction is issued according to the recognition result. The spraying end receives the fine-tuning instruction and changes the traversal planning path in real time.

[0013] The spraying adjustment module, under the condition that the spraying end enters the standard pest area, the nozzle assembly on the spraying end facing the standard pest area is pre-opened, and simultaneously, according to the maximum height of the fruit trees obtained by the spraying end in real time and a pre-built rule engine, the number of nozzles M to be opened is output, and the corresponding nozzle assembly executes the output result. The guiding calculation model is constructed by extracting and based on the number of nozzles M to be opened, the vegetation density and the pest degree value, and the estimated spraying flow rate of a single nozzle is generated.

[0014] The evaluation and adjustment module triggers a sampling survey mechanism after executing a traversal planning path once, compares the obtained average mortality rate with a preset standard threshold, and decides whether to execute a correction and adjustment strategy according to the comparison result.

[0015] Further, the dividing line is the central axis of the established moving path in the target orchard; the image processing technology includes an image processing algorithm and an image segmentation algorithm.

[0016] The process of obtaining the actual pest area is as follows:

[0017] Image preprocessing: Preprocess the two-dimensional map of the target orchard, including grayscale conversion and denoising.

[0018] Pest feature extraction: Use an image processing algorithm to extract the features of the pest area.

[0019] Region segmentation: Based on the extracted features, use an image segmentation algorithm to segment the pest area from the two-dimensional map.

[0020] Boundary determination: Perform boundary detection on the segmented pest area, and use an edge detection algorithm to obtain the boundary of the pest area, that is, the actual pest area.

[0021] Further, image registration technology is used when mapping the actual pest area to the dividing line.

[0022] Further, the analysis and calculation process of the vegetation density in the actual pest area is: divide the number of vegetation in the actual pest area by the area of the actual pest area to obtain the vegetation density in the actual pest area; the analysis and calculation process of the pest degree value in the actual pest area is: divide the area of the actual pest area by the area of the standard pest area to obtain the pest degree value in the actual pest area.

[0023] Further, the process of generating the traversal planning path is as follows:

[0024] Digitize coordinate information: Convert each coordinate in the coordinate set into a digital form;

[0025] Construct a map marker: Based on the two-dimensional map of the target orchard, mark the standard pest areas, fruit tree planting areas, and the entrance of the target orchard on the two-dimensional map;

[0026] Path planning algorithm: Use the TSP algorithm to solve the shortest path.

[0027] Further, the spraying robot walks according to the traversal planning path. The probe components equipped on the spraying end include probe A and probe B, and probe A and probe B are symmetrically installed at both sides of the front end of the spraying end;

[0028] The process of executing the real-time recognition mechanism is as follows:

[0029] The probe components continuously monitor whether there are obstacles in the advancing direction of the spraying end;

[0030] If one group of probes detects the existence of an obstacle, the recognition result is: the corresponding group of probes detects an obstacle;

[0031] If both groups of probes detect the existence of an obstacle, the recognition result is: both groups of probes detect an obstacle;

[0032] If no probe detects the existence of an obstacle, the recognition result is: no obstacle is detected;

[0033] When issuing a fine-tuning instruction based on the recognition result, if the recognition result is: the corresponding group of probes detects an obstacle;

[0034] Then the fine-tuning instruction is: the spraying end deflects towards the side away from the detected obstacle until neither group of probes detects an obstacle; if during the deflection process, both groups of probes detect an obstacle, then run the comparison strategy: identify the maximum diameter among the obstacles detected by probe A and probe B, and compare the maximum diameters of the two groups of obstacles; if the diameter detected by probe A exceeds the maximum diameter detected by probe B, then the spraying end deflects towards the side of probe B until probe A does not detect an obstacle; otherwise, the spraying end deflects towards the side of probe A until probe B does not detect an obstacle;

[0035] If the recognition result is: both groups of probes detect fruit tree branches;

[0036] Then the content of the fine-tuning instruction is: run the comparison strategy;

[0037] If the recognition result is: no fruit tree branches are detected;

[0038] Then the content of the fine-tuning instruction is: do not make a response action.

[0039] Further, the number of the nozzle assemblies is two groups, both of which are arranged at the head of the spraying pressure end, and the two groups of nozzle assemblies are symmetrically distributed and are both quarter-circular structures. A plurality of nozzles are evenly arranged on the arc surface of the nozzle assembly.

[0040] Further, when the rule engine is run to output the number M of opened nozzles, the formula based thereon is as follows:

[0041]

[0042] In the formula, H represents the maximum height of the fruit tree, H max and H min respectively represent the highest height and the lowest height of the growth of the corresponding type of fruit tree, and M max represents the total number of nozzles in the nozzle assembly; represents rounding down;

[0043] The process of constructing a guiding calculation model to generate the estimated spraying flow rate of a single nozzle is as follows:

[0044] Perform normalization processing on the extracted vegetation density and pest damage degree values, and normalize the data to the range of [0, 1];

[0045] Then, based on the normalized data and the number M of opened nozzles, establish the following formula:

[0046]

[0047] In the formula, Q represents the estimated spraying flow rate of a single nozzle, D represents the vegetation density, Ir represents the pest damage degree value, and both k1 and k2 are adjustment factors, and the value ranges of both are [0, 1].

