Field pesticide spraying control method and system based on visual identification

Abstract

The invention discloses a field pesticide spraying control method and system based on visual identification, and relates to the technical field of agricultural generalization, and the method comprises the steps: placing a debugged target device in a selected field area, collecting the image information of the field area below in real time, obtaining the contour of each crop through an image segmentation algorithm, and obtaining a field pesticide spraying result; analyzing the image in each contour through a machine learning model, identifying the pest and disease damage condition of each crop, and synchronously obtaining a state data set; according to the technical scheme, the pesticide utilization rate is increased, pesticide waste and environmental pollution are reduced, healthy growth of crops is guaranteed, the yield and quality of the crops are improved, the visual recognition technology is combined with spraying amount regulation and control operation, precise targeted pesticide spraying operation is achieved, and the pesticide spraying efficiency is improved. The characteristics of intelligence and self-adaptability provide powerful support for precise management and sustainable development of modern agriculture.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural extension, and in particular to a field drug spraying control method and system based on visual recognition. Background Art

[0002] Agricultural extension technology refers to the activities that popularize scientific and technological achievements and practical technologies that can be applied to the fields of planting, animal husbandry, forestry, fishery, etc. through experiments, demonstrations, training, guidance and consulting services, and ensure their effective application in the entire process of agricultural production before, during and after production. It is a new agricultural extension technology that uses visual recognition technology to monitor field crops in real time and identify the occurrence of pests and diseases.

[0003] The existing application publication number is CN118570678A, and the document named "Method, device, electronic device and storage medium for spraying control drugs" points out that the method for spraying control drugs includes: determining the under-growing area of ​​crops in the target plot; converting the target plot into a target array according to the spraying width of the drone and the under-growing area of ​​the crops; the target array includes multiple array points, the distance between each array point is equal to the spraying width of the drone, and the multiple array points include normal array points and abnormal array points, and the abnormal array points are array points in the under-growing area of ​​the crops; based on the reinforcement learning model, a spraying path is determined for the drone in the target array, and the spraying path must pass through each normal array point, each normal array point is passed only once, and cannot pass through each abnormal array point; control the drone to spray the control drugs on the crops in the target plot according to the spraying path; the above-mentioned spraying method can improve the accuracy of the drone spraying control drugs and avoid excessive spraying, but it only uses the drone to spray the drugs in a range form on the crops from high altitude, and cannot accurately operate and process a certain plant.

[0004] In combination with the above documents and existing technologies, when traditionally using equipment with a crawler mechanism to spray pesticides in the field, the method usually adopted is to use a nozzle to spray the pesticides. A large range of operations will be carried out during spraying, and it is impossible to accurately apply pesticides to individual seedlings or crops. By improving the design of the nozzle, the nozzle can be used to apply pesticides to each seedling, but the specific spraying amount cannot be accurately controlled, and there will still be excessive and uneven application of pesticides. At the same time, for crops that have been sprayed with pesticides, if the spraying amount of the pesticide is still set according to the previous standards, there will still be certain problems. Therefore, how to accurately adjust the spraying amount according to the status of individual crops is a core problem that needs to be solved at present. Summary of the invention

[0005] 1. Technical issues to be solved

[0006] In view of the shortcomings of the prior art, the present invention provides a field pesticide spraying control method and system based on visual recognition, which improves the utilization rate of pesticides, reduces pesticide waste and environmental pollution, ensures the healthy growth of crops, and improves the yield and quality of crops. It combines visual recognition technology with spraying amount control operation to achieve precise targeted spraying operations, thus solving the problems raised in the background technology.

[0007] (II) Technical solution

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

[0009] A field drug spraying control method based on visual recognition, the steps of the method are:

[0010] Place the debugged target device in the selected field area, collect the field image information below in real time, use the image segmentation algorithm to obtain the outline of each crop, analyze the image within each outline through the machine learning model, identify the pest and disease situation of each crop, and synchronously obtain the status data set;

[0011] In the direction of movement of the target equipment, the pest and disease situation and status data set are preprocessed to build a status assessment model for the corresponding crop, and the presence of pests and diseases in the corresponding crop is used as a constraint condition;

[0012] Input the preprocessed pest and disease situation and status data set, and output the corresponding crop status index;

[0013] Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions;

[0014] Phase 1: Receive spraying instructions and spray the crops below according to the estimated spraying amount;

[0015] The second stage: the crops that have been sprayed in real time are subjected to secondary image recognition processing to detect the drug coverage of the corresponding crops. When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crops until the drug coverage of the corresponding crops is detected to meet the standard.

[0016] The third stage: when it is identified that the corresponding crop has been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether its current state index is lower than the initial state index. If it is not lower than the initial state index, the dosing strategy is adjusted.

[0017] Furthermore, the target equipment includes a spraying robot, two rows of spraying pipe groups installed below the spraying robot, and a probe module placed between the two spraying pipe groups;

[0018] Among them, the spray pipe group includes several cross nozzle structures, which correspond to the crops planted in the selected field area in an upper and lower relationship during spraying. The cross nozzle structure consists of a Class A nozzle located in the center and a Class B nozzle located at the four corners; the nozzles of the Class A nozzles are vertically downward, and the nozzles of the Class B nozzles are all directed towards the fixed point below the Class A nozzles, and are distributed in an inclined manner.

