Field insect pest recognition statistical method and system based on image processing

Abstract

The invention relates to the technical field of agriculture, in particular to a field insect pest recognition statistical method and system based on image processing. Judging whether each sub-region image is an insect pest infection sub-region image or an insect pest infection-free sub-region image; analyzing the drug properties of the preset type of pesticide applied to each plant according to each insect pest infection sub-region image and the insect pest infection-free sub-region image to obtain the pest prevention effect of the preset type of pesticide applied to each plant; performing feature analysis on insect pests infected by the plants according to the insect pest infection sub-region images of the plants to obtain insect pest types of the insect pests infected by the plants; and counting and analyzing the insect pests infected by each plant according to the insect pest infection sub-region image of each plant to obtain the insect pest number of the insect pests infected by each plant and the insect pest image of each insect pest, the method realizes automatic identification and statistics of the field insect pests, and significantly improves the efficiency and reliability of a pesticide effect test.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, and in particular to a field pest identification and statistical method and system based on image processing. Background Art

[0002] The traditional field efficacy test result survey mainly relies on human observation and inspection records, which requires a lot of manpower and time, especially in large fields. The efficiency is very low and it is difficult to meet the needs of rapid and timely evaluation of efficacy. In addition, the manual identification of pest types and quantities is easily affected by subjective judgment, resulting in inaccurate identification results, making it difficult to accurately count the types and quantities of pests, and affecting the reliability of efficacy evaluation. Due to the lack of accurate statistical data on the types, quantities and distribution of pests, it is difficult to effectively evaluate the effect of pesticide application and determine whether the control effect of pesticides has reached expectations. With the rapid development of agricultural information technology, pest recognition technology based on image processing has gradually become a research hotspot. Image processing technology can be used to automatically identify and count pests, reduce the workload of manual inspections, and improve work efficiency. However, most of the existing pest recognition methods based on image processing focus on the identification of specific pests, lack the identification and statistical analysis of multiple pests, and are difficult to effectively evaluate the effect of pesticide application. In view of this, the present application proposes a field pest recognition and statistical method and system based on image processing. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a field pest identification and statistical method and system based on image processing.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is:

[0005] The first aspect of the present invention discloses a field pest identification and statistical method based on image processing, comprising the following steps:

[0006] Obtain characteristic images of various types of pests in the field, and construct a pest characteristic knowledge graph based on the characteristic images of various types of pests;

[0007] Based on the plant images of each plant in the field after the pesticide is applied, the plant images are obtained by the camera device, the plant images are divided into a plurality of sub-region images, and each sub-region image is judged to be an image of a sub-region infected by insect pests or an image of a sub-region not infected by insect pests;

[0008] Analyze the properties of the preset type of pesticide applied to each plant based on the images of each pest-infected sub-region and the images of the non-pest-infected sub-region to obtain the pest control effect of the preset type of pesticide applied to each plant;

[0009] Performing feature analysis on the pests infected by each plant according to the pest-infected sub-region images of each plant, and obtaining the pest types of the pests infected by each plant;

[0010] Count and analyze the pests infected by each plant according to the pest infection sub-region image of each plant, and obtain the number of pests infected by each plant and the pest image of each pest;

[0011] A statistical analysis report for each plant is generated based on the pest control effect of the preset type of pesticide applied to each plant, the pest type of the infected pests, the number of infected pests and the pest image of each pest, and the statistical analysis report is sent to a preset terminal.

[0012] Preferably, characteristic images of various types of pests in the field are obtained, and pest characteristic knowledge graphs are constructed according to the characteristic images of various types of pests, specifically:

[0013] Obtain all possible types of pests in the field and obtain characteristic images of various types of pests through the big data network;

[0014] Constructing a knowledge graph, and dividing the knowledge graph into a plurality of graph sub-nodes based on all possible types of pests in the field, and labeling an index label of a type of pest for each graph sub-node according to the type of pest;

[0015] The characteristic images of various types of pests are stored in the graph sub-nodes marked with index labels of the corresponding types of pests, and the pest characteristic knowledge graph is obtained.

[0016] Preferably, based on the plant images of each plant in the field after the application of pesticides obtained by the camera device, the plant images are divided into a plurality of sub-region images, and it is determined whether each sub-region image is an insect pest infected sub-region image or an insect pest free sub-region image, specifically:

[0017] After applying preset types of pesticides to plants in the field, plant images of the plants in the field after the pesticide application are obtained based on a camera device, a voxelization algorithm is introduced, and the size of the voxel grid is preset, and the plant image is voxelized into a plurality of voxel grids based on the voxelization algorithm;

[0018] Obtaining the region image corresponding to each voxel grid to obtain a plurality of sub-region images; randomly accessing any sub-region image, and calculating the mean square error value between the sub-region image and the feature image stored on the graph sub-node in the pest feature knowledge graph;

[0019] Determine the similarity between the sub-region image and the feature images on each graph sub-node according to the mean square error value between the sub-region image and the feature images stored on the graph sub-node in the pest feature knowledge graph;

