Peanut pest monitoring management method and planting management system

Abstract

The invention discloses a peanut pest monitoring management method and a planting management system, and relates to the technical field of agricultural management. The method comprises the following steps: uniformly dividing a monitoring management range into a plurality of monitoring grids, and selecting peanut plants in each monitoring grid as sample plants; performing insect pest type identification on the sample plants in each stage of peanut growth to obtain the probability that each sample plant belongs to each insect pest type; according to the position of the sample plant in the monitoring grid and the probability that each sample plant belongs to each pest type, analyzing to obtain a distribution area of each pest type in the monitoring management range; obtaining the pest type of each peanut plant according to the distribution area of each pest type in the monitoring management range and the position of each peanut plant; judging whether the insect pest type of the peanut plant is no insect pest; if yes, no processing is carried out; and if not, performing field insect pest treatment according to the corresponding insect pest treatment plan. The insect pest planting management accuracy and efficiency are improved.

Description

Technical Field

[0001] The invention belongs to the technical field of agricultural management, and in particular relates to a peanut pest monitoring and management method and a planting management system. Background Art

[0002] Peanuts are very susceptible to a variety of pests during their growth, such as underground pests such as white grubs and cutworms, as well as ground pests such as aphids and red spiders. These pests not only directly harm the roots, stems, leaves, and fruits of peanuts, resulting in reduced or even total crop failure, but also spread diseases, further exacerbating losses.

[0003] Traditional peanut pest monitoring mainly relies on manual field surveys, which have problems such as low efficiency, poor timeliness and low accuracy, and it is difficult to meet the needs of modern precision agriculture. Summary of the invention

[0004] The purpose of the present invention is to provide a peanut pest monitoring and management method and a planting management system to improve the accuracy and efficiency of pest planting management.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions: The present invention provides a peanut pest monitoring and management method, comprising: The monitoring management area is evenly divided into multiple monitoring grids, and a peanut plant is selected as a sample plant in each monitoring grid; Obtain the pest type identification results of sample plants at each stage of peanut growth to obtain the probability that each sample plant belongs to each pest type; According to the location of each sample plant in the monitoring grid and the probability analysis of each sample plant belonging to each pest type, the distribution area of ​​each pest type within the monitoring and management scope is obtained; The pest type of each peanut plant is obtained according to the distribution area of ​​each pest type and the location of each peanut plant within the monitoring and management scope; Determine whether the pest type of the peanut plant is pest-free; If so, no processing is performed; If not, carry out field pest control according to the corresponding pest management plan.

[0006] The present invention also discloses a peanut pest monitoring and planting management system, comprising: The identification and input unit is used for each staff member to input the identified pest type of each sample plant after the pest type is identified on several parts of the sample plant; A monitoring management unit, used to evenly divide the monitoring management range into a plurality of monitoring grids, and select a peanut plant in each monitoring grid as a sample plant; Obtain the pest type identification results of sample plants at each stage of peanut growth to obtain the probability that each sample plant belongs to each pest type; According to the location of each sample plant in the monitoring grid and the probability analysis of each sample plant belonging to each pest type, the distribution area of ​​each pest type within the monitoring and management scope is obtained; The pest type of each peanut plant is obtained according to the distribution area of ​​each pest type and the location of each peanut plant within the monitoring and management scope; A governance management unit is used to determine whether the pest type of the peanut plant is pest-free; If so, no processing is performed; If not, carry out field pest control according to the corresponding pest management plan.

[0007] The present invention obtains the accurate distribution area of ​​each pest type within the monitoring and management range by analyzing and comparing the preliminary pest identification status of a small number of sample plants by the monitoring and management unit, thereby realizing automatic and rapid detection and identification of pest types of all peanut plants within the monitoring and management range, and fully improving the efficiency of pest planting management accuracy.