[0019] Furthermore, the process of analyzing the image within each contour is:

[0020] Feature extraction: For each crop contour image, extract the required features;

[0021] Among them, the required characteristics include at least: color, texture, shape, size, number and distribution of lesions;

[0022] Machine learning model analysis: The extracted required features are input into a pre-trained machine learning model for analysis to identify the characteristics of various pests and diseases. When training the model, supervised learning is performed to enable the machine learning model to map the input required features to the output of the pest and disease density estimation value.

[0023] Pest and disease identification and density estimation: Based on the analysis results of the machine learning model, the pest and disease situation of each contour image is identified. If the corresponding crop has a disease, the machine learning model outputs the corresponding disease type and the density estimate of the pest and disease on each crop.

[0024] Furthermore, the status data set includes the average leaf width value and chlorophyll content of each crop.

[0025] Furthermore, when constructing the state assessment model for the corresponding crops, the formula based on it is:

[0026]

[0027] In the formula, Csi(D, W, C) represents the crop state index calculation function, Csi represents the state index of the corresponding crop, D represents the density estimation value, W represents the average leaf width value, C represents the chlorophyll content, and D c represents the critical density value, W_opt represents the average width of the leaf;

[0028] a1, b1, c1 are all weight coefficients in the linear calculation function, and their value ranges are all [0, 1];

[0029] a2, b2, c2 are all weight coefficients in the exponential calculation function, and their value ranges are all [0, 2]. α is the exponential term coefficient, and α>0;

[0030] k1 and k2 represent the weight coefficients of the influence of leaf average width and chlorophyll content on the state index when there is no disease or insect pest, and the value range is [0, 1];

[0031] if indicates a constraint condition.

[0032] Furthermore, the method for calculating the estimated spraying amount based on the state index is as follows:

[0033] P=B0+q*Csi

[0034] In the formula, P represents the estimated spraying amount of the corresponding crop, B0 represents the basic spraying amount, q represents the proportional coefficient, and q is a positive number and greater than 0.

[0035] Furthermore, in the first and second stages, the spraying action is performed using the Class A nozzle in the corresponding cross nozzle structure; in the third stage, the Class A nozzle and Class B nozzle in the corresponding cross nozzle structure are used when adjusting the dosing strategy.

[0036] Furthermore, a marker is pre-added to the liquid medicine to be sprayed, and during the secondary image recognition process, the marker image on the crop surface is analyzed to detect the coverage of the corresponding crop with the medicine, i.e., the coverage of the marker;

[0037] Among them, color recognition technology is used in the secondary image recognition processing to extract the area that matches the color of the marker, and the extracted area is quantitatively analyzed to obtain the drug coverage.

[0038] Furthermore, the adjusted dosing strategy is as follows:

[0039] According to the current state index and the initial state index, a proportional adjustment calculation model is established to generate the dosage of the corresponding crop, and the estimated spraying amount corresponding to the initial state index is accumulated to obtain the secondary spraying amount of the crop, and the spraying treatment of the crop is completed according to the secondary spraying amount;

[0040] When the crop is sprayed according to the secondary spraying amount, the distance value between the current corresponding crop and the surrounding adjacent crops is detected, the minimum distance value is extracted, and the minimum distance value is input into the rule engine to output the control spacing. In the cross nozzle structure corresponding to the crop, the extension value of each Class B nozzle is executed according to the control spacing.

[0041] Furthermore, the formula used to establish the proportional regulation calculation model is as follows:

[0042] Jd=C_ratio×C_dif 2 ×ε

[0043] In the formula, Jd represents the dosage of the corresponding crop, C_ratio represents the ratio between the current state index and the initial state index, C_dif represents the difference between the current state index and the initial state index, ∈ represents the error coefficient, and the value range is [0, 1];

[0044] The operation process of the rule engine is as follows:

[0045] Receive the minimum distance and synchronously collect the current height of the target device. Set in the rule engine:

[0046]

[0047] Among them, Sr represents the control distance, d_min represents the minimum distance, and H represents the current height of the target device.

[0048] A field drug spraying control system based on visual recognition, the system comprising:

[0049] The visual recognition module places the debugged target device in the selected field area, collects the field image information below in real time, uses the image segmentation algorithm to obtain the outline of each crop, analyzes the image within each outline through the machine learning model, identifies the pest and disease situation of each crop, and synchronously obtains the status data set;

[0050] The judgment control module pre-processes the pest and disease situation and state data set in the moving direction of the target device, builds a state assessment model for the corresponding crop, and takes the presence of pests and diseases in the corresponding crop as a constraint condition;

[0051] Input the preprocessed pest and disease situation and status data set, and output the corresponding crop status index;

[0052] Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions;

[0053] A spraying stage control module, which has a built-in primary spraying unit, a recheck coverage unit and an adjustment and dosing unit; the primary spraying unit is used to perform the first stage operation, the recheck coverage unit is used to perform the second stage operation, and the adjustment and dosing unit is used to perform the third stage operation;

[0054] The first stage: receiving spraying instructions and spraying the crops below according to the estimated spraying amount;

[0055] The second stage: the crops that have been sprayed in real time are subjected to secondary image recognition processing to detect the drug coverage of the corresponding crops. When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crops until the drug coverage of the corresponding crops is detected to meet the standard.