[0020] The similarities between the sub-region image and the feature images on each atlas sub-node are compared with a preset similarity threshold. If the similarities between the sub-region image and the feature images on each atlas sub-node are not greater than the preset similarity threshold, it means that there is no insect pest in the sub-region image, and the sub-region image is marked as an insect pest-free sub-region image;

[0021] If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, it indicates that there is pest in the sub-region image, and the sub-region image is marked as a pest-infected sub-region image;

[0022] Visit the next unvisited sub-region image, repeat the above steps to determine whether the sub-region image is an insect pest infected sub-region image or an insect pest free sub-region image, and so on, until all sub-region images are determined and analyzed.

[0023] Preferably, the properties of the preset type of pesticide applied to each plant are analyzed according to each pest-infected sub-region image and the non-pest-infected sub-region image to obtain the pest control effect of the preset type of pesticide applied to each plant, specifically:

[0024] Calculating the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions, and calculating the percentage of the pest-infected area of ​​the plant according to the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions;

[0025] Comparing the percentage value of the insect pest infection area of ​​the plant with a preset percentage value;

[0026] If the percentage value of the insect pest infection area of ​​the plant is not greater than the preset percentage value, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide meets the standard, and the preset type of pesticide applied to the plant is calibrated as a type of preset type of pesticide;

[0027] If the proportion of the insect pest infection area of ​​the plant is greater than the preset proportion, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide does not meet the standard, and the preset type of pesticide applied to the plant is calibrated as a type II preset type pesticide.

[0028] Preferably, the pests infected by each plant are analyzed for their characteristics according to the pest-infected sub-region images of each plant to obtain the pest types of the pests infected by each plant, specifically:

[0029] If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, then the index label marked on the atlas sub-node corresponding to the similarity greater than the preset similarity threshold is obtained;

[0030] Determine the type of pests present in the corresponding pest-infected sub-region image according to the index labels marked on the graph sub-nodes corresponding to the graph sub-nodes whose similarity is greater than a preset similarity threshold;

[0031] By analogy, the pest types existing in all pest-infected sub-region images of each plant are integrated to obtain the pest types of the pests infected in each plant.

[0032] Preferably, the pests infected by each plant are counted and analyzed according to the pest infection sub-region image of each plant, so as to obtain the number of pests infected by each plant and the pest image of each pest, specifically:

[0033] Acquire the pest-infected sub-region image of each plant, and perform grayscale, Gaussian filtering and contrast enhancement processing on the pest-infected sub-region image;

[0034] The Prewitt operator algorithm is used to calculate the gradient value of each pixel in the pest-infected sub-region image, and the pixel with the largest gradient value is marked as a seed point;

[0035] Preset the size of the domain range, and calculate the gray value difference between the seed point and the adjacent pixel points within the preset domain range;

[0036] If the grayscale value difference between a certain neighboring pixel and the seed point is greater than a preset difference threshold, the neighboring pixel is marked as an auxiliary pixel, and the auxiliary pixel is added to the area to which the seed point belongs;

[0037] If the grayscale value difference between a certain neighboring pixel point and the seed point is not greater than a preset difference threshold, the neighboring pixel point is used as a new seed point;

[0038] Repeat the above steps to add each pixel point to the area to which the seed point or the new seed point belongs, and iterate continuously until no new pixel point can be added to the area to which the seed point or the new seed point belongs, thereby obtaining a plurality of seed areas;

[0039] Acquire image information of each seed region to separate a single pest in the pest-infected sub-region image from the image background, and obtain pest images of each pest in each plant;

[0040] The regional quantity values ​​of the seed regions are counted, and the regional quantity values ​​of the seed regions are used as the number of pests infected in the corresponding plants.

[0041] The camera device comprises:

[0042] Camera, responsible for collecting images of plants in the field;

[0043] Adjust the support module, which is responsible for fixing the camera and adjusting the camera shooting angle;

[0044] A data storage module for storing image data;

[0045] The power supply module provides power for the entire system.

[0046] The second aspect of the present invention discloses a field pest identification and statistics system based on image processing, the field pest identification and statistics system based on image processing comprises a memory and a processor, the memory stores a field pest identification and statistics method program based on image processing, when the field pest identification and statistics method program based on image processing is executed by the processor, any one of the steps of the field pest identification and statistics method based on image processing is implemented.

[0047] The third aspect of the present invention discloses a computer-readable storage medium, which includes a field pest identification and statistical method program based on image processing. When the field pest identification and statistical method program based on image processing is executed by a processor, any one of the steps of the field pest identification and statistical method based on image processing is implemented.

