Plant protection unmanned aerial vehicle spraying pesticide application buffer distance determination method based on phytotoxicity detection limit and related equipment

Abstract

The invention discloses a plant protection unmanned aerial vehicle spraying pesticide application buffer distance measuring method based on a phytotoxicity detection limit and related equipment. The method comprises the following steps: placing a plurality of ground drift amount acquisition devices at downwind distances of a plurality of plant protection unmanned aerial vehicles; after flight is implemented based on different test parameters, ground drift fog drops collected by a ground drift amount collecting device are obtained; determining a ground drift amount corresponding to each downwind distance according to a ground drift amount acquisition device, and generating a first mapping relation between the downwind distance and the ground drift amount; constructing a ground drift prediction model of different downwind distances under different characteristic parameters; acquiring a second mapping relation between the phytotoxicity degree of the sensitive crop plant and the drift amount of the ground drift device at the corresponding position to determine a drift amount phytotoxicity detection limit; the buffer distance is determined according to the ground drift amount prediction model and the drift amount phytotoxicity detection limit, and the pesticide spraying buffer distance of the plant protection unmanned aerial vehicle can be reasonably set based on the accurate phytotoxicity detection limit of the adjacent crops.

Description

Technical Field

[0001] The present disclosure generally relates to the technical field of agricultural plant protection, and particularly relates to a method and device for measuring the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage. Background Art

[0002] During the process of crop planting, there are numerous crops in the same period. To ensure the normal growth of crops, growers will use insecticides, fungicides, herbicides, etc. to control pests and diseases and inhibit weeds. Among them, herbicides achieve weed control and seedling protection through selective actions such as time difference, position difference, morphology, physiological biochemistry, etc. Although herbicides have selectivity for crops (such as wheat) in the weedy field, there is a risk of potential threat to other crops on adjacent fields or ridges.

[0003] With the wide application of plant protection unmanned aircraft, the problem of pesticide drift has become increasingly serious. Due to the unreasonable use of farmland herbicides, other crops planted in the surrounding areas are often affected, resulting in growth retardation and yield reduction. Pesticide drift not only damages surrounding sensitive crops but also poses a challenge to environmental protection, which has attracted great attention to its potential hazards. Based on this, how to reasonably judge the adverse effects of pesticides on adjacent crops and set a reasonable buffer distance has become an urgent problem to be solved. Summary of the Invention

[0004] In view of the above defects or deficiencies in the prior art, it is desirable to provide a method and device for measuring the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage, which can reasonably set the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the accurate detection limit of the drift amount of pesticide damage to adjacent crops, effectively reduce the drug impact on adjacent fields while ensuring reasonable pesticide application to the target field, and improve the total output value of crops.

[0005] In a first aspect, an embodiment of the present application provides a method for measuring the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage, including:

[0006] Selecting a plurality of downwind distances based on the reference position of the test flight of the plant protection unmanned aircraft;

[0007] Placing a plurality of ground drift amount collection devices along the flight direction of the plant protection unmanned aircraft at each of the downwind distances;

[0008] After the plant protection unmanned aircraft flies based on different test parameters, obtaining the ground drift fog droplets collected by the ground drift amount collection devices;

[0009] Determining the ground drift amount corresponding to each of the downwind distances according to the ground drift amount collection devices, and generating a first mapping relationship between the downwind distance and the ground drift amount;

[0010] Construct a ground drift amount prediction model related to the distance in the downward direction under different characteristic parameters;

[0011] Obtain a second mapping relationship between the phytotoxicity degree of sensitive crop plants in adjacent plots and the drift amount of the ground drift device at the corresponding position, and determine the drift amount phytotoxicity detection limit corresponding to the sensitive crop plants;

[0012] According to the ground drift amount prediction model and the drift amount phytotoxicity detection limit, determine the buffer distance required for the spray application task to be performed to be harmless to the sensitive crop plants in adjacent plots.