[0048] Further, the process of triggering the sampling survey mechanism is as follows: Randomly select several sample points in any pest area, count the pest mortality rate of the pests in each sample point, and calculate the average mortality rate; After comparing the obtained average mortality rate with a preset standard threshold;

[0049] If the comparison result is that the average mortality rate exceeds the standard threshold, no response action is taken;

[0050] If the comparison result is that the average mortality rate does not exceed the standard threshold, the correction adjustment strategy is executed;

[0051] For the correction adjustment strategy, based on the average mortality rate, the standard threshold, and the difference between the two, construct a correction model to calculate the correction value. The process of constructing the correction model is as follows:

[0052]

[0053] Wherein, R represents the correction value, G represents the preset adjustment value, and Bz represents the standard threshold value. represents the average mortality rate, and Δ represents the difference between the average mortality rate and the standard threshold value.

[0054] Add the obtained correction value to the guiding calculation model constructed when traversing the planned path once, so that the correction value changes the originally estimated spraying flow rate of a single nozzle in an accumulative manner; repeatedly execute the operations in the evaluation and adjustment module until the comparison result is that the average mortality rate exceeds the standard threshold value.

[0055] An intelligent spraying path planning method includes the following steps:

[0056] S1. Under the condition of obtaining the two-dimensional map of the target orchard, mark the dividing line on the side of any fruit tree planting area in the target orchard; use image processing technology and edge detection algorithms to obtain the actual pest area; and map the actual pest area to the dividing line to generate the standard pest area.

[0057] S2. Collect the area and vegetation quantity of the actual pest area, synchronously obtain the area of the standard pest area, and calculate the vegetation density and pest degree value in the actual pest area through analysis.

[0058] S3. Under the condition of determining the entrance of the target orchard as the origin coordinate, extract the coordinate set formed by the corner coordinates of the standard pest area, and use the path planning algorithm to generate the traversing planned path; during the process of the spraying end executing the traversing planned path, use the probe component equipped on the spraying end to execute the real-time recognition mechanism, and issue a dynamic fine-tuning instruction according to the recognition result, and the spraying end receives the fine-tuning instruction and changes the traversing planned path in real time.

[0059] S4. When the spraying end enters the standard pest area, the nozzle assembly on the spraying end facing the standard pest area is pre-opened, and at the same time, according to the maximum height of the fruit trees obtained by the spraying end in real time and the pre-built rule engine, the number of nozzles M to be opened is output, and the corresponding nozzle assembly executes the output result; extract and construct a guiding calculation model based on the number of nozzles M to be opened, the vegetation density and the pest degree value, and generate the estimated spraying flow rate of a single nozzle.

[0060] S5. After executing the traversing planned path once, trigger the sampling survey mechanism, compare the obtained average mortality rate with the preset standard threshold value, and decide whether to execute the correction and adjustment strategy according to the comparison result.

[0061] (III) Beneficial effects

[0062] The present invention provides an intelligent spraying path planning system and method, which has the following beneficial effects:

[0063] (1) This solution uses image processing technology and edge detection algorithms to accurately identify the pest areas in the orchard and map them to the preset dividing lines to generate standard pest areas, thereby improving the accuracy of pest identification and positioning precision and providing a reliable data basis for subsequent processing. By collecting the area of ​​the actual pest area, the number of vegetation, and the area of ​​the standard pest area, the system can analyze and calculate the vegetation density and pest severity value in the actual pest area, which provides orchard managers with quantitative pest assessment indicators and helps to formulate more scientific and reasonable pest control strategies.

[0064] (2) This solution uses a path planning algorithm, combined with a set of corner coordinates of a standard pest area, to quickly generate the shortest path from the orchard entrance through all pest areas and back to the entrance. In the process of executing the traversal planning path, obstacles such as branches of fruit trees in the forward direction are detected through a real-time recognition mechanism, and dynamic fine-tuning instructions are issued based on the recognition results. This enables the spraying robot to flexibly respond to the complex environment in the orchard, avoid collisions with obstacles to the greatest extent possible, and ensure that the pest area is fully sprayed, preliminarily guaranteeing the coverage rate. The system integrates information collection, path planning, real-time recognition, and dynamic adjustment functions, realizing the comprehensive intelligence of orchard spraying operations, which not only improves the accuracy and efficiency of spraying operations, but also reduces labor costs and labor intensity.

[0065] (3) This solution dynamically adjusts the number of nozzles opened according to the maximum height of the fruit trees through the rule engine, realizing intelligent adjustment of the spraying amount, which not only ensures that the fruit trees are fully covered by the medicine, but also avoids unnecessary waste of medicine. By constructing a guidance calculation model, combined with the vegetation density and pest severity value, the estimated spraying flow rate of each nozzle is accurately calculated, ensuring the efficiency and accuracy of the spraying operation, and to a certain extent, maximizing the efficiency of drug use. By accurately controlling the spraying amount and spraying flow rate, the excessive use and drift of drugs are reduced, reducing pollution to the environment.

[0066] (4) This scheme dynamically adjusts the spraying strategy based on the comparison results between the average mortality rate and the preset standard threshold, ensuring that the spraying operation can be flexibly optimized according to the actual effect, thereby improving the efficiency and accuracy of the spraying. In conjunction with the constructed correction model, the correction value is calculated and added to the guidance calculation model, thereby achieving precise control of the estimated spray flow rate of a single nozzle, further avoiding drug waste and excessive use, and ensuring that the fruit trees are fully covered by the drug. The operations in the evaluation and adjustment module are executed cyclically until the average mortality rate exceeds the standard threshold. This continuous optimization mechanism ensures the continuous improvement of the spraying effect and meets the actual needs of orchard management. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1Schematic diagram of the system module of the present invention;

[0068] Figure 2 Schematic diagram of the overall process of the present invention;

[0069] Figure 3 Partial two-dimensional scene map of the target orchard in the present invention;

