184 results about "Crop pest" patented technology

The invention provides a crop disease and pest recognition and pathology follow-up method and device and a medium, and relates to the technical field of crop disease and pest recognition. Coding the diagnosis hypothesis in the text format into vector representation of a fixed dimension; configuring comprehensive feature representation; through a multi-stage strategy, a soft label is generated by the teacher model, and the soft label is used as supervision information to guide the student model; model check points are saved in the training process, and student model performance is evaluated by using the verification set; predicting development condition parameters of various diseases; simulating the diffusion process of the disease in the space grid through convolution operation to obtain a propagation probability graph in the future N days; a disease progress prediction result generated in combination with the disease propagation condition is used for evaluating the development trend of crop diseases and insect pests. According to the invention, detection and diagnosis of crop diseases and insect pests are realized, the propagation path of the diseases and insect pests can be traced, and root blocking measures are recommended.

The invention discloses a crop disease and insect pest early warning method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the multi-modal feature fusion analysis of a standardized training data set through an intelligent analysis terminal, and obtaining a meteorological sensitivity coefficient, a crop resistance coefficient and a disease and insect pest outbreak probability coefficient; inputting the three coefficients into a pest and disease risk quantification model, and outputting a pest and disease risk index; the early warning decision terminal triggers a multi-stage early warning rule according to the pest risk index, and generates a customized early warning scheme containing emergency measure suggestions; and the feedback verification terminal receives feedback data after the user implements the prevention and control measures, and dynamically updates the model parameters. According to the invention, the early warning decision terminal triggers the multi-stage early warning rule according to the disease and insect pest risk index, and generates the customized early warning scheme containing the emergency measure suggestion, thereby achieving the graded response and precise implementation of disease and insect pest early warning, and improving the pertinence and effectiveness of prevention and control measures.

The invention discloses a crop pest monitoring method and system based on artificial intelligence, and the method comprises the steps: constructing a crop pest recognition model, and recognizing the types and number of pests; the method comprises the following steps: acquiring crop infrared thermal imaging image data of multi-view fusion, and identifying insect pest types and quantity of unshielded areas in a crop monitoring area; identifying a thermal anomaly region, and determining the number of insect pests in a shielded region in the crop monitoring region; constructing an insect pest type identification model, and determining the insect pest type of the shielded area in the crop monitoring area; calibrating the number and type data of insect pests in the crop monitoring area; constructing an insect pest prediction model, and judging a future insect pest diffusion trend; and evaluating the effectiveness of the pest control strategy, and adjusting the pest control strategy and implementing the advance of the pest control strategy. According to the invention, comprehensive monitoring, accurate identification and scientific prevention and control of insect pests are realized, the problems of monitoring blind areas and prevention and control lag are effectively solved, and the efficiency and reliability of insect pest prevention and control are significantly improved.

The invention relates to a light-weight pest image detection method based on dynamic adaptive scanning and attention mechanism joint optimization, and solves the problems that a light-weight model sacrifices a feature modeling capability during parameter compression, so that missing detection is more, small-scale pest information is difficult to extract, and the detection precision is low. And although small-scale features can be extracted by a high-parameter-quantity scheme, the calculation complexity is high, the high-parameter-quantity scheme is difficult to deploy to edge equipment, and the unification of high detection precision and low calculation power requirements cannot be realized. The method comprises the following steps: constructing a multi-category crop pest data set; constructing a lightweight pest image detection network; training a lightweight pest image detection network; acquiring a pest image to be detected; and obtaining a pest image detection result. Target features are extracted through the dynamic self-adaptive scanning module, long-distance dependency relationships in different directions are captured by utilizing the features of a state space model, and meanwhile, the calculation efficiency is kept; meanwhile, the detection network is optimized based on the attention mechanism, the detection precision and robustness of the pest target are remarkably improved, and the real-time performance of pest detection is achieved through light-weight design.

The invention discloses a crop disease and pest identification method and system based on multi-task learning. The method comprises the following steps: firstly, constructing a crop disease and pest identification model comprising a multi-scale feature information fusion module, a disease detection branch and a severity classification branch; the method comprises the following steps: preprocessing an input leaf image, and extracting and fusing a multi-scale feature map by a multi-scale feature information fusion module; the disease detection branch generates disease and insect pest candidate regions by using a region proposal network, and outputs disease and insect pest positions and categories through disease detection in combination with a multi-scale fusion feature map; meanwhile, the fusion feature map with the maximum size is input into a severity classification branch to realize four-stage evaluation; a weighted loss function design thought is provided, and multi-task network branches can be guided to carry out joint training. According to the method, end-to-end multi-task cooperative processing is realized, disease and pest positioning, classification and severity evaluation are synchronously completed by sharing a multi-scale fusion feature map, the recognition efficiency is greatly improved, and the real-time monitoring requirement of an agricultural scene is met.

The invention provides a crop pest detection method based on an improved YOLOv11s model, and the method comprises the steps: employing YOLOv11s as an original model, providing an AMdown double-branch down-sampling module, a dynamic texture attention module and a spatial reconstruction multi-scale expansion attention module in the original YOLOv11s model, forming the improved YOLOv11s model, and employing the improved YOLOv11s model as a pest detection network model; inputting the preprocessed pest data set into an improved YOLOv11s model, and carrying out model training; and detecting an actual pest image by using the trained improved YOLOv11s model. According to the method, the YOLOv11s model is improved, an AMdown double-branch down-sampling module, a dynamic texture attention module and a spatial reconstruction multi-scale expansion attention module are provided, the generalization ability and robustness of the model are improved, and the detection precision is improved while the parameter quantity and FLOPs are not increased.