[0013] In some embodiments, the obtaining of the second mapping relationship between the phytotoxicity degree of sensitive crop plants in adjacent plots and the drift amount of the ground drift device at the corresponding position includes:

[0014] Place the sensitive crop plants in adjacent plots at different downwind distances, and place a ground drift device at the corresponding position of the plants to conduct a phytotoxicity test on the sensitive crop plants, and obtain the sensitive crop plants after the test and the ground drift amount at the corresponding position;

[0015] Pick all the leaves of the sensitive crop plants after the test and make them into leaf patterns;

[0016] Obtain the phytotoxic pixel features and leaf pixel features corresponding to the leaf patterns;

[0017] Based on the phytotoxic pixel features and the leaf pixel features, determine the yellowing ratio corresponding to the sensitive crop plants after the test;

[0018] Based on the ground drift amount corresponding to the sensitive crop plants after the test and the yellowing ratio, generate the second mapping relationship between the phytotoxicity degree and the ground drift amount of the ground drift device at the corresponding position of the sensitive crop plants.

[0019] In some embodiments, the determining of the ground drift amount corresponding to each of the downwind distances according to the ground drift amount collection device includes:

[0020] For the ground drift amount collection device at any one of the downwind distances, elute the liquid medicine collected in the ground drift amount collection device with deionized water to obtain an eluate;

[0021] Measure and record the fluorescence value of the eluate using a fluorometer; wherein, in the plant protection UAV spray application test, the test liquid medicine with both the ABF fluorescent tracer and the test agent being 0.1%;

[0022] Determine the ground drift amount corresponding to the downwind distance according to the fluorescence value.

[0023] In some embodiments, the following formula is used to determine the ground drift amount corresponding to the downwind distance:

[0024]

[0025] where β dep is the droplet drift amount per unit area, with the unit of mL / cm 2 ; F cal is the relationship coefficient between the fluorescence value and the tracer concentration, with the unit of μg / L; V dil is the volume of the eluent added, with the unit of mL; ρ smpl is the absorbance value of the eluent; ρ blk is the fluorescence value of the blank plastic culture dish; ρ spray is the tracer concentration of the spraying liquid, with the unit of g / L; A col is the area of the plastic culture dish, with the unit of cm 2 .

[0026] In some embodiments, constructing the ground drift amount prediction model related to the downwind distance includes:

[0027] Constructing an initial ANN artificial neural network model and GWO algorithm parameters;

[0028] Taking the structural features of the initial ANN artificial neural network model as the prey, calculating the position vectors of the wolf pack; calculating the fitness corresponding to the current position vectors of the wolf pack based on the training set;

[0029] Calculating the information of the three gray wolves with the best fitness, updating their position vectors and the position vectors of the wolf pack, and performing iterative training accordingly;

[0030] When the termination condition is met, assigning the position vectors of the wolf pack in the current iteration to the ANN artificial neural network model to obtain the ground drift amount prediction model.

[0031] In some embodiments, according to the ground drift amount prediction model and the drift amount phytotoxicity detection limit, determining the buffer distance required for the spray application task to be performed to be harmless to the sensitive crop plants in the adjacent plot includes:

[0032] Using the ground drift amount prediction model to predict the spray application task to be performed, and obtaining the predicted ground drift amount of the spray application task to be performed at each downwind distance;

[0033] Comparing the predicted ground drift amounts of the spray application task to be performed at each downwind distance with the drift amount phytotoxicity detection limit, and determining the downwind distance corresponding to the drift amount matching the drift amount phytotoxicity detection limit;

[0034] Use the preset downwind distance that is adjacent to and less than the downwind distance under the foundation as the buffer distance.

[0035] In some embodiments, the detection limit of phytotoxicity is that the degree of phytotoxicity is 0-1%.

[0036] In a second aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the method described in the embodiment of the present application is implemented.

[0037] In a third aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the method described in the embodiment of the present application is implemented.

[0038] In a fourth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, the method described in the embodiment of the present application is implemented.

[0039] The method for determining the buffer distance of a plant protection UAV spray application based on the detection limit of phytotoxicity proposed in the embodiment of the present application can use the drift amount-phytotoxicity detection limit obtained by constructing the second mapping relationship between the ground drift amount and the degree of phytotoxicity in a prior experiment, and construct a ground drift amount prediction model according to the experiment, realizing the accurate prediction of the ground drift amount at different downwind distances under different characteristic parameters. Furthermore, by comparing the ground drift amount at different downwind distances with the drift amount-phytotoxicity detection limit, the buffer distance required to ensure that the adjacent plot is harmless during the spray application task to be executed is further determined, providing guidance for the safe spraying operation of the plant protection UAV.