[0048] The present invention solves the technical defects existing in the background technology, and has the following beneficial effects: based on the image of each plant in the field after the application of pesticides obtained by the camera device, the plant image is divided into a plurality of sub-region images, and it is judged whether each sub-region image is an image of a pest-infected sub-region or an image of a pest-free sub-region; according to each pest-infected sub-region image and the pest-free sub-region image, the medicinal properties of the preset type of pesticide applied to each plant are analyzed to obtain the pest control effect of the preset type of pesticide applied to each plant; according to the pest-infected sub-region image of each plant, the medicinal properties of the preset type of pesticide applied to each plant are analyzed to obtain the pest control effect of the preset type of pesticide applied to each plant; The infected pests are analyzed for characteristics to obtain the pest types of the infected pests in each plant; the infected pests of each plant are counted and analyzed according to the pest infection sub-region images of each plant to obtain the pest quantity of the infected pests in each plant and the pest images of each pest; a statistical analysis report for each plant is generated according to the pest control effect of the preset type of pesticide applied to each plant, the pest types of the infected pests, the pest quantity of the infected pests and the pest images of each pest. This method realizes the automatic identification and statistics of field pests, and significantly improves the efficiency and reliability of efficacy tests. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0050] Figure 1 This is a flow chart of the first method of the statistical method for pest identification in Honda;

[0051] Figure 2 The flowchart of the second method of the statistical method for pest identification in Honda;

[0052] Figure 3 It is a simplified structural diagram of the camera equipment. DETAILED DESCRIPTION

[0053] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0054] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0055] The first aspect of the present invention discloses a statistical method for identifying field pests based on image processing, such as Figure 1 As shown, the following steps are included:

[0056] S102: Acquire characteristic images of various types of insect pests in the field, and construct an insect pest characteristic knowledge graph based on the characteristic images of various types of insect pests;

[0057] S104: based on the plant images of each plant in the field after the pesticide application obtained by the camera device, the plant images are divided into a plurality of sub-region images, and each sub-region image is judged to be an image of a sub-region infected with insect pests or an image of a sub-region not infected with insect pests;

[0058] S106: analyzing the properties of the preset type of pesticide applied to each plant according to the images of each pest-infected sub-region and the images of the non-pest-infected sub-region, to obtain the pest control effect of the preset type of pesticide applied to each plant;

[0059] S108: performing feature analysis on the pests infected by each plant according to the pest-infected sub-region image of each plant, and obtaining the pest type of the pests infected by each plant;

[0060] S110: Counting and analyzing the pests infected by each plant according to the pest infected sub-region image of each plant, and obtaining the number of pests infected by each plant and the pest image of each pest;

[0061] S112: Generate a statistical analysis report for each plant based on the pest control effect of the preset type of pesticide applied to each plant, the pest type of the infected pests, the number of infected pests and the pest image of each pest, and send the statistical analysis report to a preset terminal.

[0062] It should be noted that, first, by acquiring characteristic images of various types of pests and constructing a knowledge graph of pest characteristics, a visual recognition basis for different pests is established. Then, the images of plants in the field are obtained by using a camera after applying the pesticide, and these images are segmented and processed to decompose the plant images into multiple sub-region images. The system will further determine whether each sub-region is infected by pests. This is done by comparing the sub-region image with the known pest characteristic knowledge graph. This step helps to accurately locate the location of the pest and improve the accuracy of recognition. Then, by analyzing the pest-infected sub-regions and the pest-free sub-regions, the efficacy of the preset type of pesticide can be evaluated, which not only includes the direct effect of the drug properties, but also may involve comprehensive evaluation indicators such as the stability, permeability and insecticidal efficiency of the agent in a specific environment, thereby providing a basis for the selection and use of pesticides. Next, the system performs characteristic analysis on the pests to determine the specific pest type infected by each plant, and then performs a counting analysis of the number of pests to quantify the distribution of the pests. Combined with the acquisition of pest images, more intuitive pest examples can be obtained, which is convenient for further analysis and research. Finally, the above analysis results are summarized to generate a statistical analysis report, which is pushed through preset terminals (such as mobile phones, computers, etc.), which not only facilitates agricultural managers to understand the field pest situation in a timely manner, but also provides detailed data support for subsequent decision-making, such as adjusting pesticide use strategies, optimizing planting plans, etc. In summary, this method realizes the automatic identification and statistics of field pests by integrating advanced technologies such as image processing and machine learning, significantly improves the efficiency and reliability of efficacy tests, and provides a scientific, efficient and accurate decision support tool for agricultural production.

[0063] Preferably, characteristic images of various types of pests in the field are obtained, and pest characteristic knowledge graphs are constructed based on the characteristic images of various types of pests, such as Figure 2 As shown, specifically:

[0064] S202: Acquire all possible types of insect pests in the field, and acquire characteristic images of various types of insect pests through a big data network;

[0065] S204: constructing a knowledge graph, and dividing the knowledge graph into a plurality of graph sub-nodes based on all possible types of pests in the field, and labeling an index label of a type of pest for each graph sub-node according to the various types of pests;

[0066] S206: The characteristic images of various types of pests are stored in the graph sub-nodes of the index labels marked with the corresponding types of pests, so as to obtain the pest characteristic knowledge graph.