[0070] Figure 4 Schematic diagram of the application scenario of the probe in the present invention;

[0071] Figure 5 Schematic diagram of the application scenario of the nozzle assembly in the present invention;

[0072] Reference numerals: 1. Nozzle assembly. Detailed implementation manners

[0073] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a 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 those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0074] Embodiment 1:

[0075] Please refer to Figure 1 、 Figures 3 to 5 , this embodiment provides an intelligent spraying path planning system. The application scenario of this system is mostly used in greenhouses for planting fruit trees or fruits and vegetables, and can also be used in the area for cultivating fruit trees according to needs. Most of the modern fruit tree planting and cultivation are arranged in accordance with established areas, which is convenient for subsequent observation, spraying, and picking work;

[0076] This system consists of several functional modules, and each functional module runs in sequence, namely the information collection module, the data analysis and processing module, the path planning module, the spraying adjustment module, and the evaluation and adjustment module;

[0077] Now, the above-mentioned each functional module will be described and elaborated in sequence:

[0078] Information collection module:

[0079] Under the condition of obtaining the two-dimensional map of the target orchard, mark the dividing line on the side of any fruit tree planting area in the target orchard; use image processing technology and edge detection algorithm to obtain the actual pest area; and map the actual pest area to the dividing line to generate the standard pest area; wherein, the dividing line is the central axis of the established moving path in the target orchard;

[0080] The two-dimensional map of the target orchard is a plan view, and the acquisition methods include drone aerial photography and satellite image interception;

[0081] Drone aerial photography:

[0082] Use a drone equipped with a high-resolution camera to conduct aerial photography of the orchard to obtain a bird's-eye view image of the orchard; this method has flexibility and high efficiency and can quickly cover the entire orchard area; import the orchard images taken by the drone into professional image processing software (such as DJI Terra), and generate a two-dimensional plane of the orchard through processing steps such as image stitching and correction; this process may involve the collation and confirmation of images to ensure that the images are clear, without overexposure, and without missing parts, etc.

[0083] Satellite image interception:

[0084] Utilize existing satellite image resources, such as Google Earth, Baidu Maps, etc. These platforms provide rich geographical information, including satellite images of the orchard area; locate the target orchard area on the satellite image platform, use the image interception tool to intercept the image of the orchard part, and perform appropriate cropping and adjustment to obtain a clear two-dimensional map of the orchard.

[0085] In this embodiment, drone aerial photography can be selected to obtain and store the two-dimensional map of the target orchard in advance.

[0086] Refer to Figure 3 As shown, all fruit trees in the target orchard need to be planned in advance before planting. For the selection of fruit tree planting areas, they are arranged in several longitudinal patterns, and the areas between adjacent fruit tree planting areas are for subsequent pesticide spraying ends or staff to enter. The spacing between adjacent fruit tree planting areas is kept consistent for easy management.

[0087] For the dividing line, this dividing line is a virtual route and can be displayed in the two-dimensional map of the target orchard and serves as part of the traversal planning path of the pesticide spraying end; the central axis of the established moving path in the target orchard indicates that the path formed between adjacent fruit tree planting areas has a certain width, and the center line of this path is the central axis that divides the two symmetrical path areas; for the dividing lines at the edges of the fruit tree planting areas, although they are not adjacent to other fruit tree planting areas, their path specifications are the same, so the dividing lines in the same target orchard always maintain a uniform distribution.

[0088] Image processing technology includes image processing algorithms and image segmentation algorithms.

[0089] The process of obtaining the actual pest area is as follows:

[0090] Image preprocessing: Preprocess the two-dimensional map of the target orchard, including grayscale conversion, denoising, and contrast enhancement, etc., to improve the image quality for subsequent processing; Pest feature extraction: Use image processing algorithms (including color space conversion and texture analysis) to extract the features of the pest areas; For example, the pest areas show different colors (including withered yellow and brown) or textures (including damaged leaves and spots) from normal vegetation; Region segmentation: Based on the extracted features, use image segmentation algorithms (including threshold segmentation, region growing, and clustering segmentation) to segment the pest areas from the two-dimensional map, which may require multiple iterations and parameter adjustments to obtain accurate segmentation results; Boundary determination: Perform boundary detection on the segmented pest areas using edge detection algorithms (including Canny, Sobel) to obtain the accurate boundaries of the pest areas, that is, the actual pest areas;

[0091] When mapping the actual pest areas to the dividing line, image registration technology is adopted;

[0092] Align the coordinates of the actual pest areas with the standard coordinates on the dividing line by calculating the coordinates of the centroid or bounding box of the pest areas and converting them into the corresponding coordinates on the dividing line; Refer to Figure 3 It can be seen the ranges of the actual pest areas and their corresponding standard pest areas in actual detection;

[0093] The shaded part is the actual pest area;

[0094] The rectangular part formed by taking the dividing line as the side and having four corner coordinates is the standard pest area.

[0095] Data processing and analysis module:

[0096] Collect the area and vegetation quantity of the actual pest areas, synchronously obtain the area of the standard pest areas, and through analysis and calculation, obtain the vegetation density and pest degree values within the actual pest areas;

[0097] Among them, the area of the actual pest areas is collected using measuring tools (such as GPS, tape measures, etc.) or geographic information system (GIS) software for accurate measurement. In this embodiment, to ensure automated measurement, GIS software is used for measurement and collection. The vegetation quantity is the vegetation quantity within the actual pest areas, which can be directly counted and obtained from the two-dimensional map of the target orchard; For the area of the standard pest areas, it can be obtained based on the area of the actual pest areas in combination with GIS software;

[0098] The analysis and calculation process of the vegetation density in the actual pest area is as follows: divide the number of vegetation in the actual pest area by the area of the actual pest area, and the result obtained is the vegetation density in the actual pest area; the pest degree value in the actual pest area is used to quantify the impact degree of pests on fruit trees, which is represented by the pest area ratio. Then, the analysis and calculation process of the pest degree value in the actual pest area is: divide the area of the actual pest area by the area of the standard pest area, and the result obtained is the pest degree value in the actual pest area.