[0040] Additional aspects and advantages of the present invention will be given in part in the following description, become apparent in part from the following description, or be understood through the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objects, and advantages of the present application will become more apparent:

[0042] Figure 1 Shows the implementation environment architecture diagram of the terminal interface recognition method provided by the embodiment of the present application;

[0043] Figure 2 Shows the structural schematic diagram of a computer system of an electronic device or a server suitable for implementing the embodiment of the present application. DETAILED DESCRIPTION

[0044] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than limiting the invention. Additionally, it should be noted that for the convenience of description, only the parts related to the invention are shown in the drawings.

[0045] It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0046] To further illustrate the technical solutions provided by the embodiments of the present application, the following will be described in detail in conjunction with the accompanying drawings and specific implementation manners. Although the embodiments of the present application provide the method operation instruction steps as shown in the following embodiments or drawings, based on routine or non-creative labor, more or fewer operation instruction steps may be included in the method. In steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. When the method is actually processed or executed by the device, it can be executed in the order shown in the embodiments or drawings or executed in parallel.

[0047] Please refer to Figure 1 , Figure 1 which shows a schematic flow chart of a method for measuring the buffer distance of a plant protection unmanned aircraft for spraying pesticides based on the detection limit of pesticide damage. As Figure 1 shown, the method includes:

[0048] Step 101, select a plurality of downwind distances based on the reference position of the test flight of the plant protection unmanned aircraft.

[0049] It should be noted that the downwind distance is the distance at the position in the downwind direction of the plant protection unmanned aircraft and at the edge of the spray width of the plant protection unmanned aircraft. Optionally, the downwind distance can be determined according to the plant protection unmanned aircraft spraying pesticide environment, such as the distance between adjacent plots, etc. Preferably, the downwind distances can be selected as 3m, 5m, 10m, 15m, 20m, 30m, and 50m, and the present application does not make specific limitations.

[0050] Step 102, place a plurality of ground drift amount collection devices along the flight direction of the plant protection unmanned aircraft at each downwind distance.

[0051] Among them, the ground drift amount collection device can be a collection container with a certain opening, such as a plastic petri dish.

[0052] Optionally, multiple ground drift amount collection devices can be continuously placed along the flight direction of the plant protection UAV. For example, they can be placed at preset intervals along the flight distance, or the corresponding placement interval can be calculated according to the preset number of placements based on the flight distance. Preferably, the preset number of placements can be 9, that is, based on the flight distance of the plant protection UAV and the preset number of placements being 9, the placement interval is calculated, and then several ground drift amount collection devices are placed simultaneously at each point according to the placement interval.

[0053] Step 103, after the plant protection UAV performs a flight based on different test parameters, obtain the ground drift fog droplets collected by the ground drift amount collection device.

[0054] That is to say, during the test, the plant protection UAV will perform multiple flights based on different test parameters, and after each flight, the ground drift fog droplets collected by the ground drift amount collection device are collected to complete the test data corresponding to the test flight.

[0055] In a feasible embodiment, the test parameters include but are not limited to flight altitude, flight speed, droplet size, average ambient wind speed, average temperature, average humidity, nozzle rotation speed, and downwind distance.

[0056] Step 104, determine the ground drift amount corresponding to each downwind distance according to the ground drift amount collection device, and generate a first mapping relationship between the downwind distance and the ground drift amount.

[0057] In a feasible embodiment, determining the ground drift amount corresponding to each downwind distance according to the ground drift amount collection device and generating a first mapping relationship between the downwind distance and the ground drift amount includes: for the ground drift amount collection device at any downwind distance, elute the liquid medicine collected in the ground drift amount collection device with deionized water to obtain an eluate, measure and record the fluorescence value of the eluate using a fluorometer. Among them, in the plant protection UAV spray application test, the test liquid medicine with both ABF fluorescent tracer and the test agent being 0.1% is used. Determine the ground drift amount corresponding to the downwind distance according to the fluorescence value.

[0058] Optionally, the following formula is used to determine the ground drift amount corresponding to the downwind distance:

[0059]

[0060] where β dep is the droplet drift amount per unit area, with the unit of mL / cm 2 ; F cal is the relationship coefficient between the fluorescence value and the tracer concentration, with the unit of μg / L; V dil is the volume of the added eluate, with the unit of mL; ρ smpl is the absorbance value of the eluate; ρblk is the fluorescence value of the blank plastic petri dish; ρ spray is the concentration of the spray liquid tracer, with the unit of g / L; A col is the area of the plastic petri dish, with the unit of cm 2 .

[0061] Step 105: Construct a ground drift prediction model related to the downwind distance under different characteristic parameters.