[0008] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0010] Figure 1 A schematic diagram of the functional units and information flow of a peanut pest monitoring and planting management system according to an embodiment of the present invention; Figure 2 A schematic diagram of a step flow of a monitoring management unit and a governance management unit according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the step flow of step S2 in one embodiment of the present invention; Figure 4 This is a schematic diagram of the step flow of step S3 in one embodiment of the present invention; Figure 5 This is a schematic diagram of the step flow of step S31 in one embodiment of the present invention; Figure 6 This is a schematic diagram of a step flow chart of step S32 in an embodiment of the present invention; Figure 7This is a schematic diagram of the step flow of step S4 in one embodiment of the present invention; Figure 8 This is a schematic diagram of a step flow chart of step S42 in an embodiment of the present invention; In the accompanying drawings, the components represented by the reference numerals are listed as follows: 1-Identification and entry unit, 2-Monitoring and management unit, 3-Governance and management unit. DETAILED DESCRIPTION

[0011] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0012] It should be noted that the terms "first", "second", etc. in this application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0013] See also Figures 1 to 2 As shown, the present invention provides a peanut pest monitoring and planting management system, which is divided into an identification and entry unit 1, a monitoring and management unit 2, and a governance and management unit 3 in terms of functional units. The identification and entry unit 1 in the system can be a hardware entity or an application installed on a mobile phone or computer, and is used in step S01 for a staff member to enter the identified pest type of each sample plant, or the staff member can manually identify the pest type of the sample plant and then enter it.

[0014] The parts of peanut plants include stems, leaves and flowers. The stems, leaves and flowers of peanut plants are visible parts, and there are also buried parts such as roots, fruits, and seeds. In the process of identifying pests and diseases of peanut plants, the results of identifying different parts of different plants may be different, and the results of visual identification or identification by means of intelligent comparison, genetic testing, etc. by different staff may also be different. The identification and entry unit 1 can execute step S01 to obtain the pest type of the sample plant by the staff. The insect types include no pests, peanut aphids, leaf rollers, tea yellow mites, beet armyworms, field spider mites, white grubs, armyworms, cotton bollworms, thrips and / or peanut spider scales.

[0015] The technical means used by staff in pest identification are becoming increasingly diverse, combining traditional methods with modern technology. It can be visual inspection by staff, that is, empirical judgment by observing the morphology of pests (color, size, antennae, etc.) and symptoms of damage (boring holes, excrement, leaf defects, etc.), and microscopic identification can be combined during the process. It can also be the use of digital intelligent identification technology, such as taking photos of pests and automatically comparing them with databases through AI algorithms, such as iNaturalist, Plantix, Google Lens, etc. for identification, and the "Scan Farmer" app and Tencent AI Lab's pest and disease identification tools can also be used. Molecular biology technology can also be used to extract pest DNA fragments (such as COI genes) and compare them with databases to achieve accurate species identification.

[0016] Visual inspection and digital intelligent identification by staff usually consider the damage forms caused by different pests to peanut plants. For example, when peanuts are in the state of being infested by peanut aphids, the leaves will turn yellow and curl due to the aphids sucking the juice, and the stem growth and flower development will also be affected. When peanuts are in the state of being infested by leaf rollers, the larvae spin silk to roll the leaves into buds and eat the mesophyll, causing the leaves to shrink or wither. In severe cases, the upper leaves turn white, affecting the pod formation of the flowers. When peanuts are in the state of being infested by tea yellow mites, adult mites and young mites concentrate on young buds, tender leaves, flowers and young fruits to suck juice, causing the leaves to thicken and become stiff, and the leaf edges to curl. In severe cases, it affects flowering and fruiting. When peanuts are in the state of being infested by beet armyworms, the larvae eat the leaves, causing notches or holes in the leaves. In severe cases, the leaves are eaten up, leaving only the petioles and veins, reducing the yield. When peanuts are infested by field spider mites, they mainly harm the quality and yield of peanuts, and suck the sap, causing poor plant growth. When peanuts are infested by white grubs, the larvae harm the fruit needles and young fruits, causing the plants to be short, the leaves to turn yellow, and in severe cases, the plants to die. When peanuts are infested by fall armyworms, the larvae eat the leaves, causing notches or holes in the leaves, and in severe cases, they eat all the leaves. When peanuts are infested by cotton bollworms, the larvae harm the leaves and pods of peanuts, affecting yield. When peanuts are infested by thrips, they suck the sap from the tender leaves and inflorescences of peanuts, causing leaf deformation and poor growth. When peanuts are infested by new spider scales, the larvae drill into the root bark to suck the sap, causing the plants to be short, the leaves to turn yellow, and in severe cases, the plants to die.