[0067] It should be noted that all possible types of pest samples are obtained, and the characteristic images of these pests are obtained using the big data network. These characteristic images contain multiple appearance features of pests such as size, shape, color, texture, etc., which lay the foundation for subsequent identification and classification. Based on the large number of pest samples collected, a comprehensive knowledge graph is constructed. This graph divides all possible pest types into several graph sub-nodes, each of which represents a specific type of pest. This structured approach enables the graph to have a global vision and focus on specific pest types. For ease of management and query, each graph sub-node is assigned an index label of a type of pest. In this way, in the actual operation process, when a pest sample is obtained, the corresponding graph sub-node can be quickly located by its appearance features, and then the type of the pest can be identified. The characteristic image of each type of pest is stored in the graph sub-node corresponding to its index label. This precise image-label correspondence makes the knowledge graph a powerful database that can not only be used to identify unknown pests, but also provide rich reference information, such as the life cycle of pests and prevention and control methods. The pest feature knowledge graph constructed in this way can greatly improve the accuracy and efficiency of pest identification.

[0068] Preferably, based on the plant images of each plant in the field after the application of pesticides obtained by the camera device, the plant images are divided into a plurality of sub-region images, and it is determined whether each sub-region image is an insect pest infected sub-region image or an insect pest free sub-region image, specifically:

[0069] After applying preset types of pesticides to plants in the field, plant images of the plants in the field after the pesticide application are obtained based on a camera device, a voxelization algorithm is introduced, and the size of the voxel grid is preset, and the plant image is voxelized into a plurality of voxel grids based on the voxelization algorithm;

[0070] Obtaining the region image corresponding to each voxel grid to obtain a plurality of sub-region images; randomly accessing any sub-region image, and calculating the mean square error value between the sub-region image and the feature image stored on the graph sub-node in the pest feature knowledge graph;

[0071] Determine the similarity between the sub-region image and the feature images on each graph sub-node according to the mean square error value between the sub-region image and the feature images stored on the graph sub-node in the pest feature knowledge graph;

[0072] The similarities between the sub-region image and the feature images on each atlas sub-node are compared with a preset similarity threshold. If the similarities between the sub-region image and the feature images on each atlas sub-node are not greater than the preset similarity threshold, it means that there is no insect pest in the sub-region image, and the sub-region image is marked as an insect pest-free sub-region image;

[0073] If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, it indicates that there is pest in the sub-region image, and the sub-region image is marked as a pest-infected sub-region image;

[0074] Visit the next unvisited sub-region image, repeat the above steps to determine whether the sub-region image is an insect pest infected sub-region image or an insect pest free sub-region image, and so on, until all sub-region images are determined and analyzed.

[0075] It should be noted that after applying the preset type of pesticide to the plants in the field, the plant images are collected using a camera device, and the voxelization algorithm is introduced to divide the plant images into several voxel grids. The size of the voxel grid is a pre-set parameter. This step helps to refine the image processing and improve the recognition accuracy. The corresponding regional image is extracted from each voxel grid to form a series of sub-region images, which is the basis for further pest identification. For each sub-region image, the mean square error value between it and the feature image stored in the pest feature knowledge graph is calculated. The smaller the mean square error value, the higher the similarity between the two images. Based on the calculated similarity value, it is judged whether the sub-region image matches the pest feature. If the similarity is lower than the preset threshold, it means that there is no pest in the area, and it is marked as a pest-free sub-region image; otherwise, it is marked as a pest-infected sub-region image. Repeat the above steps for all sub-region images that have not been visited until all sub-region images are identified. It can accurately identify the distribution location of pests on plants, provide real-time and accurate pest information, and provide strong support for the automated monitoring and management of farmland pests.

[0076] Preferably, the properties of the preset type of pesticide applied to each plant are analyzed according to each pest-infected sub-region image and the non-pest-infected sub-region image to obtain the pest control effect of the preset type of pesticide applied to each plant, specifically:

[0077] Calculating the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions, and calculating the percentage of the pest-infected area of ​​the plant according to the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions;

[0078] Comparing the percentage value of the insect pest infection area of ​​the plant with a preset percentage value;

[0079] If the percentage value of the insect pest infection area of ​​the plant is not greater than the preset percentage value, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide meets the standard, and the preset type of pesticide applied to the plant is calibrated as a type of preset type of pesticide;

[0080] If the proportion of the insect pest infection area of ​​the plant is greater than the preset proportion, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide does not meet the standard, and the preset type of pesticide applied to the plant is calibrated as a type II preset type pesticide.