[0062] Specifically, it includes obtaining the historical test data of the plant protection UAV spraying test, preprocessing the historical test data to obtain the preprocessed historical test data, using the random forest algorithm to perform correlation analysis based on the preprocessed historical test data, determining at least one target characteristic parameter for spray drift prediction, and obtaining the preprocessed test parameter data corresponding to the target characteristic parameter, and constructing a training set and a test set. Then, construct an initial ANN-GWO artificial neural network model, and use the training set and the test set to train the initial ANN-GWO artificial neural network model to obtain a ground drift prediction model for spray drift prediction.

[0063] It should be noted that preprocessing the historical test data is beneficial to reducing the interference of incorrect data and improving the reliability and accuracy of the data.

[0064] In the embodiment of the present application, preprocessing the historical test data includes deleting the missing values in the historical test data. Specifically, a group of historical test data with missing values is deleted. Preferably, a group of historical test data with missing values in the test parameter data corresponding to the candidate characteristic parameters is deleted.

[0065] Furthermore, the embodiment of the present application performs normalization and standardization processing on the historical test data after missing value processing.

[0066] It should be noted that normalizing the historical test data can scale the test parameter data corresponding to multiple candidate characteristic parameters to a unified measurement range, such as [0, 1], so as to avoid abnormal weight influence in subsequent analysis due to large differences between the test parameter data. And, standardizing the historical test data can make the processed data conform to the standard normal distribution, further improving the reliability and accuracy of the data in subsequent use.

[0067] Specifically, the following formula can be used for normalization processing:

[0068]

[0069] where x i is the original historical test data to be normalized, is the normalized historical test data, whose value range is [0, 1], x max is the maximum value in the original historical test data;

[0070] The following formula is used for standardization processing:

[0071]

[0072] where μ is the mean of the normalized historical test data, σ is the standard deviation of the normalized historical test data, and x standardized is the standardized historical test data. After standardization, the historical test data conforms to the standard normal distribution, with a mean of 0 and a standard deviation of 1.

[0073] It should also be noted that when standardizing the historical test data, the test parameter data obtained after normalizing each candidate feature parameter can be standardized separately to improve the feature expression ability of each candidate feature parameter itself.

[0074] In addition, the random forest algorithm is an algorithm for judging the importance of features in decision trees. Specifically, in the random forest algorithm, the Gini index is used to calculate the importance of each feature. Among them, the Gini index is an index used to measure the impurity or uncertainty of data.

[0075] Specifically, the following formula is used to calculate the Gini index:

[0076]

[0077] where p k represents the weight of the k-th category.

[0078] Furthermore, using the random forest algorithm, correlation analysis is performed based on the preprocessed historical test data to determine at least one target feature parameter for predicting the spray drift amount, including: obtaining the difference in the Gini index of each candidate feature parameter before and after the decision tree branching, using the difference in the Gini index of each candidate feature parameter to determine the feature contribution amount of each candidate feature parameter, using the ratio of the feature contribution amount of each candidate feature parameter to the total sum of the feature contribution amounts of all candidate feature parameters to determine the contribution value of the candidate feature parameter, and taking at least one candidate feature parameter whose contribution value meets the preset conditions as the target feature parameter.

[0079] Exemplarily, for the candidate feature parameter j, the change amount of the Gini index at node m can be obtained by calculating the difference between the Gini index of the candidate feature parameter j before branching at this node and the Gini index after branching. Specifically, it can be expressed by the following formula,

[0080] VIM jm =GI m-GI l -GI r

[0081] Among them, VIM jm represents the change value of the Gini index of candidate feature parameter j on node m, and GI m represents the Gini index before branching, and GI l and GI r are the Gini indices of the two new nodes generated after node m branches.

[0082] Therefore, the feature contribution of candidate feature parameter j in decision tree i is:

[0083] VIM ij = ∑ m∈M VIM jm

[0084] If there are n decision trees in the random forest, the feature contribution of candidate feature parameter j is:

[0085]

[0086] The value obtained by normalizing the contribution of candidate feature parameter j is the contribution value of candidate feature parameter j:

[0087]

[0088] Among them, VIM' j represents the contribution of feature j obtained after normalization, represents the sum of the differences in Gini indices of all features.