[0099] Specifically, by using image processing technology and edge detection algorithms, the pest areas in the orchard can be accurately identified and mapped onto the preset dividing lines to generate standard pest areas, which improves the accuracy of pest identification and the positioning precision, providing a reliable data basis for subsequent processing; by collecting the area of the actual pest area, the number of vegetation, and the area of the standard pest area, the system can analyze and calculate the vegetation density and pest degree value in the actual pest area, which provides a quantitative pest assessment index for orchard managers and helps to formulate more scientific and reasonable pest control strategies.

[0100] Path planning module:

[0101] Under the condition that the entrance of the target orchard is determined as the origin coordinates, extract the coordinate set formed by the corner coordinates of the standard pest area, and use the path planning algorithm to generate a traversal planning path; during the process of the spraying end executing the traversal planning path, use the probe parts equipped on the spraying end to execute a real-time recognition mechanism, and issue dynamic fine-tuning instructions according to the recognition results. The spraying end receives the fine-tuning instructions and changes the traversal planning path in real time;

[0102] Among them, referring to Figure 3 As shown, the standard pest area is a rectangle, and the four right-angled sides of the rectangle are the corners. Therefore, there are four groups of corner coordinates for one standard pest area, and eight groups of corner coordinates for two standard pest areas. Figure 3 These eight groups of corners in

[0103] The path planning algorithm adopts the Traveling Salesman Problem (TSP) algorithm or its variants, such as genetic algorithms and ant colony algorithms;

[0104] The process of generating the traversal planning path is as follows:

[0105] Digitization of coordinate information: The coordinates in the image coordinate set (in Figure 3Taking it as an example, the coordinates in the coordinate set, including Coordinate 1 to Coordinate 8, are converted into digital form for easy computer processing, and the entrance of the target orchard is determined as the starting point (i.e., the origin coordinates); Map marking: Based on the two-dimensional map of the target orchard, mark the standard pest areas, fruit tree planting areas, and the entrance of the target orchard on the two-dimensional map; Path planning algorithm: Use the Traveling Salesman Problem (TSP) algorithm or its variants, such as genetic algorithms, ant colony algorithms, etc., to solve the shortest path problem. The input is the origin coordinates and the coordinate set, and the output is the shortest path starting from the entrance of the target orchard, traversing each coordinate in the coordinate set, and then returning to the entrance of the target orchard;

[0106] For example:

[0107] Coordinate 1 can be expressed as (x1, y1), Coordinate 2 as (x2, y2), and so on. The coordinates of the entrance of the target orchard can be set as (0, 0) or determined according to actual needs; On the two-dimensional plane, mark the standard pest areas, fruit tree planting areas, and the entrance of the target orchard with different colors or symbols to ensure that the map is consistent with the actual orchard layout; Select a suitable algorithm for solving; For example, the genetic algorithm searches for the optimal solution by simulating natural selection and genetic mechanisms; The ant colony algorithm finds the shortest path by simulating the foraging behavior of ants; The specific implementation of the algorithm needs to be adjusted according to the orchard scale and the number of coordinate points.

[0108] Refer to Figure 3 As shown, the spraying end uses a spray robot that can move autonomously;

[0109] The spray robot walks along the traversed planned path. The probe components equipped on the spraying end include two groups of probes A and B. Probes A and B are respectively installed at the positions on both sides of the front end of the spraying end (not marked in the figure). The probe components are used to detect whether there are redundant fruit tree branches (i.e., obstacles) in the advancing direction of the spraying end and the maximum diameter of the fruit tree branches (i.e., the maximum width);

[0110] The process of executing the real-time recognition mechanism is as follows:

[0111] The probe components continuously monitor whether there are redundant fruit tree branches in the advancing direction of the spraying end;

[0112] If one group of probes detects the existence of fruit tree branches, the recognition result is: the corresponding group of probes detects the existence of fruit tree branches;

[0113] If both groups of probes detect the existence of fruit tree branches, the recognition result is: both groups of probes detect the existence of fruit tree branches;

[0114] If no probe detects the existence of fruit tree branches, the recognition result is: no fruit tree branches are detected;

[0115] When issuing a dynamic fine-tuning instruction based on the recognition result, if the recognition result is that the fruit tree branches are detected by the corresponding group of probes;

[0116] Then the content of the fine-tuning instruction is: the spraying end deflects towards the side away from the detected fruit tree branches until neither of the two groups of probes detects the fruit tree branches; if during the deflection process, both groups of probes detect the fruit tree branches, then run the comparison strategy: identify the maximum branch width among the fruit tree branches detected by probe A and probe B, and compare the maximum branch widths of the two groups; if the corresponding branch width maximum of probe A exceeds that of probe B, then the spraying end deflects towards the side of probe B until probe A no longer detects the fruit tree branches; if the corresponding branch width maximum of probe B exceeds that of probe A, then the spraying end deflects towards the side of probe A until probe B no longer detects the fruit tree branches;