[0056] The third stage: when it is identified that the corresponding crop has been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether its current state index is lower than the initial state index. If it is not lower than the initial state index, the dosing strategy is adjusted.

[0057] (III) Beneficial effects

[0058] The present invention provides a field drug spraying control method and system based on visual recognition, which has the following beneficial effects:

[0059] (1) This solution can accurately spray pesticides according to the actual pest and disease conditions and growth status of crops, and automatically adjust the initial spraying amount, thereby improving the utilization rate of pesticides and reducing pesticide waste and environmental pollution. At the same time, by real-time collection and analysis of field image information, it can promptly detect and deal with pest and disease problems, effectively ensuring the healthy growth of crops and improving the yield and quality of crops.

[0060] (2) This plan is implemented in stages;

[0061] First, the spraying amount is estimated based on the crop's pest and disease conditions and growth status, and the spraying action is executed;

[0062] Subsequently, the drug coverage rate is detected through secondary image recognition processing to ensure that the drug solution evenly covers the crop surface; if the coverage rate does not meet the standard, the secondary spraying mechanism is triggered until the standard is met;

[0063] In addition, when it is identified that the crop has been oversprayed, the change in its state index is determined through visual inspection, and the dosing strategy is adjusted accordingly to achieve precise application of pesticides and enhance management effects. At the same time, by detecting the distance between crops and inputting it into the rule engine, the extension value of the Class B sprinkler acting on the area below the crop side is dynamically adjusted to avoid the liquid medicine affecting the surrounding crops while also being able to carry out targeted and comprehensive liquid medicine spraying treatment on the target crop, thus improving the efficacy of the medicine to a certain extent.

[0064] In summary, this technical solution improves the utilization rate of pesticides, reduces pesticide waste and environmental pollution, ensures the healthy growth of crops, and improves the yield and quality of crops. It combines visual recognition technology with spraying volume control operations to achieve precise targeted spraying operations. Its intelligent and adaptive characteristics also provide strong support for the precise management and sustainable development of modern agriculture. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of the control method steps in the present invention;

[0066] Figure 2One of the schematic diagrams of a single cross nozzle structure in the target device used in the present invention;

[0067] Figure 3 This is a second schematic diagram of a single cross nozzle structure in the target device used in the present invention;

[0068] Figure 4 It is a schematic diagram of a real scene of the target device acting on some crops below in the present invention;

[0069] Figure 5 It is a modular schematic diagram of the control system in the present invention. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] Embodiment 1:

[0072] See also Figures 1 to 4 , this embodiment provides a field drug spraying control method and system based on visual recognition;

[0073] This method is an intelligent field pesticide spraying control method that integrates visual recognition, information processing, and precise control. Through advanced visual recognition technology, the system can identify pests and diseases and crop status in farmland in real time, and combined with precise spraying control mechanism, it can achieve directional and quantitative spraying of drugs, and can carry out targeted spraying treatment in stages, effectively improving the efficiency of pesticide use, reducing environmental pollution, and improving agricultural production benefits.

[0074] The specific steps of the control method are as follows:

[0075] S1. Place the debugged target device in the selected field area, collect the field image information below in real time, use the image segmentation algorithm to obtain the outline of each crop, analyze the image within each outline through the machine learning model, identify the pest and disease situation of each crop, and synchronously obtain the status data set;

[0076] The target equipment includes a spraying robot, two rows of spraying tubes installed below the spraying robot (for spraying drugs), and a probe module placed between the two spraying tubes (for image recognition and analysis). The spraying robot is a crawler structure, and the two crawlers are located in two adjacent ditches in the selected field area. Therefore, the movement trajectory of the spraying robot completely covers every field area below.

[0077] Crops are planted in intervals in each field, such as rice seedlings (the arrangement is relatively regular and corresponds to the position of the cross nozzle structure above, so that each cross nozzle structure can accurately spray the corresponding crops below);

[0078] This embodiment takes the rice seedlings planted uniformly in the field as an example:

[0079] The two rows of spraying pipe groups are vertically distributed on the movement track of the spraying robot, and the spraying robot is equipped with a medicine box, a booster pump and other structures to spray the required liquid medicine through the spraying pipe group and act on the corresponding seedlings below;

[0080] like Figures 2 to 4 As shown, the spray pipe group includes several cross nozzle structures, which are composed of a type A nozzle located in the center and a type B nozzle located at the four corners. The nozzles of the type A nozzles are vertically downward, and the nozzles of the type B nozzles are all directed to a certain distance below the type A nozzles, and are distributed in an inclined manner (specifically, inclined at 30° to 60°). The area covered during spraying is referred to Figure 4 It can be seen that the liquid medicine is used to fully cover the bottom of the leaves of the seedlings;