[0089] It should be understood that the candidate feature parameter with a larger contribution value is considered to have a greater impact on the decision. Therefore, by calculating the Gini index of each candidate feature parameter, the importance degree of multiple candidate feature parameters in the historical test data can be analyzed, and then at least one candidate feature parameter with a higher importance degree is used as the target feature parameter.

[0090] Optionally, the N candidate feature parameters with the highest contribution values are used as the target feature parameters, or at least one candidate feature parameter with a contribution value ratio sum greater than 50% is used as the target parameter.

[0091] Preferably, when the downwind distance of the candidate feature parameter meets the selection strategy of the target feature parameter, the downwind distance is directly used as the target feature parameter. When the downwind distance of the candidate feature parameter does not meet the selection strategy of the target feature parameter, the downwind distance is simulated as the target feature parameter.

[0092] Further, obtain the preprocessed test parameter data corresponding to the target feature parameters, and construct a training set and a test set. Preferably, the preprocessed test parameter data corresponding to the target feature parameters can be divided into a training set and a test set according to a ratio of 8:2.

[0093] It should be noted that GWO (Grey Wolf Optimizer) takes the weights and biases of the neural network model as optimization variables, and performs global search and local development by simulating the hunting behavior of the grey wolf group, so as to find the optimal structure combination.

[0094] In the embodiment of the present application, different parameter combinations of the ANN artificial neural network model are randomly initialized in the exploration space through the GWO algorithm to train the ANN artificial neural network model. By iteratively updating these solutions, the optimal solution is gradually approximated to determine the best ANN artificial neural network model structure for predicting the drift amount.

[0095] In the embodiment of the present application, an initial ANN artificial neural network model needs to be constructed according to the number of target feature parameters. The GWO algorithm parameters include the number of grey wolves and the maximum number of iterations, where the number of grey wolves is greater than or equal to 3.

[0096] Before optimization, all the weights and biases of the initial ANN artificial neural network model need to be flattened into a one-dimensional vector and mapped to GWO:

[0097] D = (input_dim × hidden_dim) + hidden_dim + (hidden_dim × output_dim) + output_dim

[0098] Where input_dim is the number of input target feature parameters, hidden_dim is the number of neurons in each layer, and output_dim is the number of output results.

[0099] Preferably, a position vector of the initial wolf pack is randomly generated. Among them, the position vector of each grey wolf in the wolf pack corresponds to a set of parameters of the ANN artificial neural network model.

[0100] Further, calculate the fitness corresponding to the position vector of each grey wolf in the wolf pack.

[0101] It should be noted that the fitness is used to evaluate the difference between the predicted value generated by the ANN artificial neural network model corresponding to the current wolf pack position vector based on the training set and the actual value corresponding to the training set. Among them, the fitness can adopt the coefficient of determination R 2 , root mean square error RMSE, mean absolute error MAE, etc.

[0102] In the embodiments of the present application, since it is necessary to make a prediction according to the target downwind distance during the subsequent prediction using the model, that is, to focus on the input of the downwind distance characteristic parameter, therefore, during the model training, it is necessary to at least achieve the attention to the downwind distance and take into account other target characteristic parameters at the same time.

[0103] Preferably, in the embodiments of the present application, the improved root mean square error is used to calculate the fitness corresponding to the current wolf pack position vector.

[0104] Specifically, the present application adopts a multi-objective optimization strategy, that is, the balance solution is selected through the Pareto front to achieve the simultaneous optimization of the model performance and the feature weight distribution. The fitness function is:

[0105]

[0106] where n is the number of training times, ω i is the feature weight, O i is the drift amount data corresponding in the training set, and P i is the prediction result according to the test parameter data in the training set.

[0107] It should be noted that the optimization objective of the fitness function is to minimize the model error (root mean square error RMSE) and maximize the uniformity of the feature weights at the same time, so as to ensure that the ANN artificial neural network model can effectively take into account the influence of other target characteristic parameters on the spray drift amount while paying sufficient attention to the downwind distance of the target characteristic parameters, so as to ensure that when predicting the spray drift amount of the target downwind distance later, the drift amount of the target downwind distance can be predicted relying on the uniform weights of other target characteristic parameters.

[0108] In the embodiments of the present application, the preset maximum number of iterations of GWO is used as the training termination condition. Then, the wolf position vector with the optimal fitness is assigned to the ANN artificial neural network model to obtain the ground drift amount prediction model. Among them, the ground drift amount prediction model has multiple input ends, which correspond one by one to the number of target characteristic parameters.