[0081] It should be noted that, first, the plant image is segmented into pest-infected sub-area images and non-pest-infected sub-area images through image processing technology. Next, the ratio of the number of pest-infected sub-area images to the total number of areas is counted to obtain the percentage of pest-infected area. This ratio reflects the degree of pest coverage on the plant after the application of pesticides. The calculated percentage of pest-infected area is compared with the preset benchmark value. The preset benchmark value usually represents the ideal standard for pest control effect. If the actual percentage value is less than or equal to the preset benchmark value, it means that the pest control effect of the pesticide meets the standard, and it can be classified as "Type I preset type pesticide". If the actual percentage value is greater than the preset benchmark value, the pest control effect of the pesticide does not meet expectations, and it can be classified as "Type II preset type pesticide".

[0082] Preferably, the pests infected by each plant are analyzed for their characteristics according to the pest-infected sub-region images of each plant to obtain the pest types of the pests infected by each plant, specifically:

[0083] If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, then the index label marked on the atlas sub-node corresponding to the similarity greater than the preset similarity threshold is obtained;

[0084] Determine the type of pests present in the corresponding pest-infected sub-region image according to the index labels marked on the graph sub-nodes corresponding to the graph sub-nodes whose similarity is greater than a preset similarity threshold;

[0085] By analogy, the pest types existing in all pest-infected sub-region images of each plant are integrated to obtain the pest types of the pests infected in each plant.

[0086] It should be noted that, first, the image of the field plant after the application of pesticides is obtained by a camera device, and the image processing technology is used to segment it into multiple sub-region images. This step is based on the assumption that the pests are mainly concentrated in a specific part of the plant, so segmentation can more accurately locate and identify the pests. For each sub-region image, the system calculates the similarity between it and the feature image in the pest feature knowledge graph. The pest feature knowledge graph contains the feature images of various known pests. By comparing the similarity between the sub-region image and these feature images, it can be preliminarily determined whether a certain type of pest exists in the sub-region image. A preset similarity threshold is set. If the similarity between the sub-region image and the feature image on a certain atlas sub-node exceeds this threshold, it means that the sub-region image is likely to contain the pest type corresponding to the feature image. Once it is determined that the sub-region image has a high similarity with the feature image of a certain atlas sub-node, the system will determine the pest type existing in the sub-region image according to the index label marked by the atlas sub-node. The above analysis is performed on the pest-infected sub-region images in all plants, and the pest type infected in each plant is obtained after integration processing. This method can automatically identify the types of insect pests on plants in the field, greatly improving the efficiency and accuracy of identification and reducing the error rate of manual observation and identification.

[0087] Preferably, the pests infected by each plant are counted and analyzed according to the pest infection sub-region image of each plant, so as to obtain the number of pests infected by each plant and the pest image of each pest, specifically:

[0088] Acquire the pest-infected sub-region image of each plant, and perform grayscale, Gaussian filtering and contrast enhancement processing on the pest-infected sub-region image;

[0089] The Prewitt operator algorithm is used to calculate the gradient value of each pixel in the pest-infected sub-region image, and the pixel with the largest gradient value is marked as a seed point;

[0090] Preset the size of the domain range, and calculate the gray value difference between the seed point and the adjacent pixel points within the preset domain range;

[0091] If the grayscale value difference between a certain neighboring pixel and the seed point is greater than a preset difference threshold, the neighboring pixel is marked as an auxiliary pixel, and the auxiliary pixel is added to the area to which the seed point belongs;

[0092] If the grayscale value difference between a certain neighboring pixel point and the seed point is not greater than a preset difference threshold, the neighboring pixel point is used as a new seed point;

[0093] Repeat the above steps to add each pixel point to the area to which the seed point or the new seed point belongs, and iterate continuously until no new pixel point can be added to the area to which the seed point or the new seed point belongs, thereby obtaining a plurality of seed areas;

[0094] Acquire image information of each seed region to separate a single pest in the pest-infected sub-region image from the image background, and obtain pest images of each pest in each plant;

[0095] The regional quantity values ​​of the seed regions are counted, and the regional quantity values ​​of the seed regions are used as the number of pests infected in the corresponding plants.

[0096] It should be noted that, first, the acquired pest-infected sub-region image is grayed, Gaussian filtered, and contrast enhanced. Graying simplifies the color information of the image, Gaussian filtering removes noise, and contrast enhancement highlights the details of the image, laying the foundation for subsequent image analysis. The Prewitt operator algorithm is used to calculate the gradient value of each pixel in the image, and the pixel with the largest gradient value is found as the seed point. The seed point is usually the key feature point of the edge of the pest. The size of the domain range is preset, and the gray value difference between the seed point and its neighboring pixels is calculated. If the difference exceeds the preset threshold, the neighboring pixel is marked as an affiliated pixel and added to the domain to which the seed point belongs. This process is iterated until no new pixel can be added to the domain. Through the above steps, multiple seed regions are finally obtained, each region corresponding to a pest in the image. These regions clearly isolate the pests, which is convenient for subsequent analysis and statistics. The image information of a single pest is extracted from the seed region, that is, the pest image of each pest in each plant. The total number of seed regions, that is, the number of pests, is counted. Each seed region represents an insect pest, so the number of seed regions directly corresponds to the number of insect pests. Through seed point detection and field range expansion, this method can accurately identify individual insect pests in the image, and can effectively distinguish them even in densely distributed situations. The entire process is highly automated, reducing manual intervention and improving efficiency and consistency. It can accurately count the number of insect pests on each plant, and can also obtain image information of the insect pests, providing basic data for subsequent insect pest analysis and research.