[0017] Please continue reading Figures 1 to 3 As shown, since the results of pest identification on different parts of the peanut plant by different staff members during the operation of the identification and entry unit 1 may be different, after executing step S21 to obtain the pest type of the sample plant obtained by each staff member through pest identification on each part of the sample plant, it is also necessary to execute step S22 to obtain the proportion of the sample plants belonging to each pest type as the probability that the sample plants belong to each pest type.

[0018] Please continue reading Figures 4 to 5 As shown, after obtaining the preliminary results of pest identification of the identification entry unit 1, since the pest identification results for a single peanut plant, that is, the preliminary identification results, may not be completely accurate, it is necessary to combine the position of each target plant and the identification results for auxiliary judgment. Therefore, step S3 can be executed next to obtain the distribution area of ​​each pest type within the monitoring and management scope according to the position of the monitoring grid where each sample plant is located and the probability analysis of each sample plant belonging to each pest type. Specifically, step S31 can be executed first to select a reference sample plant from all the sample plants. Specifically, step S311 can be executed first to use the pest type with a probability of not 0 among the pest types to which all the sample plants belong as the suspected pest type. Next, step S312 can be executed for each suspected pest type, and the sample plant corresponding to the maximum probability value of the pest type among all the sample plants is used as the reference sample plant.

[0019] See also Figures 2 to 6 As shown, since pests are contagious, pests of peanut plants usually occur in clusters. Although the pest identification results of a single peanut plant may not be accurate, the summary analysis of the identification results of many peanut plants is reliable. Therefore, the distribution area of ​​each pest type can be assisted by the location distribution of the plant. Therefore, step S32 can be executed next to obtain the distribution area of ​​each pest type within the detection management range according to the difference in the probability of each pest type between the reference target plant and other target plants. Specifically, step S321 can be executed first to take the cumulative value of the absolute value of the probability difference on each pest type between the target plants as the pest identification difference between the target plants, and calculate the pest identification difference between each reference target plant and each other target plant. Next, step S322 can be executed to divide the other target plants other than each reference target plant and the reference target plant with the smallest pest identification difference into the same distribution area. Next, step S323 can be executed to calculate the average probability of all target plants in each distribution area for each pest type. Next, step S324 may be executed to take the several pest types with average probabilities greater than the set warning value as the pest types of the distribution area, and obtain the distribution areas of each pest type within the detection and management range.

[0020] Please continue reading Figure 5 and 6As shown, the pest types of plants in the distribution areas divided in the above steps may not be consistent, so it is necessary to perform inspection and judgment. In the inspection process, step S325 can be first executed to determine whether the target plant with the smallest pest identification difference between each distribution area and the average probability of all target plants in the distribution area on each pest type is the reference target plant in the distribution area. If so, it means that the pest types of peanut plants in each distribution area are consistent, so step S326 can be executed next to obtain the distribution areas of each pest type within the detection management range. If not, it means that the pest types of peanut plants in the distribution area are inconsistent, so step S327 can be executed next to reselect the reference target plant; calculate and obtain the target plant with the smallest pest identification difference between each distribution area and the average probability of all target plants in the distribution area on each pest type as the reselected reference target plant. In order to make the pest types of peanut plants in each distribution area consistent, the division method of the distribution area can be continuously optimized by iterative calculation and judgment, that is, return to execute steps S322 to S325 to recalculate and divide the updated distribution area according to the reselected reference target plant, and re-judge whether the target plant with the smallest pest identification difference between each distribution area and the average probability of all target plants in the distribution area for each pest type is the reference target plant in the distribution area. The above process is continuously iterated and optimized to obtain a distribution area containing consistent peanut plant pest types.