[0109] Step 106, obtain the second mapping relationship between the phytotoxicity degree of the sensitive crop plants in the adjacent plot and the drift amount of the ground drift device at the corresponding position, and determine the drift amount phytotoxicity detection limit corresponding to the sensitive crop plants.

[0110] It should be noted that the sensitive crop can be the plant type planted in the adjacent plot in the current season, that is to say, the crop type to be planted or actually planted in the adjacent plot can be used as the sensitive crop to understand the true sensitive reaction of the crop plants to the liquid medicine during the test process.

[0111] In a feasible embodiment, sensitive crop plants in adjacent plots are placed at different downwind distances, and ground drift devices are placed at the corresponding positions of the plants to conduct a phytotoxicity test on the sensitive crop plants, obtaining the sensitive crop plants after the test and the ground drift amount at the corresponding positions; all the leaves of the sensitive crop plants after the test are picked and made into leaf patterns; the phytotoxic pixel features and leaf pixel features corresponding to the leaf images are obtained, and based on the phytotoxic pixel features and leaf pixel features, the yellowing ratio corresponding to the sensitive crop plants after the test is determined, and based on the ground drift amount and the yellowing ratio corresponding to the sensitive crop plants after the test, a second mapping relationship between the phytotoxicity degree and the ground drift amount of the ground drift device at the corresponding position of the sensitive crop is generated.

[0112] Specifically, the sensitive crop plants after the test are placed in a non-interference room and stored for 7 days to enable the sensitive crop plants after the test to fully react after being treated with the drug. Then, the leaves of the sensitive crop plants are picked and pasted on A4 paper to form leaf patterns. Among them, each leaf can be made into a leaf pattern separately to improve the accuracy and reliability of subsequent image processing.

[0113] After obtaining the leaf patterns, using an image processing algorithm, image processing is performed on the leaf images to extract the phytotoxic pixel features and leaf pixel features from the leaf patterns. Specifically, image segmentation is performed on each leaf image respectively using RGB thresholds to obtain the phytotoxic pixel features of the phytotoxic part and the leaf pixel features of the non-phytotoxic part. Among them, the phytotoxic pixel features are the pixel feature regions where the RGB color values correspond to yellow, including but not limited to various degrees of yellow pixel regions, and the leaf pixel features are the non-white regions in the leaf patterns, that is, the regions on the entire A4 paper except for the white paper are the leaf pixel feature regions.

[0114] Furthermore, by calculating the proportion of the phytotoxic pixel features in the leaf pixel features, the yellowing ratio corresponding to the sensitive crop plants after the test is determined. Optionally, the complete leaf area can be calculated first using the leaf pixel features, and then the corresponding yellowing ratio can be calculated using the number of phytotoxic pixel features and the leaf area. Among them, the yellowing ratio is used as a quantitative analysis index for the phytotoxicity degree of the sensitive crop plants. Optionally, the yellowing ratio is proportional to the phytotoxicity degree. Exemplarily, the sensitive crop plants after the test with a yellowing ratio of 99.9 - 100% have a higher phytotoxicity degree, and the sensitive crop plants after the test with a yellowing ratio of 0 - 1% have a lower phytotoxicity degree.

[0115] The ground drift device at the corresponding position of the sensitive crop is eluted, and its corresponding ground drift amount is obtained through calculation. The average value of the drift amounts corresponding to the sensitive crop plants after the test with a yellowing ratio of 0 - 1% is the drift amount phytotoxicity detection limit of the sensitive crop.

[0116] That is, the phytotoxicity detection limit is a phytotoxicity degree of 0 - 1%.

[0117] Step 107: Determine the buffer distance required for the spray application task to be executed to be harmless to the sensitive crop plants in adjacent plots according to the ground drift amount prediction model and the drift amount phytotoxicity detection limit.

[0118] In a feasible embodiment, the spray application task to be executed is predicted by using the ground drift amount prediction model to obtain the predicted ground drift amount of the spray application task to be executed at each downwind distance. The predicted ground drift amount of the spray application task to be executed at each downwind distance is compared with the drift amount phytotoxicity detection limit to determine the downwind distance corresponding to the drift amount matching under the basis of the drift amount phytotoxicity detection limit. The preset downwind distance that is adjacent to and less than the downwind distance under the basis is used as the buffer distance.