[0117] It should be noted that in the actual application process, the probes configured on both sides of the front end of the spraying end respectively monitor both sides of the front edge of the spraying end. Refer to Figure 4 As can be seen, for the specific positions of probe A and probe B, the structures of probe A and B are the same, and their functions and principles are the same. Therefore, taking probe A as an example, the ultrasonic probe part in probe A is realized by pre-adjusting its emission angle and reception sensitivity. Refer to Figure 4 As shown in , to meet the requirement of detecting objects within a range of 5 meters in front and a maximum width of 2 meters, and then using the camera probe part in probe A when needed to detect and analyze the maximum branch width of the fruit tree branches. Therefore, probe A can realize the functions of detecting obstacles and judging the size of obstacles;

[0118] The technical principle for any probe to detect the maximum branch width of the fruit tree branches is:

[0119] The camera probe part analyzes the maximum diameter of the obstacle among the obstacles existing in the front, which is mainly realized through image processing technology; first, the camera probe part captures the front image and identifies the contour of the obstacle through an edge detection algorithm; then, uses feature extraction technology to obtain the key feature points of the obstacle, such as corner points, intersection points, etc.; then, by calculating the distances between these feature points, the size of the obstacle, including its maximum diameter, can be estimated.

[0120] If the recognition result is that both groups of probes detect the fruit tree branches;

[0121] Then the content of the fine-tuning instruction is: run the comparison strategy;

[0122] If the recognition result is that no fruit tree branches are detected;

[0123] Then the content of the fine-tuning instruction is: do not make a response action.

[0124] Specifically, by using a path planning algorithm and combining it with the corner coordinate set of the standard pest area, the shortest path that starts from the orchard entrance, traverses all pest areas, and returns to the entrance can be quickly generated. During the process of executing the traversal of the planned path, obstacles such as fruit tree branches in the forward direction are detected through a real-time recognition mechanism, and dynamic fine-tuning instructions are issued based on the recognition results. This enables the spraying robot to flexibly cope with the complex environment in the orchard, avoid collisions with obstacles to the greatest extent, and at the same time ensure that the pest areas are comprehensively sprayed, initially guaranteeing the coverage rate.

[0125] The probe components equipped on the spraying robot can monitor the obstacles ahead in real time and accurately analyze the size of the obstacles through image processing technology. When an obstacle is detected, the robot can perform precise avoidance operations according to the position and size of the obstacle, improving the safety and efficiency of the spraying operation. The system integrates multiple functional modules such as information collection, path planning, real-time recognition, and dynamic adjustment, realizing the full intelligence of orchard spraying operations. This not only improves the accuracy and efficiency of the spraying operation, but also reduces the labor cost and labor intensity.

[0126] Spraying adjustment module:

[0127] When the spraying end enters the standard pest area, the nozzle assembly 1 facing the standard pest area on the spraying end is pre-opened (indicating the state of being ready to open but not yet opened). Synchronously, based on the maximum height of the fruit trees obtained in real time by the spraying end and the pre-built rule engine, the number of nozzles to be opened M is output, and the corresponding nozzle assembly 1 executes the output result. Extract and construct a guiding calculation model based on the number of nozzles to be opened M, vegetation density, and pest degree value, and generate the estimated spraying flow rate per single nozzle (i.e., the amount of pesticide sprayed per minute, unit: liters / minute).

[0128] Among them, referring to Figure 5 it can be seen that the number of nozzle assemblies 1 is two groups, both are configured at the head (i.e., the front end) of the spraying robot (i.e., the spraying end), and the two groups of nozzle assemblies 1 are symmetrically distributed, both are quarter-circular structures. A number of nozzles are evenly configured on the arc surface of the nozzle assembly 1 for spraying atomized insecticidal drugs; at the same time Figure 5 also gives the approximate spraying range of each nozzle during operation; M is a positive integer, Figure 5 as shown in

[0129] The maximum height of the fruit trees obtained in real time by the spraying end is measured by the ultrasonic sensor configured on the spraying end (referring to Figure 5 it can be clearly seen the position of the ultrasonic sensor, and the ultrasonic sensor is connected to the vertex of the fruit tree), by emitting ultrasonic waves to the fruit tree and receiving the reflected signals to generate the maximum height of the fruit tree.

[0130] When running the rules engine to output the number of nozzles M to be opened, the formula is as follows:

[0131]

[0132] In the formula, H represents the maximum height of the fruit tree, H max 、H min respectively represent the highest height and the lowest height of the growth of fruit trees of the corresponding type. For example: for type X fruit trees, the highest growth height is 0 to 10 meters, then H max = 10, H min = 0, M max represents the total number of nozzles in the nozzle assembly 1; represents rounding down;

[0133] Logical explanation: In the proportional calculation in the formula, when the lowest height takes the value of 0, the reciprocal of the ratio of the maximum height H of the fruit tree to the highest height is calculated. When the maximum height H of the fruit tree approaches 0, this ratio will become very large; when the maximum height H of the fruit tree approaches the highest height, this ratio approaches 1; the lowest height is used as a scaling factor to magnify the ratio in order to distribute the value of M in a wider range; +1 is to ensure that the result is at least 1 to avoid no nozzles working;

[0134] The process of constructing a guidance calculation model to generate the estimated spraying flow rate of a single nozzle is as follows:

[0135] Normalize the extracted vegetation density and pest degree values so that the normalized data is within the range of [0, 1];

[0136] Then, based on the normalized data and the number of nozzles M opened, establish the following formula:

[0137]

[0138] In the formula, Q represents the estimated spraying flow rate of a single nozzle, D represents the vegetation density, Ir represents the pest degree value, and k1 and k2 are both adjustment factors used to adjust the influence degree of the vegetation density and the pest degree on the spraying flow rate, and their value ranges are both [0, 1];