[0081] In the same nozzle structure, the distance between any type B nozzle and type A nozzle can be adjusted, and the adjustment can be performed synchronously through the telescopic rod, which ensures to a certain extent that the liquid sprayed from the type B nozzle will not act on the adjacent seedlings;

[0082] Before using the image segmentation algorithm to obtain the outline of each crop:

[0083] The field image information needs to be preprocessed, including cropping, scaling, denoising and other steps to improve the image quality. Then, the preprocessed field image information is segmented using image segmentation algorithms, such as deep learning algorithms such as the Unet model, to obtain the outline of each crop. The image segmentation algorithm can automatically identify the crop area in the field image information and separate it from the background.

[0084] The process of analyzing the image within each contour through the machine learning model is:

[0085] Feature extraction: For each crop contour image, useful features are extracted, including the size, number and distribution of lesions in addition to color, texture and shape. These complex features are crucial for estimating the density of pests and diseases;

[0086] Among them, color, texture, and shape are specific not only to the crop but also to the disease spots that are present;

[0087] Machine learning model analysis: The extracted features are input into a pre-trained machine learning model (such as a convolutional neural network (CNN)) for analysis. The machine learning model can accurately identify the features of various pests and diseases by learning a large amount of labeled pest and disease image data. When training the model, a large amount of image data containing pest and disease density annotations is also required. Through supervised learning, the model learns how to map the input features to the output of pest and disease density estimation.

[0088] The image data containing the pest density annotations can be obtained by manual annotation or by using other sensors (such as lidar); the machine learning model used in this embodiment is a CNN model;

[0089] Pest and disease identification and density estimation: Based on the analysis results of the CNN model, the pest and disease situation of each contour image is identified; if the corresponding crop has a disease, the model will output the corresponding disease type and the estimated density of the pest and disease on each crop;

[0090] Among them, the pest and disease situation shows:

[0091] the presence or absence of diseases, and when present, the type of disease and the estimated density of pests on each crop;

[0092] The status data set includes the average leaf width and chlorophyll content of each crop. The average leaf width represents the data obtained by summing up and averaging the maximum width of all leaves detected in the corresponding crop under the current detection environment. The chlorophyll content is obtained through non-contact spectral detection, which uses visible light and near-infrared spectral analysis technology to infer the chlorophyll content by measuring the spectral information after light passes through or reflects from the leaves.

[0093] S2. Preprocess the pest and disease situation and state data set in the moving direction of the target device, build a state assessment model for the corresponding crop, take the presence of pests and diseases on the corresponding crop as a constraint condition, input the preprocessed pest and disease situation and state data set, and output the state index of the corresponding crop;

[0094] Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions;

[0095] Among them, when the target device continues to move, its movement trajectory will completely cover the selected field area;

[0096] The contents of preprocessing the pest and disease situation and status dataset are as follows:

[0097] Normalize the pest and disease situation and status data set to facilitate subsequent calculations and processing;

[0098] When constructing the state assessment model for the corresponding crop, the formula is based on:

[0099]

[0100] In the formula, Csi(D, W, C) represents the crop state index calculation function, Csi represents the state index of the corresponding crop, D represents the density estimation value, W represents the average leaf width value, C represents the chlorophyll content, and D c It represents the critical density value, which is obtained by calculating the average value of the corresponding historical data. It is used to distinguish between linear calculation and exponential calculation. W_opt represents the average width of the blade.

[0101] a1, b1, c1 are all weight coefficients in the linear calculation function, and their value ranges are all [0, 1];

[0102] a2, b2, c2 are all weight coefficients in the exponential calculation function, and their value ranges are all [0, 2]. α is the exponential term coefficient, and α>0;

[0103] k1 and k2 represent the weight coefficients of the influence of leaf average width and chlorophyll content on the state index when there is no disease or insect pest, and the value range is [0, 1];

[0104] if indicates a constraint;

[0105] The weight coefficient is determined by the coefficient of variation method, which is a method of assigning weights to each indicator according to the degree of variation between the current value of each evaluation indicator and the target value. If the numerical difference of a certain indicator is large, it can clearly distinguish the evaluated objects, indicating that the indicator has rich information for distinguishing, and thus the indicator should be given a larger weight. On the contrary, if the numerical difference of each evaluated object on a certain indicator is small, then the ability of this indicator to distinguish the evaluated objects is weak, and thus the indicator should be given a smaller weight. This method directly uses the information contained in each indicator to obtain the weight of the indicator through calculation, so it is objective.

[0106] Function logic description:

[0107] When D≤D c When the pest density is low, a linear calculation method is used, and the state index Csi is a linear combination of the density estimate D, the deviation degree of the average leaf width value (1-W / W_opt), and the insufficient chlorophyll content (1-C); when D>D cWhen D=0 (high density of pests and diseases), the exponential calculation method is used. The state index Csi is an exponential function of the density estimate D, and is affected by the average leaf width and chlorophyll content at the same time. When D=0 (no pests and diseases), the state index Csi is mainly affected by the deviation degree of the average leaf width (1-W / W_opt) and the insufficient chlorophyll content (1-C). The square function is used to calculate its nonlinear contribution.