[0119] That is to say, after determining the parameters corresponding to the spray application task to be executed, the ground drift amount at each downwind distance under this parameter combination can be predicted. Then, according to the ground drift amount prediction model and the drift amount phytotoxicity detection limit, the phytotoxicity degree corresponding to each ground drift amount is obtained. The phytotoxicity degree corresponding to each ground drift amount is respectively compared with the phytotoxicity degree corresponding to the drift amount phytotoxicity detection limit. Preferably, starting from the ground drift amount corresponding to the minimum downwind distance, the phytotoxicity degree mapped by the ground drift amount corresponding to each downwind distance is compared with the phytotoxicity degree of the drift amount phytotoxicity detection limit in turn. The first downwind distance less than the drift amount phytotoxicity detection limit is used as the buffer distance corresponding to the task to be executed.

[0120] Therefore, the method for determining the buffer distance for spraying and applying pesticides by a plant protection UAV based on the phytotoxicity detection limit proposed in the embodiment of the present application can use prior experiments to construct a second mapping relationship between the ground drift amount and the phytotoxicity degree, and construct a ground drift amount prediction model according to the experiments, realizing accurate prediction of the ground drift amount at different downwind distances under different characteristic parameters. Furthermore, by comparing the ground drift amount at different downwind distances with the drift amount phytotoxicity detection limit, the buffer distance required for the spray application task to be executed to be harmless to adjacent plots is further determined, providing guidance for the safe pesticide application operation of the plant protection UAV.

[0121] It should be noted that although the operations of the method of the present invention are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result.

[0122] Next, refer to Figure 2 , Figure 2 which shows a schematic structural diagram of a computer system of an electronic device or a server suitable for implementing the embodiments of the present application.

[0123] As Figure 2As shown, the computer system includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 202 or a program loaded from a storage section 208 into a random access memory (RAM) 203. In the RAM 203, various programs and data required for the operation instructions of the system are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0124] The following components are connected to the I / O interface 205; an input section 206 including a keyboard, a mouse, etc.; an output section 207 including, for example, a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN card, a modem, etc. The communication section 209 performs communication processing via a network such as the Internet. A drive 210 is also connected to the I / O interface 205 as needed. A removable medium 211, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 210 as needed so that a computer program read from it can be installed into the storage section 208 as needed.

[0125] Specifically, according to an embodiment of the present application, the process described above with reference to the flowchart Figure 2 can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program includes program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 209, and / or installed from the removable medium 211. When the computer program is executed by the central processing unit (CPU) 201, the above functions defined in the system of the present application are executed.

[0126] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0127] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operation instructions of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram can represent a module, a program segment, or a part of code, and the foregoing module, program segment, or part of code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two connected blocks can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for executing the specified functions or operation instructions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0128] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist separately without being assembled into the electronic device. The above computer-readable storage medium stores one or more programs, and when the above programs are executed by one or more processors, they are used to implement the method for measuring the buffer distance of spraying pesticides by a plant protection UAV based on the detection limit of pesticide damage described in the present application.

[0129] The above description is only the preferred embodiment of the present application and an explanation of the technical principles applied. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the foregoing disclosure concept. For example, the technical solutions formed by mutually replacing the above features with the technical features (but not limited to) having similar functions disclosed in the present application.

Claims

1. A method for determining the spray buffer distance of a plant protection unmanned aircraft based on the detection limit of pesticide injury, characterized in that, Including: Based on the reference position of the unmanned aerial vehicle (UAV) for plant protection test flight, multiple downwind distances are selected; Along the flight direction of the UAV, multiple ground drift amount collection devices are placed at each of the downwind distances; After the UAV performs flight based on different test parameters, the ground drift droplets collected by the ground drift amount collection devices are obtained; According to the ground drift amount collection devices, the ground drift amounts corresponding to the respective downwind distances are determined, and a first mapping relationship between the downwind distances and the ground drift amounts is generated; A ground drift amount prediction model related to the downwind distance under different characteristic parameters is constructed; A second mapping relationship between the phytotoxicity degree of sensitive crop plants in adjacent plots and the drift amount of the ground drift device at the corresponding position is obtained, and the drift amount phytotoxicity detection limit corresponding to the sensitive crop plants is determined; According to the ground drift amount prediction model and the drift amount phytotoxicity detection limit, the buffer distance required for the spray application task to be performed to be harmless to the sensitive crop plants in adjacent plots is determined.