[0139] Logical explanation: The number of nozzles M opened is used as part of the basic flow rate and is directly multiplied by the result of the subsequent non-linear function, introduced in the form of ln(1 + D * e k1*Ir ) When the vegetation density increases, the spraying flow rate will also increase, but the increasing speed will gradually slow down as the density increases, avoiding waste caused by a large amount of liquid medicine being unable to enter the deep layer area; combined with the vegetation density, through the exponential function e k1*IrTo amplify or reduce the impact of vegetation density on flow rate, the higher the degree of pest damage, the faster the spraying flow rate increases; k1 adjusts the sensitivity of the impact of pest damage degree on the flow rate affected by vegetation density; k2 is used to adjust the reduction degree of spraying flow rate when the vegetation density is low or the pest damage degree is low; 1 + k2*(1 - D)*(1 - Ir) is used to ensure that when the vegetation density or pest damage degree is very low, the spraying flow rate will not increase infinitely but has an upper limit;

[0140] Overall description:

[0141] The number of nozzle openings M = 3;

[0142] Vegetation density D = 0.6 (assuming it has been normalized);

[0143] Pest damage degree value Ir = 0.8 (assuming it has been normalized);

[0144] Adjustment factors k1 = 0.5, k2 = 0.3;

[0145] Substitute into the formula for calculation: Q = (3 * ln(1 + 0.6 * 1.4818)) / (1 + 0.024);

[0146] Q = (3 * ln(1.8951) / 1.024) ≈ 1.89 (rounded to two decimal places) (unit: liters per minute).

[0147] Specifically, the system dynamically adjusts the number of nozzle openings according to the maximum height of the fruit trees through the rule engine, achieving intelligent adjustment of the spraying amount. This not only ensures that the fruit trees are fully covered with medicine but also avoids unnecessary waste of medicine; by constructing a guiding calculation model and combining the vegetation density and pest damage degree value, the estimated spraying flow rate of each nozzle is accurately calculated, which ensures the efficiency and accuracy of the spraying operation and, to a certain extent, maximizes the use efficiency of the medicine;

[0148] This technical solution can be flexibly adjusted according to different orchard environments (such as fruit tree height, vegetation density, pest damage degree, etc.), and has strong adaptability and versatility; by precisely controlling the spraying amount and spraying flow rate, the overuse and dispersion of medicine are reduced, and the use of medicine is minimized to a certain extent, thereby reducing environmental pollution.

[0149] Evaluation and adjustment module:

[0150] After performing a traversal of the planned path, trigger the sampling survey mechanism, compare the obtained average mortality rate with the preset standard threshold, and decide whether to execute the correction and adjustment strategy according to the comparison result;

[0151] Among them, the process of triggering the sampling survey mechanism is as follows:

[0152] Randomly select several sample points within any pest-infested area, count the survival situation of pests in each sample point (i.e., the pest mortality rate corresponding to each sample point), and calculate the average mortality rate;

[0153] The specific numerical calculation process is as follows:

[0154] The pest mortality rate corresponding to each sample point = (the number of pests that have fallen off + the number of dead pests that have not fallen off) / the total number of pests × 100%; for example, if an inspection is carried out 24 hours after spraying in a certain pest-infested area, it is known that there are 100 pests that have fallen off, 50 dead pests that have not fallen off, and the total number of pests in this area is 200, then the pest mortality rate is:

[0155] Pest mortality rate = (100 + 50) / 200 × 100% = 75%;

[0156] Then, average the pest mortality rates of each sample point. If the calculated result is still 75%, it means that the average mortality rate after spraying has reached 75%, which can be used as a specific value to evaluate the spraying effect; the higher the mortality rate, the better the spraying effect.

[0157] If the comparison result is that the average mortality rate exceeds the standard threshold, no response action is taken;

[0158] If the comparison result is that the average mortality rate does not exceed the standard threshold, the correction and adjustment strategy is executed;

[0159] The content of the correction and adjustment strategy is as follows:

[0160] Based on the average mortality rate, the standard threshold, and the difference between the two, construct a correction model to calculate the correction value. The process of constructing the correction model is as follows:

[0161]

[0162] In the formula, R represents the correction value, G represents the preset adjustment value, usually taking an increment of 0.5 liters per minute as a unit. At the same time, G also serves as a correction coefficient to control the amplitude of the correction value, Bz represents the standard threshold, represents the average mortality rate;

[0163] Logical explanation: First, calculate the difference Δ between the average mortality rate and the standard threshold. This difference directly reflects the gap between the current mortality rate and the expected mortality rate (i.e., the standard threshold); introduce the correction coefficient G, which is a small positive number used to control the overall amplitude of the correction value. The selection of K should be based on actual applications, usually an increment of one unit of flow rate, to ensure that the adjusted spraying flow rate after correction can effectively adjust the mortality rate without causing waste or excess; directly take the smaller value between the difference and 1 to calculate the correction value to ensure that the correction value will not be too large;

[0164] Add the obtained correction value to the guiding calculation model constructed when traversing the planned path once, so that the correction value changes the originally estimated spraying flow rate of a single nozzle in an accumulative manner. The specific formula is as follows:

[0165]

[0166] Loop and execute the operations in the evaluation and adjustment module until the comparison result is that the average mortality rate exceeds the standard threshold.