[0021] In order to supplement the implementation process of the above steps S321 to S327, the source code of some functional modules is provided, and the explanation is compared in the comment section. In order to avoid the leakage of data involving commercial secrets, some data that does not affect the implementation of the solution are desensitized, the same below.

[0022] #include <iostream> #include <vector> #include <map> #include <cmath> #include <algorithm> #include <limits> using namespace std; / / Define pest type enumeration enum PestType { NO_PEST, / / No pests APHID, / / Peanut aphid LEAF_ROLLER, / / Leaf Roller TEA_MITE, / / Tea yellow mite BEET_ARMYWORM, / / Beet Armyworm FIELD_MITE, / / Field spider mite GRUB, / / Grub SPODOPTERA, / / Spodoptera litura BOLLWORM, / / bollworm THRIPS, / / Thrips NEW_SPIDER_MITE / / Peanut's new spider mite }; / / Define the target plant structure struct TargetPlant { int id; / / Plant ID map<PestType, double> pestProbs; / / Probability of each pest type }; / / Calculate the difference in pest identification double calculatePestDifference(const map<PestType, double> &probs1,const map<PestType, double> &probs2) { double difference = 0.0; for (const auto&prob : probs1) { difference += abs(prob.second - probs2.at(prob.first)); } return difference; } / / Divide the distribution area map <int, vector <targetplant>>divideDistributionRegions(const vector <targetplant>&plants, const vector <targetplant>&landmarks) { map<int, vector <targetplant>>regions; / / Traverse all target plants for (const auto&plant : plants) { double minDifference = numeric_limits <double>::max(); int closestLandmarkId = -1; / / Find the reference target plant with the smallest difference in pest identification with the current plant for (const auto&landmark : landmarks) { double difference = calculatePestDifference(plant.pestProbs,landmark.pestProbs); if (difference <minDifference) { minDifference = difference; closestLandmarkId = landmark.id; } } / / Divide the current plant into the corresponding distribution area regions[closestLandmarkId].push_back(plant); } return regions; } / / Calculate the average probability of the distribution area map<PestType, double> calculateAverageProbabilities(const vector <targetplant>&plants) map<PestType, double> avgProbs; map<PestType, int> count; / / Initialize average probability and count for (const auto&plant : plants) { for (const auto&prob : plant.pestProbs) { avgProbs[prob.first] += prob.second; count[prob.first]++; } } / / Calculate the average probability for (auto&prob : avgProbs) { prob.second / = count[prob.first]; } return avgProbs; } / / Determine whether the reference target plant needs to be updated bool needUpdateLandmarks(const map <int, vector <targetplant>>®ions,const vector <targetplant>&landmarks) for (const auto®ion : regions) { int landmarkId = region.first; const auto&plants = region.second; / / Calculate the average probability of the current distribution area auto avgProbs = calculateAverageProbabilities(plants); / / Find the target plant with the smallest difference from the average probability double minDifference = numeric_limits <double>::max(); int closestPlantId = -1; for (const auto&plant : plants) { double difference = calculatePestDifference(plant.pestProbs,avgProbs); if (difference <minDifference) { minDifference = difference; closestPlantId = plant.id; } } / / If the target plant with the smallest difference is not the current reference target plant, it needs to be updated if (closestPlantId != landmarkId) { return true; } } return false; } / / Update the reference target plant vector <targetplant>updateLandmarks(const map<int, vector <targetplant>>®ions) { vector <targetplant>newLandmarks; for (const auto®ion : regions) { const auto&plants = region.second; / / Calculate the average probability of the current distribution area auto avgProbs = calculateAverageProbabilities(plants); / / Find the target plant with the smallest difference from the average probability double minDifference = numeric_limits <double>::max(); TargetPlant closestPlant; for (const auto&plant : plants) { double difference = calculatePestDifference(plant.pestProbs,avgProbs); if (difference <minDifference) { minDifference = difference; closestPlant = plant; } } / / Take the target plant with the smallest difference as the new reference target plant newLandmarks.push_back(closestPlant); } return newLandmarks; } int main() { / / 1. The example has obtained the target plant data and reference target plant vector <targetplant>targetPlants; for (int i = 0; i<10; ++i) { TargetPlant plant; plant.id = i; plant.pestProbs[APHID] = static_cast <double>(rand()) / RAND_MAX; plant.pestProbs[LEAF_ROLLER] = static_cast <double>(rand()) / RAND_MAX; plant.pestProbs[TEA_MITE] = static_cast <double>(rand()) / RAND_MAX; targetPlants.push_back(plant); } vector <targetplant>landmarks = {targetPlants[0], targetPlants[5]}; / / Example reference target plants / / 2. Divide the initial distribution area auto regions = divideDistributionRegions(targetPlants, landmarks); / / 3. Iteratively optimize the distribution area while (needUpdateLandmarks(regions, landmarks)) { landmarks = updateLandmarks(regions); regions = divideDistributionRegions(targetPlants, landmarks); } / / 4. Output the final distribution area cout << "Final distribution area division result: " << endl; for (const auto& region : regions) { cout << "Reference target plant ID: " << region.first << ", included plant IDs: "; for (const auto& plant : region.second) { cout << plant.id << " "; } cout << endl; } return 0; } Function summary: This code implements the function of dividing the pest distribution area according to the difference in pest identification between the reference target plant and other target plants, and optimizing the distribution area through iteration. First, the pest identification difference is calculated, and the cumulative value of the absolute value of the difference in the probability of pest types between the target plants is calculated as the pest identification difference. Then the distribution area is divided, and each target plant is divided into the distribution area where the reference target plant with the smallest difference in pest identification is located. Next, the average probability is calculated, and the average probability of the target plant in each distribution area for each pest type is calculated. Then, it is determined whether the reference target plant needs to be updated. If the target plant with the smallest difference in the average probability in a distribution area is not the current reference target plant, the reference target plant needs to be updated. Finally, the distribution area is optimized iteratively by updating the reference target plant and re-dividing the distribution area until the distribution area is stable.