[0108] Example:

[0109] Assume there is a set of data: D = 8 / m2 (pests and diseases exist), W = 10cm, C = 2.0mg / g, D c =5 pieces / m2, W_opt=12cm, a1, b1, c1 are 1, 0.5, 0.3 respectively, a2, b2, c2 are 2, 0.5, 0.3 respectively, α=0.5, k1, k2 are 0.4 and 0.6 respectively;

[0110] When pests and diseases exist (D=8>D c =5):

[0111] Csi(8, 10, 2.0)≈4.48+0.083-0.3=4.263 (keep three decimal places);

[0112] When there is no pest or disease (D = 0):

[0113] Csi(0,10,2.0)≈0.011+0.6=0.611 (keep three decimal places);

[0114] It should be noted that:

[0115] In constructing the state assessment model for the corresponding crops, the larger the state index of the crop, the worse the state of the corresponding crop, and the more pesticides are needed;

[0116] The method for calculating the estimated spraying amount based on the state index is as follows:

[0117] P=B0+q*Csi

[0118] In the formula, P represents the estimated spraying amount of the corresponding crop, B0 represents the basic spraying amount, q represents the proportional coefficient, and the proportional coefficient q is positively correlated with the state index Csi, q is a positive number and greater than 0; it should be noted that even if the crop is in a completely healthy state, a certain amount of pesticide may be required for prevention or maintenance, so the basic spraying amount is selected.

[0119] By adopting the above technical solutions, intelligent and precise crop pest and disease control effects are achieved;

[0120] The solution first uses image segmentation algorithms and machine learning models to identify pests and diseases and estimate density of crops in the field, while obtaining status data such as the average width of crop leaves and chlorophyll content. Then, a crop status assessment model is built to output the crop status index based on the pest and disease situation and status data set, which is used as the basis for decision-making on spraying dosage.

[0121] This technical solution solves the problems of over-application and uneven application of pesticides in traditional spraying methods, and realizes precise spraying according to the actual pest and disease conditions and growth status of crops, thereby improving the utilization rate of pesticides and reducing pesticide waste and environmental pollution. At the same time, by real-time collection and analysis of field image information, pest and disease problems can be discovered and dealt with in a timely manner, effectively ensuring the healthy growth of crops and improving the yield and quality of crops. In addition, the solution can also automatically adjust the spraying amount according to the situation, such as the pest and disease situation, providing strong support for the sustainable development of modern agriculture.

[0122] S3, Phase 1:

[0123] Receive spraying instructions and spray the crops below according to the estimated spraying amount;

[0124] Among them, the structure used when performing the spraying action is:

[0125] lie in Figure 4 The cross nozzle structure in front of the middle arrow corresponds to a cross nozzle structure located above the corresponding crop. Only the valve connecting the liquid medicine and the type A nozzle is opened, and the type A nozzle sprays the liquid medicine on the surface of the corresponding crop, thereby realizing the spraying action. The corresponding spraying amount is also operated according to the estimated spraying amount P of the corresponding crop.

[0126] Phase 2:

[0127] Perform secondary image recognition processing on crops that have been sprayed in real time to detect the amount of drug coverage on the corresponding crops. When the drug coverage rate reaches the preset coverage standard, no response will be made.

[0128] When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crop until the drug coverage of the corresponding crop is detected to meet the standard;

[0129] Among them, the surface of the crop that has been sprayed in real time is covered with liquid medicine;

[0130] Add markers to the drug solution. During the secondary image recognition process, only the marker image on the crop surface is analyzed to detect the drug coverage of the corresponding crop, i.e., the marker coverage.

[0131] It should be noted that this marker needs to have significant visual characteristics, such as a specific color or fluorescence, and be harmless to the seedlings and the environment; for example, a food-grade edible pigment can be used as a marker; adding method: when preparing the drug, evenly mix an appropriate amount of the marker into the drug;

[0132] Specifically, color recognition technology is used in the secondary image recognition process:

[0133] Principle: Color recognition technology based on color space (such as RGB, HSV, etc.) can detect and analyze specific colors in images; Application: Use a high-resolution color camera (i.e., probe module) to capture images of the crop surface, and use a color recognition algorithm to extract areas that match the marker color; Advantages: Intuitive, easy to implement, and suitable for situations where the marker color contrasts sharply with the crop surface;

[0134] Apply the above-mentioned color recognition technology to extract the area of ​​the marker;

[0135] By quantitatively analyzing the extracted marker regions, its drug coverage can be obtained;

[0136] The preset coverage standard is usually set at 95% coverage;

[0137] When the drug coverage rate does not reach 95%, an early warning signal is issued, which is received, recorded and stored by the remote end, indicating that there is an abnormality or other problem in the structure of the spray pipe group of the corresponding target equipment, and it needs to be checked and repaired after the operation is completed;