[0167] Specifically, according to the comparison result between the average mortality rate obtained from the sampling survey mechanism and the preset standard threshold, dynamically adjust the spraying strategy, which ensures that the spraying operation can be flexibly optimized according to the actual effect, improving the spraying efficiency and accuracy; in cooperation with the constructed correction model, calculate the correction value and add it to the guiding calculation model, realizing precise control of the originally estimated spraying flow rate of a single nozzle, further achieving the effect of avoiding drug waste and overuse, while ensuring that the fruit trees are fully covered with drugs. Loop and execute the operations in the evaluation and adjustment module until the average mortality rate exceeds the standard threshold. This continuous optimization mechanism ensures the continuous improvement of the spraying effect and meets the actual needs of orchard management.

[0168] Embodiment 2:

[0169] Please refer to Figure 2 , based on Embodiment 1, this embodiment also provides an intelligent spraying path planning method, including the following specific steps:

[0170] S1. Under the condition of obtaining the two-dimensional map of the target orchard, mark a dividing line on the side of any fruit tree planting area in the target orchard; use image processing technology and edge detection algorithms to obtain the actual pest area; and map the actual pest area to the dividing line to generate a standard pest area;

[0171] S2. Collect the area and vegetation quantity of the actual pest area, and simultaneously obtain the area of the standard pest area. Through analysis and calculation, obtain the vegetation density and pest degree value in the actual pest area;

[0172] S3. Under the condition of determining the entrance of the target orchard as the origin coordinate, extract the coordinate set formed by the coordinates of each corner of the standard pest area, and use a path planning algorithm to generate a traversing planned path; during the process of the spraying end executing the traversing planned path, use the probe parts equipped on the spraying end to execute a real-time recognition mechanism, and issue a dynamic fine-tuning instruction according to the recognition result. The spraying end receives the fine-tuning instruction and changes the traversing planned path in real time;

[0173] S4. Under the condition that the spraying end enters the standard pest area, the nozzle assembly 1 on the spraying end facing the standard pest area is pre-opened. Synchronously, according to the maximum height of the fruit tree obtained in real time by the spraying end and the pre-built rule engine, the number of nozzles to be opened M is output, and the corresponding nozzle assembly 1 executes the output result; extract and construct a guiding calculation model based on the number of nozzles to be opened M, vegetation density, and pest degree value to generate the estimated spraying flow rate of a single nozzle.

[0174] S5. After executing a traversal of the planned path once, trigger a sampling survey mechanism, compare the obtained average mortality rate with a preset standard threshold, and decide whether to execute a correction and adjustment strategy according to the comparison result.

[0175] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution.

[0176] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0177] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. An intelligent spraying path planning system, the system comprising: The information collection module, under the condition of obtaining a two-dimensional map of the target orchard, marks a dividing line on the side of any fruit tree planting area in the target orchard; Using image processing technology and edge detection algorithm, the actual pest area is obtained; and the actual pest area is mapped onto the dividing line to generate a standard pest area; The data analysis and processing module collects the area and number of vegetation in the actual pest area, simultaneously obtains the area of ​​the standard pest area, and obtains the vegetation density and pest severity value in the actual pest area through analysis and calculation; it is characterized by also including: The path planning module extracts the coordinate set formed by the coordinates of each corner of the standard pest area under the condition that the target orchard entrance is determined as the origin coordinate, and uses the path planning algorithm to generate a traversal planning path; in the process of executing the traversal planning path, the spraying end uses the probe part on the spraying end to perform a real-time recognition mechanism, and issues a dynamic fine-tuning instruction based on the recognition result. The spraying end receives the fine-tuning instruction and changes the traversal planning path in real time; The spraying adjustment module, when the spraying end enters the standard pest area, the spray head assembly (1) on the spraying end facing the standard pest area is pre-opened, and the number of nozzle openings M is outputted synchronously based on the maximum height of the fruit tree obtained in real time by the spraying end and the pre-built rule engine, and the corresponding spray head assembly (1) executes the output result; extracts and constructs a guidance calculation model based on the number of nozzle openings M, vegetation density and pest severity value, and generates an estimated spraying flow rate of a single nozzle; When running the rule engine to output the number of nozzle openings M, the formula used is as follows: ； In the formula, H represents the maximum height of the fruit tree, , They represent the highest and lowest heights of the corresponding types of fruit trees. Indicates the total number of nozzles in the nozzle assembly (1); Indicates rounding down; The process of building a guidance calculation model to generate an estimated spray flow rate for a single sprinkler is as follows: The extracted vegetation density and pest severity values ​​are normalized to the interval [0, 1]; Then, based on the normalized data and the number of nozzle openings M, the following formula is established: ； In the formula, Q represents the estimated spray flow rate of a single nozzle, D represents the vegetation density, Ir represents the pest severity value, k1 and k2 are adjustment factors, and their value ranges are [0, 1]; The evaluation and adjustment module triggers the sampling survey mechanism after executing a traversal planning path, compares the average mortality rate obtained with the preset standard threshold, and decides whether to implement the correction and adjustment strategy based on the comparison result; The process of triggering the sampling survey mechanism is: randomly select a number of sample points in any pest area, count the pest mortality rate of the pests in each sample point, and calculate the average mortality rate; compare the obtained average mortality rate with the preset standard threshold; If the comparison result is: the average mortality rate exceeds the standard threshold, no response action will be taken; If the comparison result is: the average mortality rate does not exceed the standard threshold, the correction adjustment strategy is implemented; The revised adjustment strategy is to construct a revised model based on the average mortality rate, the standard threshold, and the difference between the two to calculate the revised value. The process of constructing the revised model is as follows: ； In the formula, R represents the correction value, G represents the preset adjustment value, and Bz represents the standard threshold value. represents the average mortality rate, Δ represents the difference between the average mortality rate and the standard threshold; The obtained correction value is added to the guidance calculation model constructed when executing a traversal planning path, so that the correction value changes the original single nozzle estimated spray flow rate by accumulation; the operations in the evaluation and adjustment module are executed cyclically until the comparison result is: the average mortality rate exceeds the standard threshold.