[0097] In addition, the method further comprises the following steps:

[0098] Obtaining chemical composition information of various preset types of pesticides, and generating search tags according to the chemical composition information of various preset types of pesticides;

[0099] Searching the big data network based on the search tag to obtain the volatilization rates of various preset types of pesticides under various combinations of environmental factors;

[0100] Constructing a knowledge base, and importing the volatilization rates of various preset types of pesticides under various combinations of environmental factors into the knowledge base to obtain a volatilization rate knowledge base;

[0101] Before applying the preset type of pesticide to the plants in the field, respectively, obtaining the latest application time node of the preset type of pesticide to each plant;

[0102] According to the latest application time node of the preset type of pesticide for each plant, a preset application time period for each plant is formulated;

[0103] Based on the weather forecast software, the predicted environmental factors for applying the preset type of pesticide to each plant in each preset pesticide application time period are obtained;

[0104] Importing predicted environmental factors for applying preset types of pesticides to each plant in each pre-pesticide application time period into the volatilization rate knowledge base for pairing, and obtaining predicted volatilization rates after applying preset types of pesticides to each plant in each pre-pesticide application time period;

[0105] The predicted volatilization rates after applying the preset type of pesticide to each plant in each pre-application time period are sorted, and the pre-application time period corresponding to the minimum predicted volatilization rate is extracted, and the pre-application time period corresponding to the minimum predicted volatilization rate is output as the optimal application time period for applying the preset type of pesticide to the corresponding plant;

[0106] By analogy, the optimal application time period for applying preset types of pesticides to each plant is obtained.

[0107] It should be noted that, first, the system obtains and analyzes the information of pesticide components of various preset types, and generates retrieval tags for search and classification. Using the big data network and retrieval tags, the system searches and obtains the volatilization rate data of various preset types of pesticides under different environmental conditions. These data include factors such as temperature, humidity, and wind speed. The collected volatilization rate data are integrated into the knowledge base to form a database on the relationship between the volatilization rate of pesticides and environmental factors, which is convenient for subsequent queries and applications. Before applying the pesticide, the system determines the latest time point for applying the pesticide for each plant based on the specific needs of each plant (for example, considering the crop growth cycle, the peak period of pests and diseases, etc.). This is to ensure that the pesticide is applied during the critical period of crop growth, while avoiding too early or too late to improve the efficacy of the drug. Based on weather forecasting software, the system obtains predicted environmental factors such as temperature, humidity, wind speed, etc. during the preset pesticide application time period, and provides real-time or future environmental conditions for volatilization rate prediction. The predicted environmental factors are imported into the volatilization rate knowledge base. The system calculates the volatilization rate after application in the pre-application time period based on historical data and current environmental predictions. This step uses machine learning or complex algorithms to simulate the behavior of pesticides in specific environments. The system sorts the predicted volatilization rates and finds the pre-application time period corresponding to the minimum volatilization rate as the optimal application time. This ensures that the pesticides are most effective in the most favorable environment while reducing negative impacts on the environment. Through precise calculations and predictions, the best application time is selected for each experimental plant to maximize the effectiveness of the pesticide and minimize environmental impacts, thereby improving the reliability of the experimental results and being able to provide a more reliable evaluation report.

[0108] In addition, the steps after obtaining the plant images of each plant in the field after the pesticide application based on the camera device may also include the following steps:

[0109] A local linear embedding model is introduced, the plant image is input into the local linear embedding model, and feature decomposition is performed to obtain an orthogonal matrix and a diagonal matrix composed of linear vectors.

[0110] Multiplying the orthogonal matrix with the diagonal matrix to obtain an eigenvector matrix;

[0111] Selecting an eigenvector with the largest eigenvalue in the eigenvector matrix, taking it as a reference point, and constructing a new coordinate system based on the reference point;

[0112] All eigenvectors in the eigenvector matrix are mapped to a new coordinate system to generate a target point cloud data matrix; the target point cloud data matrix contains position information of all eigenvectors in the image in the new coordinate system.

[0113] The coordinate points of each limit point are extracted from the target point cloud data matrix to obtain a set of limit coordinate points. The limit coordinate points represent the most important feature information in the image.

[0114] The extreme coordinate point set is reassembled into a world coordinate system to generate a denoised plant image.