[0138] When the secondary spraying mechanism is triggered:

[0139] The structure used refers to Figure 4 As shown, another group of spraying pipes behind the arrow opens the corresponding Class A nozzles flush with the Class A nozzles in front. After the target device moves, the corresponding Class A nozzles in the rear will be above the corresponding crops, and then the secondary spraying action will be realized. At this time, the probe module is continuously opened until it is detected that the drug coverage reaches the preset coverage standard;

[0140] Phase 3:

[0141] Under the condition that the corresponding crop has been identified to have been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether the current state index is lower than the initial state index;

[0142] If it is not lower than that, the dosing strategy is adjusted, proving that the initial spraying action is invalid;

[0143] If it is lower than that, no response will be made, which proves that the initial spraying action is effective;

[0144] Wherein, if it is identified that the corresponding crop has been sprayed before the start of the first stage, it means that the current spraying operation is the second or more spraying operation in the same period (for example, the seedling period);

[0145] The visual inspection operation is performed taking into account the crops that have been marked previously;

[0146] The adjusted dosing strategy implemented is as follows:

[0147] S301, establishing a proportional adjustment calculation model based on the current state index and the initial state index to generate the dosage of the corresponding crop, and accumulating the estimated spraying amount corresponding to the initial state index to obtain the secondary spraying amount of the crop, and completing the spraying treatment of the crop based on the secondary spraying amount;

[0148] Among them, the formula used when establishing the proportional regulation calculation model is as follows:

[0149] Jd=C_ratio×C_dif 2 ×ε

[0150] Where Jd represents the dosage of the corresponding crop, C_ratio represents the ratio between the current state index and the initial state index, C_dif represents the difference between the current state index and the initial state index, ∈ represents the error coefficient, and the value range is [0, 1];

[0151] Logical explanation: The ratio between the current state index and the initial state index reflects the degree of change of the state index. The difference between the current state index and the initial state index is then squared. This square term emphasizes the influence of the change in the state index on the dosage, that is, the greater the change in the state index, the faster the dosage increases (because the square term will amplify the change). Finally, the error coefficient is used to consider the errors in the actual measurement and model calculation to adjust the final result and improve the accuracy of the calculation result.

[0152] The following is an example of a table format:

[0153]

[0154] In the above example, the error coefficient is a variable. Through this formula and table example, we can more intuitively understand the relationship between the dosage and the change of the state index, and adjust the error coefficient according to the actual situation to adapt to different application scenarios;

[0155] S302, when spraying the crop according to the secondary spraying amount, detect the distance value between the current corresponding crop and the surrounding adjacent crops, extract the minimum distance value, and input the minimum distance value into the rule engine, output the control spacing, and the extension value of each Class B nozzle in the cross nozzle structure corresponding to the crop is executed according to the control spacing;

[0156] Among them, when the crop is sprayed with pesticides according to the secondary spraying amount, all types of nozzles in the cross nozzle structure used are presented as follows Figure 2 In the state shown, the A-type nozzle sprays the crop from above, and the B-type nozzle sprays the crop from the side. Under the condition of not affecting other crops, the upper and lower surfaces of the crop leaves and the roots can be infiltrated with the liquid to a certain extent;

[0157] Specifically, during the secondary spraying process, in order to accurately spray the liquid and avoid affecting the surrounding crops, we need to detect the distance between the target crop and the adjacent crops. Specifically, we use image recognition or sensor technology to obtain several distance values ​​between the crop border and the adjacent crop border.

[0158] In order to extract the minimum value among these distance values, a traversal algorithm is used, that is, all detected distance values ​​are compared one by one, a larger value is initially set as the candidate for the minimum value, and then each distance value is compared with this candidate value in turn; if the current distance value is less than the candidate value, the candidate value is updated to the current distance value; after traversing all distance values, the candidate value is the required minimum distance value; the algorithm is simple and efficient, and can quickly and accurately extract the minimum distance between crops, providing key parameters for subsequent spraying treatment, ensuring that the liquid can be accurately sprayed on the target crops;

[0159] The operation process of the rule engine is as follows:

[0160] Receive the minimum distance and synchronously collect the current height of the target device. Set in the rule engine:

[0161]

[0162] Among them, Sr represents the control distance, d_min represents the minimum distance, and H represents the current height of the target device;

[0163] The control spacing Sr should be a balance between the minimum distance d_min and the target device height H. Specifically:

[0164] When d_min is very small (i.e. the distance between crops is very close), Sr should be relatively small to avoid spraying the liquid onto adjacent crops, thereby reducing the extension value of the B-type nozzle. When H is very large (i.e. the height of the equipment is very high), the liquid may be more easily affected by external factors such as wind during the spraying process and disperse, so Sr should also be relatively small to ensure that the liquid can be accurately sprayed onto the target crop.

[0165] Specifically, in the same cross nozzle structure, the four B-type nozzles are all away from the central A-type nozzle. The distance away is the adjustment spacing. Figure 3 shown.