2. The intelligent spraying path planning system according to claim 1, characterized in that: The separation line is the central axis of the predetermined moving path in the target orchard; the image processing technology includes image processing algorithm and image segmentation algorithm; The process of obtaining the actual infestation area is as follows: Image preprocessing: Preprocess the two-dimensional image of the target orchard, including grayscale and denoising; Pest feature extraction: Use image processing algorithms to extract features of pest areas; Region segmentation: Based on the extracted features, the image segmentation algorithm is used to segment the pest area from the two-dimensional image; Boundary determination: Perform boundary detection on the segmented pest area and use edge detection algorithm to obtain the boundary of the pest area, that is, the actual pest area.

3. The intelligent spraying path planning system according to claim 1, characterized in that: Image registration techniques are used to map the actual infestation area onto the divider lines.

4. The intelligent spraying path planning system according to claim 1, characterized in that: The analysis and calculation process of the vegetation density in the actual pest area is as follows: divide the number of vegetation in the actual pest area by the area of ​​the actual pest area to obtain the vegetation density in the actual pest area; the analysis and calculation process of the pest severity value in the actual pest area is as follows: divide the area of ​​the actual pest area by the area of ​​the standard pest area to obtain the pest severity value in the actual pest area.

5. The intelligent spraying path planning system according to claim 1, characterized in that: The process of generating a traversal planning path is as follows: Coordinate information digitization: converting each coordinate in the coordinate set into digital form; Construct map markings: Based on the two-dimensional map of the target orchard, mark the standard pest area, fruit tree planting area and the entrance of the target orchard on the two-dimensional map; Path planning algorithm: TSP algorithm is used to solve the shortest path.

6. The intelligent spraying path planning system according to claim 5, characterized in that: The spraying robot walks according to the traversal planning path. The matching probe parts on the spraying end include probe A and probe B, and probe A and probe B are symmetrically installed at the edges of both sides of the front end of the spraying end; The process of implementing the real-time recognition mechanism is as follows: The probe monitors in real time whether there are obstacles in the direction of the spraying end; If one of the probe groups detects the presence of an obstacle, the recognition result is: the corresponding group of probes detects the obstacle; If both sets of probes detect the existence of obstacles, the recognition result is: both sets of probes detect obstacles; If no probe detects the existence of an obstacle, the recognition result is: no obstacle is detected; When issuing a fine-tuning command based on the recognition result, if the recognition result is: the corresponding group of probes detects an obstacle; Then the fine-tuning instruction is: the spray end is deflected toward the side away from the detected obstacle until both sets of probes do not detect the obstacle; If both sets of probes detect obstacles during the deflection process, the comparison strategy will be run: identify the maximum diameter of the obstacles detected by probe A and probe B, and compare the maximum diameters of the two sets of obstacles; if probe A exceeds the maximum diameter detected by probe B, the spray end will deflect toward the side of probe B until probe A detects no obstacles; On the contrary, the spray end will deflect toward the side of probe A until probe B detects no obstacle; If the identification result is: both sets of probes detect fruit tree branches; Then the content of the fine-tuning instruction is: run the comparison strategy; If the identification result is: no fruit tree branches or trunks are detected; The content of the fine-tuning instruction is: no response action.

7. The intelligent spraying path planning system according to claim 6, characterized in that: The number of the nozzle assemblies (1) is two groups, both of which are arranged at the head of the spraying end, and the two groups of nozzle assemblies (1) are symmetrically distributed, both of which are quarter-circular structures, and a plurality of nozzles are evenly arranged on the arc surface of the nozzle assembly (1).

8. An intelligent spraying path planning method, using any system described in claims 1 to 7, characterized in that: The steps include: S1. Under the condition of obtaining a two-dimensional map of the target orchard, mark a dividing line on the side of any fruit tree planting area in the target orchard; use image processing technology and edge detection algorithm to obtain the actual pest area; and map the actual pest area onto the dividing line to generate a standard pest area; S2. Collect the area and number of vegetation in the actual pest area, and simultaneously obtain the area of ​​the standard pest area, and obtain the vegetation density and pest severity value in the actual pest area through analysis and calculation; S3, under the condition that the entrance of the target orchard is determined as the origin coordinate, extract the coordinate set formed by the coordinates of each corner of the standard pest area, and use the path planning algorithm to generate a traversal planning path; When the spraying end is executing the traversal planning path, the probe part equipped on the spraying end is used to execute the real-time recognition mechanism, and a dynamic fine-tuning instruction is issued according to the recognition result. The spraying end receives the fine-tuning instruction and changes the traversal planning path in real time. S4. When the spraying end enters the standard pest area, the spray head assembly (1) on the spraying end facing the standard pest area is pre-opened, and the number of nozzle openings M is outputted synchronously based on the maximum height of the fruit tree obtained in real time by the spraying end and the pre-built rule engine, and the corresponding spray head assembly (1) executes the output result; extracts and constructs a guidance calculation model based on the number of nozzle openings M, vegetation density and pest severity value, and generates an estimated spray flow rate of a single nozzle; S5. After executing a traversal planning path, the sampling survey mechanism is triggered, and the average mortality rate obtained is compared with the preset standard threshold. According to the comparison result, it is decided whether to implement the correction adjustment strategy.

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