[0023] The above code is developed through a detailed algorithm to achieve dynamic division and optimization of pest distribution areas, providing a scientific basis for pest monitoring and management.

[0024] See also Figure 2 , 7 As shown in Figure 8, since the accuracy of the output result of the identification and input unit 1 may not meet the requirements, in view of this, the pest type of the distribution area where the peanut plant is located can be considered for auxiliary judgment, that is, step S4 is executed to obtain the pest type of each peanut plant according to the distribution area of ​​each pest type within the monitoring and management scope and the position of each peanut plant. Specifically, step S41 can be executed first to obtain the pest identification setting accuracy. Next, step S42 can be executed to determine whether it meets the pest identification setting accuracy based on the probability of each target plant in each pest type. In the process of determining whether the identification and input unit 1 is sufficiently accurate in step S42, step S421 can be first executed to calculate the difference between the maximum probability value and the second largest probability value of the sample plant in each pest type as the pest type probability gradient value of the sample plant. Next, step S422 can be executed to use the ratio of the pest type probability gradient value of the sample plant to the maximum probability as the pest identification accuracy of the sample plant. Then step S423 can be executed to determine whether the pest identification accuracy of the sample plant is greater than or equal to the pest identification setting accuracy. If yes, step S424 can be executed to determine that the probability of the sample plant on each pest type meets the accuracy set for pest identification. If no, step S425 can be executed to determine that the probability of the sample plant on each pest type does not meet the accuracy set for pest identification.

[0025] If the probability of the sample plant on each pest type meets the accuracy set for pest identification, it means that the result of pest identification is directly usable, so step S43 can be executed next to output the pest type that meets the accuracy set for pest identification. If the probability of the sample plant on each pest type does not meet the accuracy set for pest identification, it means that the result of pest identification is not credible, so the pest status of the distribution area where it is located can be used as the pest status of the peanut plant, that is, step S44 is executed to use the pest type corresponding to the distribution area where the target plant's position falls as the pest type of the target plant.