[0166] By adopting the above technical solution, accurate and efficient crop spraying effect is achieved, solving the problems of excessive spraying, uneven spraying and inability to adjust the spraying amount according to the actual state of the crop in the traditional spraying process;

[0167] The solution first estimates the spraying amount based on the crop's pest and disease conditions and growth status, and then executes the spraying action;

[0168] Subsequently, the drug coverage rate is detected through secondary image recognition processing to ensure that the drug solution evenly covers the crop surface; if the coverage rate does not meet the standard, the secondary spraying mechanism is triggered until the standard is met;

[0169] In addition, when it is identified that the crop has been oversprayed, the change in its state index is determined through visual inspection, and the dosing strategy is adjusted accordingly to achieve precise spraying. At the same time, during the secondary spraying process, the distance between crops is detected and input into the rule engine to dynamically adjust the extension value of the Class B nozzle to prevent the liquid from affecting the surrounding crops.

[0170] This technical solution improves the utilization rate of pesticides, reduces pesticide waste and environmental pollution, ensures the healthy growth of crops, and improves the yield and quality of crops; at the same time, its intelligent and adaptive characteristics also provide strong support for the precise management and sustainable development of modern agriculture.

[0171] Embodiment 2:

[0172] See also Figure 5 Based on Example 1, this embodiment also provides a field drug spraying control system based on visual recognition, which includes the following:

[0173] The visual recognition module places the debugged target device in the selected field area, collects the field image information below in real time, uses the image segmentation algorithm to obtain the outline of each crop, analyzes the image within each outline through the machine learning model, identifies the pest and disease situation of each crop, and synchronously obtains the status data set;

[0174] The judgment control module pre-processes the pest and disease situation and state data set in the moving direction of the target device, builds a state assessment model for the corresponding crop, and takes the presence of pests and diseases in the corresponding crop as a constraint condition;

[0175] Input the preprocessed pest and disease situation and status data set, and output the corresponding crop status index;

[0176] Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions;

[0177] A spraying stage control module, which has a built-in primary spraying unit, a recheck coverage unit and an adjustment and dosing unit; the primary spraying unit is used to perform the first stage operation, the recheck coverage unit is used to perform the second stage operation, and the adjustment and dosing unit is used to perform the third stage operation;

[0178] The first stage: receiving spraying instructions and spraying the crops below according to the estimated spraying amount;

[0179] The second stage: the crops that have been sprayed in real time are subjected to secondary image recognition processing to detect the drug coverage of the corresponding crops. When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crops until the drug coverage of the corresponding crops is detected to meet the standard.

[0180] The third stage: when it is identified that the corresponding crop has been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether its current state index is lower than the initial state index. If it is not lower than the initial state index, the dosing strategy is adjusted.

[0181] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0182] 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, and may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0183] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A field drug spraying control method based on visual recognition, characterized in that: The steps of this method are: Place the debugged target device in the selected field area, collect the field image information below in real time, use the image segmentation algorithm to obtain the outline of each crop, analyze the image within each outline through the machine learning model, identify the pest and disease situation of each crop, and synchronously obtain the status data set; In the direction of movement of the target equipment, the pest and disease situation and status data set are preprocessed to build a status assessment model for the corresponding crop, and the presence of pests and diseases in the corresponding crop is used as a constraint condition; Input the preprocessed pest and disease situation and status data set, and output the corresponding crop status index; Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions; Phase 1: Receive spraying instructions and spray the crops below according to the estimated spraying amount; The second stage: the crops that have been sprayed in real time are subjected to secondary image recognition processing to detect the drug coverage of the corresponding crops. When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crops until the drug coverage of the corresponding crops is detected to meet the standard. The third stage: when it is identified that the corresponding crop has been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether its current state index is lower than the initial state index. If it is not lower than the initial state index, the dosing strategy is adjusted.

2. The method for controlling field drug spraying based on visual recognition according to claim 1, characterized in that: The target equipment includes a spraying robot, two rows of spraying pipes installed under the spraying robot, and a probe module placed between the two spraying pipes. Among them, the spray pipe group includes several cross nozzle structures, which correspond to the crops planted in the selected field area in an upper and lower relationship during spraying. The cross nozzle structure consists of a Class A nozzle located in the center and a Class B nozzle located at the four corners; the nozzles of the Class A nozzles are vertically downward, and the nozzles of the Class B nozzles are all directed towards the fixed point below the Class A nozzles, and are distributed in an inclined manner.

3. A field drug spraying control method based on visual recognition according to claim 2, characterized in that: The process of analyzing the image within each contour is: Feature extraction: For each crop contour image, extract the required features; Among them, the required characteristics include at least: color, texture, shape, size, number and distribution of lesions; Machine learning model analysis: The extracted required features are input into a pre-trained machine learning model for analysis to identify the characteristics of various pests and diseases. When training the model, supervised learning is performed to enable the machine learning model to map the input required features to the output of the pest and disease density estimation value. Pest and disease identification and density estimation: Based on the analysis results of the machine learning model, the pest and disease situation of each contour image is identified. If the corresponding crop has a disease, the machine learning model outputs the corresponding disease type and the density estimate of the pest and disease on each crop.