[0115] It should be noted that the images of plants in the field after the application of pesticides are captured by a camera. These images contain the appearance characteristics of the plants, including possible pests, etc. Next, the local linear embedding model is introduced. This model decomposes the image features into an orthogonal matrix composed of linear vectors and a diagonal matrix by analyzing the local linear relationship in the image. The orthogonal matrix represents the orthogonal basis of the eigenvectors, while the diagonal matrix records the weights or importance of these eigenvectors. By multiplying the orthogonal matrix with the diagonal matrix, an eigenvector matrix is ​​obtained, which contains all the eigenvectors and their weights for subsequent image analysis and processing. The eigenvector with the largest eigenvalue is selected from the eigenvector matrix as the reference point, and a new coordinate system is constructed based on this reference point. This new coordinate system can more effectively represent the key information in the image. All eigenvectors are mapped to the new coordinate system to generate a target point cloud data matrix. This matrix contains the position information of all eigenvectors in the new coordinate system, which helps to show the key features in the image more clearly. The coordinates of each extreme point are extracted from the target point cloud data matrix to form a set of extreme coordinate points. These extreme points represent the most important or most significant feature information in the image. The extreme coordinate point sets are reassembled into the world coordinate system to generate a denoised plant image, a process that helps highlight the key features of the plant while reducing the impact of noise and other irrelevant information. Through the local linear embedding model, the system can more accurately identify and extract key features in the image, such as the shape, size, possible pests, etc. of the plant. The generated denoised plant image helps remove noise and other interference factors in the image, making the key information more clearly visible.

[0116] like Figure 3 As shown, the camera device includes:

[0117] Camera 2011, responsible for collecting images of plants in the field;

[0118] The adjustment support module 2022 is responsible for fixing the camera and adjusting the camera shooting angle;

[0119] Data storage module 2033, storing image data;

[0120] The power supply module 2044 provides power supply for the entire system.

[0121] The second aspect of the present invention discloses a field pest identification and statistics system based on image processing, the field pest identification and statistics system based on image processing comprises a memory and a processor, the memory stores a field pest identification and statistics method program based on image processing, when the field pest identification and statistics method program based on image processing is executed by the processor, any one of the steps of the field pest identification and statistics method based on image processing is implemented.

[0122] The third aspect of the present invention discloses a computer-readable storage medium, which includes a field pest identification and statistical method program based on image processing. When the field pest identification and statistical method program based on image processing is executed by a processor, any one of the steps of the field pest identification and statistical method based on image processing is implemented.

[0123] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0124] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0125] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0126] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0127] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0128] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A statistical method for identifying field pests based on image processing, characterized in that: The following steps are involved: Obtain characteristic images of various types of pests in the field, and construct a pest characteristic knowledge graph based on the characteristic images of various types of pests; Based on the plant images of each plant in the field after the pesticide is applied, the plant images are obtained by the camera device, the plant images are divided into a plurality of sub-region images, and each sub-region image is judged to be an image of a sub-region infected by insect pests or an image of a sub-region not infected by insect pests; Analyze the properties of the preset type of pesticide applied to each plant based on the images of each pest-infected sub-region and the images of the non-pest-infected sub-region to obtain the pest control effect of the preset type of pesticide applied to each plant; Performing feature analysis on the pests infected by each plant according to the pest-infected sub-region images of each plant, and obtaining the pest types of the pests infected by each plant; Count and analyze the pests infected by each plant according to the pest infection sub-region image of each plant, and obtain the number of pests infected by each plant and the pest image of each pest; A statistical analysis report for each plant is generated based on the pest control effect of the preset type of pesticide applied to each plant, the pest type of the infected pests, the number of infected pests and the pest image of each pest, and the statistical analysis report is sent to a preset terminal.

2. The method for identifying field pests based on image processing according to claim 1, characterized in that: Obtain the characteristic images of various types of pests in the field, and build a pest characteristic knowledge graph based on the characteristic images of various types of pests, specifically: Obtain all possible types of pests in the field and obtain characteristic images of various types of pests through the big data network; Constructing a knowledge graph, and dividing the knowledge graph into a plurality of graph sub-nodes based on all possible types of pests in the field, and labeling each graph sub-node with an index label of a type of pest according to the type of pest; The characteristic images of various types of pests are stored in the graph sub-nodes marked with index labels of the corresponding types of pests, and the pest characteristic knowledge graph is obtained.

3. The method for identifying field pests based on image processing according to claim 1, characterized in that: Based on the plant images of each plant in the field after the application of pesticides obtained by the camera equipment, the plant images are divided into a plurality of sub-region images, and it is determined whether each sub-region image is an image of a pest-infected sub-region or an image of a non-pest-infected sub-region, specifically: After applying preset types of pesticides to plants in the field, plant images of the plants in the field after the pesticide application are obtained based on a camera device, a voxelization algorithm is introduced, and the size of the voxel grid is preset, and the plant image is voxelized into a plurality of voxel grids based on the voxelization algorithm; Obtaining the region image corresponding to each voxel grid to obtain a plurality of sub-region images; randomly accessing any sub-region image, and calculating the mean square error value between the sub-region image and the feature image stored on the graph sub-node in the pest feature knowledge graph; Determine the similarity between the sub-region image and the feature images on each graph sub-node according to the mean square error value between the sub-region image and the feature images stored on the graph sub-node in the pest feature knowledge graph; The similarities between the sub-region image and the feature images on each atlas sub-node are compared with a preset similarity threshold. If the similarities between the sub-region image and the feature images on each atlas sub-node are not greater than the preset similarity threshold, it means that there is no insect pest in the sub-region image, and the sub-region image is marked as an insect pest-free sub-region image; If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, it indicates that there is pest in the sub-region image, and the sub-region image is marked as a pest-infected sub-region image; Visit the next unvisited sub-region image, repeat the above steps to determine whether the sub-region image is an insect pest infected sub-region image or an insect pest free sub-region image, and so on, until all sub-region images are determined and analyzed.