[0026] Please continue reading Figures 1 to 3 As shown, the governance management unit 3 in the system can be an intelligent terminal guided by the monitoring management unit 2, which is received by the field management personnel and operated. During the operation, step S5 can be first executed to determine whether the pest type of the peanut plant is pest-free. If so, step S5 can be executed without processing. Otherwise, step S7 can be executed next to carry out field pest control according to the corresponding pest disposal plan. The specific means can be to use lights, color plates, etc. to lure and kill pests, or to select high-efficiency and low-toxic pesticides, such as imidacloprid and cypermethrin, to kill pests by spraying, root irrigation, etc.

[0027] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the device, system, method and computer program product according to multiple embodiments of the present application. In this regard, each square frame in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the logical function of the specification. In some alternative implementations, the functions marked in the square frame can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square frames can actually be executed substantially in parallel, and they can also be executed in reverse order sometimes, depending on the functions involved.

[0028] It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart, can be implemented by hardware that performs the corresponding function or action, such as a circuit or ASIC (Application Specific Integrated Circuit), or can be implemented by a combination of hardware and software, such as firmware.

[0029] Although the present invention is described herein in conjunction with various embodiments, in the process of implementing the claimed invention, those skilled in the art may understand and implement other variations of the disclosed embodiments by viewing the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple situations. A single processor or other unit may implement several functions listed in the claims. Certain measures are recorded in different dependent claims, but this does not mean that these measures cannot be combined to produce good results.

[0030] The embodiments of the present application have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes are obvious to those of ordinary skill in the art without departing from the scope of the described embodiments. The selection of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein.< / targetplant> < / double> < / double> < / double> < / targetplant> < / double> < / targetplant> < / targetplant> < / targetplant> < / double> < / targetplant> < / targetplant> < / targetplant> < / double> < / targetplant> < / targetplant> < / targetplant> < / targetplant> < / limits> < / algorithm> < / cmath> < / map> < / vector> < / iostream>

Claims

1. A peanut pest monitoring and management method, characterized in that: include, The monitoring management area is evenly divided into multiple monitoring grids, and a peanut plant is selected as a sample plant in each monitoring grid; Obtain the pest type identification results of sample plants at each stage of peanut growth to obtain the probability that each sample plant belongs to each pest type; According to the location of each sample plant in the monitoring grid and the probability analysis of each sample plant belonging to each pest type, the distribution area of ​​each pest type within the monitoring and management scope is obtained; The pest type of each peanut plant is obtained according to the distribution area of ​​each pest type and the location of each peanut plant within the monitoring and management scope; Determine whether the pest type of the peanut plant is pest-free; If so, no processing is performed; If not, carry out field pest control according to the corresponding pest management plan.

2. The method according to claim 1, characterized in that The step of obtaining the pest type identification results of the sample plants at each stage of peanut growth to obtain the probability that each sample plant belongs to each pest type, include, Obtaining the pest type of the sample plant obtained by performing pest identification on each part of the sample plant each time, wherein the parts of the peanut plant include stems, leaves and / or flowers; The proportion of sample plants belonging to each pest type is obtained as the probability that the sample plants belong to each pest type, wherein the pest types include no pest, peanut aphids, leaf rollers, tea yellow mites, beet armyworms, field spider mites, white grubs, Spodoptera litura, cotton bollworms, thrips and / or peanut spider scales.

3. The method according to claim 1 or 2, characterized in that: The step of obtaining the distribution area of ​​each pest type within the monitoring management scope according to the location of each sample plant in the monitoring grid and the probability analysis of each sample plant belonging to each pest type includes: Selecting a number of sample plants from all the sample plants as reference sample plants; The distribution areas of each pest type within the detection and management scope are obtained based on the difference in the probability of each pest type between the reference sample plants and other sample plants.