4. The method for controlling field drug spraying based on visual recognition according to claim 3, characterized in that: The status dataset includes the average leaf width value and chlorophyll content for each crop.

5. The method for controlling field drug spraying based on visual recognition according to claim 4, characterized in that: When constructing the state assessment model for the corresponding crop, the formula is based on: In the formula, Csi(D, W, C) represents the crop state index calculation function, Csi represents the state index of the corresponding crop, D represents the density estimation value, W represents the average leaf width value, C represents the chlorophyll content, and D c represents the critical density value, W_opt represents the average width of the leaf; a1, b1, c1 are all weight coefficients in the linear calculation function, and their value ranges are all [0, 1]; a2, b2, c2 are all weight coefficients in the exponential calculation function, and their value ranges are all [0, 2]. α is the exponential term coefficient, and α>0; k1 and k2 represent the weight coefficients of the influence of leaf average width and chlorophyll content on the state index when there is no disease or insect pest, and the value range is [0, 1]; if indicates a constraint condition.

6. The method for controlling field drug spraying based on visual recognition according to claim 5, characterized in that: The method for calculating the estimated spraying amount based on the state index is as follows: P=B0+q*Csi In the formula, P represents the estimated spraying amount of the corresponding crop, B0 represents the basic spraying amount, q represents the proportional coefficient, and q is a positive number and greater than 0.

7. The method for controlling field drug spraying based on visual recognition according to claim 6, characterized in that: In the first and second stages, the spraying action is performed using the Class A nozzle in the corresponding cross nozzle structure; in the third stage, the Class A nozzle and Class B nozzle in the corresponding cross nozzle structure are used when adjusting the dosing strategy.

8. The method for controlling field drug spraying based on visual recognition according to claim 1, characterized in that: Add markers to the sprayed liquid in advance, and analyze the marker image on the crop surface during the secondary image recognition process to detect the coverage of the corresponding crop, i.e., the coverage of the marker; Among them, color recognition technology is used in the secondary image recognition processing to extract the area that matches the color of the marker, and the extracted area is quantitatively analyzed to obtain the drug coverage.

9. The method for controlling field drug spraying based on visual recognition according to claim 7, characterized in that: The adjusted dosing strategy implemented is as follows: According to the current state index and the initial state index, a proportional adjustment calculation model is established to generate the dosage of the corresponding crop, and the estimated spraying amount corresponding to the initial state index is accumulated to obtain the secondary spraying amount of the crop, and the spraying treatment of the crop is completed according to the secondary spraying amount; The formula used to establish the proportional regulation calculation model is as follows: Jd=C_ratio×C_dif 2 ×ε In the formula, Jd represents the dosage of the corresponding crop, C_ratio represents the ratio between the current state index and the initial state index, C_dif represents the difference between the current state index and the initial state index, ∈ represents the error coefficient, and the value range is [0, 1]; When spraying the crop according to the secondary spraying amount, the distance between the current corresponding crop and the surrounding adjacent crops is detected, the minimum distance value is extracted, and the minimum distance value is input into the rule engine to output the control spacing. In the cross nozzle structure corresponding to the crop, the extension value of each Class B nozzle is executed according to the control spacing; The operation process of the rule engine is as follows: Receive the minimum distance and synchronously collect the current height of the target device. Set in the rule engine: Among them, Sr represents the control distance, d_min represents the minimum distance, and H represents the current height of the target device.

10. A field drug spraying control system based on visual recognition, characterized in that: The system includes: The visual recognition module places the debugged target device in the selected field area, collects the field image information below in real time, uses the image segmentation algorithm to obtain the outline of each crop, analyzes the image within each outline through the machine learning model, identifies the pest and disease situation of each crop, and synchronously obtains the status data set; The judgment control module pre-processes the pest and disease situation and state data set in the moving direction of the target device, builds a state assessment model for the corresponding crop, and takes the presence of pests and diseases in the corresponding crop as a constraint condition; Input the preprocessed pest and disease situation and status data set, and output the corresponding crop status index; Mark crops with pests and diseases, calculate the estimated spraying amount based on the status index, and send spraying instructions; A spraying stage control module, which has a built-in primary spraying unit, a recheck coverage unit and an adjustment and dosing unit; the primary spraying unit is used to perform the first stage operation, the recheck coverage unit is used to perform the second stage operation, and the adjustment and dosing unit is used to perform the third stage operation; The first stage: receiving spraying instructions and spraying the crops below according to the estimated spraying amount; The second stage: the crops that have been sprayed in real time are subjected to secondary image recognition processing to detect the drug coverage of the corresponding crops. When the drug coverage rate does not meet the coverage standard, an early warning signal is issued and the secondary spraying mechanism is triggered to spray the drug on the surface of the corresponding crops until the drug coverage of the corresponding crops is detected to meet the standard. The third stage: when it is identified that the corresponding crop has been oversprayed before the start of the first stage, a visual inspection operation is performed to determine whether its current state index is lower than the initial state index. If it is not lower than the initial state index, the dosing strategy is adjusted.

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