4. The method for identifying field pests based on image processing according to claim 1, characterized in that: The properties of the preset types of pesticides applied to each plant are analyzed based on the images of each pest-infected sub-region and the images of the non-pest-infected sub-region, and the pest control effects of the preset types of pesticides applied to each plant are obtained, specifically: Calculating the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions, and calculating the percentage of the pest-infected area of ​​the plant according to the total number of all images marked as pest-infected sub-regions and images marked as non-pest-infected sub-regions; Comparing the percentage value of the insect pest infection area of ​​the plant with a preset percentage value; If the percentage value of the insect pest infection area of ​​the plant is not greater than the preset percentage value, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide meets the standard, and the preset type of pesticide applied to the plant is calibrated as a type of preset type of pesticide; If the proportion of the insect pest infection area of ​​the plant is greater than the preset proportion, the preset type of pesticide applied to the plant is obtained, indicating that the pest control effect of the preset type of pesticide does not meet the standard, and the preset type of pesticide applied to the plant is calibrated as a type II preset type pesticide.

5. The method for identifying and counting field pests based on image processing according to claim 3, characterized in that: According to the pest infection sub-region images of each plant, the characteristics of the pests infected by each plant are analyzed to obtain the pest types of the pests infected by each plant, specifically: If the similarity between the sub-region image and a feature image on a certain atlas sub-node is greater than a preset similarity threshold, then the index label marked on the atlas sub-node corresponding to the similarity greater than the preset similarity threshold is obtained; Determine the type of pests present in the corresponding pest-infected sub-region image according to the index labels marked on the graph sub-nodes corresponding to the graph sub-nodes whose similarity is greater than a preset similarity threshold; By analogy, the pest types existing in all pest-infected sub-region images of each plant are integrated to obtain the pest types of the pests infected in each plant.

6. The method for identifying and counting field pests based on image processing according to claim 1, characterized in that: According to the pest infection sub-region images of each plant, the pests infected by each plant are counted and analyzed to obtain the number of pests infected by each plant and the pest images of each pest, specifically: Acquire the pest-infected sub-region image of each plant, and perform grayscale, Gaussian filtering and contrast enhancement processing on the pest-infected sub-region image; The Prewitt operator algorithm is used to calculate the gradient value of each pixel in the pest-infected sub-region image, and the pixel with the largest gradient value is marked as a seed point; Preset the size of the domain range, and calculate the gray value difference between the seed point and the adjacent pixel points within the preset domain range; If the grayscale value difference between a certain neighboring pixel and the seed point is greater than a preset difference threshold, the neighboring pixel is marked as an auxiliary pixel, and the auxiliary pixel is added to the area to which the seed point belongs; If the grayscale value difference between a certain neighboring pixel point and the seed point is not greater than a preset difference threshold, the neighboring pixel point is used as a new seed point; Repeat the above steps to add each pixel point to the area to which the seed point or the new seed point belongs, and iterate continuously until no new pixel point can be added to the area to which the seed point or the new seed point belongs, thereby obtaining a plurality of seed areas; Acquire image information of each seed region to separate a single pest in the pest-infected sub-region image from the image background, and obtain pest images of each pest in each plant; The regional quantity values ​​of the seed regions are counted, and the regional quantity values ​​of the seed regions are used as the number of pests infected in the corresponding plants.

7. The method for identifying field pests based on image processing according to claim 1, characterized in that: The camera device comprises: Camera, responsible for collecting images of plants in the field; Adjust the support module, which is responsible for fixing the camera and adjusting the camera shooting angle; A data storage module for storing image data; The power supply module provides power for the entire system.

8. A field pest identification and statistics system based on image processing, characterized in that: The field pest identification and statistics system based on image processing includes a memory and a processor. The memory stores a field pest identification and statistics method program based on image processing. When the field pest identification and statistics method program based on image processing is executed by the processor, the steps of the field pest identification and statistics method based on image processing as described in any one of claims 1 to 7 are implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a field pest identification and statistical method program based on image processing. When the field pest identification and statistical method program based on image processing is executed by a processor, the steps of the field pest identification and statistical method based on image processing as described in any one of claims 1 to 7 are implemented.

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