4. The method according to claim 3, characterized in that The step of selecting a number of reference sample plants from all the sample plants includes: The pest types with a non-zero probability among the pest types to which all sample plants belong are regarded as suspected pest types; For each suspected pest type, the sample plant with the maximum probability value of the pest type among all sample plants is used as the reference sample plant.

5. The method according to claim 3, characterized in that: The step of obtaining the distribution area of ​​each pest type within the detection management range according to the difference in the probability of each pest type between the reference sample plant and other sample plants, include, The accumulated value of the absolute value of the probability difference of each pest type between the sample plants is used as the pest identification difference between the sample plants, and the pest identification difference between each reference sample plant and each other sample plant is calculated; The sample plants other than each reference sample plant and the reference sample plant with the smallest difference in pest identification are divided into the same distribution area; Calculate the average probability of obtaining each pest type for all sample plants in each distribution area; Several pest types with an average probability greater than a set warning value are all regarded as the pest types of the distribution area to obtain the distribution areas of each pest type within the detection and management scope.

6. The method according to claim 5, characterized in that The step of obtaining the distribution area of ​​each pest type within the detection management range according to the difference in the probability of each pest type between the reference sample plant and other sample plants also includes: Determine whether the sample plant with the smallest difference in pest identification between the average probability of each pest type of all sample plants in the distribution area and that of all sample plants in the distribution area is the reference sample plant in the distribution area; If so, the distribution areas of each pest type within the detection and management scope are obtained; If not, reselect reference sample plants; Recalculate and divide the updated distribution area based on the reselected reference sample plants; Re-determine whether the sample plant with the smallest difference in pest identification between the average probability of each pest type of all sample plants in each distribution area and the average probability of each pest type of all sample plants in the distribution area is the reference sample plant in the distribution area.

7. The method according to claim 6, characterized in that The step of reselecting reference sample plants comprises: The sample plant with the smallest difference in pest identification between the average probability of each pest type of all sample plants in each distribution area and the distribution area is calculated and obtained as the reference sample plant for reselection.

8. The method according to claim 1, characterized in that The step of obtaining the pest type of each peanut plant according to the distribution area of ​​each pest type within the monitoring and management scope and the position of each peanut plant includes: Get the accuracy of pest identification settings; Determine whether the accuracy of pest identification is met based on the probability of each sample plant on each pest type; If so, the pest type corresponding to the maximum probability of the sample plant in each pest type is taken as the pest type of all peanut plants in the monitoring grid where the sample plant is located; If not, the pest type of the distribution area where the sample plant is located will be used as the pest type of all peanut plants in the monitoring grid where the sample plant is located.

9. The method according to claim 8, characterized in that The step of judging whether the probability of each sample plant on each pest type meets the set accuracy of the pest identification includes: The difference between the maximum probability and the second maximum probability of each pest type of the sample plant is calculated as the probability gradient value of the pest type of the sample plant; The ratio of the probability gradient value of the pest type of the sample plant to the maximum probability is taken as the pest identification accuracy of the sample plant; Determining whether the pest identification accuracy of the sample plant is greater than or equal to the pest identification setting accuracy; If so, it is determined that the probability of the sample plant on each pest type meets the set accuracy of the pest identification; If not, then vice versa.

10. A peanut pest monitoring and planting management system, characterized in that: include, An identification and input unit, used for inputting the identified pest type of each sample plant after the pest type is identified on several parts of the sample plant; A monitoring management unit, used to evenly divide the monitoring management range into a plurality of monitoring grids, and select a peanut plant in each monitoring grid as a sample plant; Obtain the pest type identification results of sample plants at each stage of peanut growth to obtain the probability that each sample plant belongs to each pest type; According to the location of each sample plant in the monitoring grid and the probability analysis of each sample plant belonging to each pest type, the distribution area of ​​each pest type within the monitoring and management scope is obtained; The pest type of each peanut plant is obtained according to the distribution area of ​​each pest type and the location of each peanut plant within the monitoring and management scope; A governance management unit is used to determine whether the pest type of the peanut plant is pest-free; If so, no processing is performed; If not, carry out field pest control according to the corresponding pest management plan.

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