2. The method for determining the buffer distance of spray application of a plant protection unmanned aircraft based on the detection limit of pesticide injury as claimed in claim 1, wherein The obtaining of the second mapping relationship between the phytotoxicity degree of sensitive crop plants in adjacent plots and the drift amount of the ground drift device at the corresponding position includes: The sensitive crop plants in adjacent plots are placed at different downwind distances, and a ground drift device is placed at the corresponding position of the plants to conduct a phytotoxicity test on the sensitive crop plants, and the sensitive crop plants and the corresponding ground drift amounts after the test are obtained; All the leaves of the sensitive crop plants after the test are picked and made into leaf patterns; The phytotoxicity pixel features and leaf pixel features corresponding to the leaf patterns are obtained; Based on the phytotoxicity pixel features and the leaf pixel features, the yellowing ratio corresponding to the sensitive crop plants after the test is determined; Based on the ground drift amount corresponding to the sensitive crop plants after the test and the yellowing ratio, a second mapping relationship between the phytotoxicity degree and the ground drift amount of the ground drift device at the corresponding position of the sensitive crop plants is generated.

3. The method for determining the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide injury, as claimed in claim 1, is characterized in that The determining of the ground drift amounts corresponding to the respective downwind distances according to the ground drift amount collection devices includes: For the ground drift amount collection device at any one of the downwind distances, the liquid medicine collected in the ground drift amount collection device is eluted with deionized water to obtain an eluate; The fluorescence value of the eluate is measured and recorded using a fluorometer; wherein, in the UAV spray application test, the test liquid medicine with both the ABF fluorescence tracer and the test agent being 0.1% is used; According to the fluorescence value, the ground drift amount corresponding to the downwind distance is determined.

4. The method for determining the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage according to claim 3, characterized in that, The following formula is used to determine the ground drift amount corresponding to the downwind distance: Among them, β dep is the amount of droplet drift per unit area, with the unit of mL / cm 2 ; F cal is the relationship coefficient between the fluorescence value and the tracer concentration, with the unit of μg / L; V dil is the volume of the eluent added, with the unit of mL; ρ smpl is the absorbance value of the eluent; ρ blk is the fluorescence value of the blank plastic petri dish; ρ spray is the tracer concentration of the spraying solution, with the unit of g / L; A col is the area of the plastic petri dish, with the unit of cm 2 .

5. The method for determining the buffer distance of spray application of a plant protection unmanned aircraft based on the detection limit of pesticide damage according to claim 1, characterized in that, The constructing of the ground drift amount prediction model related to the downwind distance includes: An initial artificial neural network (ANN) model and grey wolf optimization (GWO) algorithm parameters are constructed; Taking the structural features of the initial ANN model as prey, the position vectors of the wolf pack are calculated; based on the training set, the fitness corresponding to the current position vectors of the wolf pack is calculated; The information of the three grey wolves with the optimal fitness is calculated, and their position vectors and the position vectors of the wolf pack are updated, and iterative training is carried out accordingly; When the termination condition is satisfied, the position vectors of the wolf packs in the current iteration round are assigned to the ANN artificial neural network model to obtain the ground drift amount prediction model.

6. The method for determining the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage as claimed in claim 1, wherein Determining the buffer distance required for the spray application task to be harmless to the sensitive crop plants in the adjacent plot according to the ground drift amount prediction model and the drift amount phytotoxicity detection limit includes: Using the ground drift amount prediction model to predict the spray application task to be executed, and obtaining the predicted ground drift amount of the spray application task to be executed at each downwind distance; Comparing the predicted ground drift amount of the spray application task to be executed at each downwind distance with the drift amount phytotoxicity detection limit, and determining the downwind distance corresponding to the drift amount match with the drift amount phytotoxicity detection limit; Taking the preset downwind distance adjacent to and less than the downwind distance of the base as the buffer distance.

7. The method for determining the buffer distance of spraying pesticides by a plant protection unmanned aircraft based on the detection limit of pesticide damage according to claim 6, characterized in that The phytotoxicity detection limit is that the degree of phytotoxicity is 0-1%.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for determining the buffer distance for the spraying operation of the plant protection UAV based on the phytotoxicity detection limit as described in any one of claims 1-7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method for determining the buffer distance for the spraying operation of the plant protection UAV based on the phytotoxicity detection limit as described in any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for determining the buffer distance for the spraying operation of the plant protection UAV based on the phytotoxicity detection limit as described in any one of claims 1-7.

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