The invention discloses a crop pest image intelligent identification system based on an electronic information technology, and relates to the technical field of agricultural informatization. The system comprises an image acquisition module which acquires RGB image data of a target crop area and synchronously acquires associated environment monitoring data and image shooting time data; the preprocessing module preprocesses the data; the insect pest recognition module outputs insect pest type data and insect body position frame selection coordinates through an improved YOLO recognition model; the comparison module generates insect pest development state data; a risk assessment module calls a pest hazard level dictionary matching risk coefficient, and calculates an environmental suitability value in combination with environmental monitoring processing data; the risk scoring module generates a comprehensive risk score; and the early warning decision module matches the prevention and treatment scheme data when the comprehensive risk score exceeds a preset threshold. According to the technology, intelligence and precision of insect pest recognition and early warning are realized, and the agricultural insect pest prevention and control efficiency is improved.

The invention provides an agricultural pest diagnosis and prevention method and device, electronic equipment and a storage medium, and relates to the technical field of agriculture, and the method comprises the steps: extracting image features, shooting time and a shooting position from a crop pest image of a target region; inputting the image features into a multi-modal recognition model to obtain a first diagnosis result output by the multi-modal recognition model; inputting the shooting time and the attribution environment data corresponding to the shooting position into the multi-source data mining model to obtain a second diagnosis result output by the multi-source data mining model; and inputting the first diagnosis result and the second diagnosis result into a disease and pest knowledge map for fusion reasoning, and generating a plant protection medication management suggestion. According to the invention, through the multi-modal information fusion and step-by-step reasoning technology and in combination with the disease and pest knowledge graph, the accuracy and interpretability of disease and pest diagnosis and the individuation of the prevention and control scheme are realized.

The invention discloses application of a microbial natural product GameXPeptide-A in improving the pest control effect of an insecticide, and belongs to the technical field of biological control. Experiments prove that after the GameXPeptide-A is combined with insecticides such as metarhizium anisopliae, beauveria bassiana, nuclear polyhedrosis virus, 4% of abamectin acetamiprid, 10% of spinosad emamectin benzoate, 30% of etoxazole nitrile, 45% of biphenyl etoxazole, 5% of emamectin benzoate, 5% of chlorantraniliprole, 5% of abamectin and the like for use, the insecticidal effect is obviously improved; the insecticidal activity of the insecticide on agricultural pests such as thrips, ostrinia nubilalis and prodenia litura can be effectively improved, so that the substance can be combined with different insecticides for use, and the insecticidal activity of the insecticide on crop pests in agricultural production such as lepidoptera, diptera, coleoptera, orthoptera, hemiptera and homoptera is improved; the compound can be used for development and application of novel pest immunosuppressants or insecticide synergists.

The present invention relates to the field of computer vision, specifically a method and system for real-time identification and control of crop pests and diseases based on multimodal edge computing. The system comprises: acquiring drone multispectral imagery, infrared thermal imaging, and the NDVI index, preprocessing them in combination with field IoT sensor data to generate a multimodal feature input sequence; constructing a spectral-environmental fusion feature matrix based on the Transformer attention mechanism, and utilizing the YOLOv7-Spectral model for pest and disease detection to generate a pest and disease distribution heat map; calculating the pest risk index (BRI) and pesticide application priority map based on historical data; employing an ant colony optimization algorithm to plan drone application paths and optimizing the application plan based on wind speed and humidity parameters; accurately applying pesticides along the optimized paths by the drones and monitoring disease trends; adjusting the model based on application feedback data, and optimizing pest and disease control strategies using federated learning. The present invention improves recognition accuracy, reduces pesticide use, and achieves precise, efficient, and intelligent control.

The invention discloses a crop disease and pest diagnosis system based on a knowledge graph, and relates to the technical field of disease and pest diagnosis. A data acquisition module acquires description information, image data and environment data of crops; the data processing module is used for extracting symptom keywords, disease and pest expansion rate fluctuation characteristics and soil EC value stress characteristics; the knowledge graph construction module is used for establishing an association network comprising disease and pest symptom nodes, environmental condition nodes and dynamic feature nodes; the disease and pest distinguishing module evaluates the distinguishing accuracy of disease and pest similar symptoms in combination with dynamic characteristics and environmental characteristics; the diagnosis result division module is used for accurately dividing diagnosis results and taking corresponding treatment measures; and the optimization management module dynamically optimizes a knowledge graph and a classification model based on fixed time period analysis of an incomplete accuracy diagnosis result, so that the accuracy of disease and insect pest diagnosis is remarkably improved, prevention and treatment errors caused by misdiagnosis are avoided, and intelligent technical support is provided for agricultural production.

The invention relates to the field of intelligent greenhouse management, and particularly discloses an intelligent greenhouse management system based on computer vision and deep learning, and the system comprises a data collection module which obtains environment data and soil data in a greenhouse in real time; the image acquisition module is used for acquiring crop growth images; the core controller preprocesses the data acquired by the data acquisition module and the image acquisition module and uploads the data to the cloud database; the computer vision and deep learning analysis module is used for identifying crop types and pest and disease damage conditions based on the crop images; the water-fertilizer control unit comprises an intelligent control module, a water-fertilizer all-in-one machine and a drip irrigation system; the user interaction module provides remote monitoring and control functions for a user; according to the system and the method, the condition of economic loss caused by the fact that crop diseases and insect pests cannot be correctly treated due to insufficient experience in traditional agriculture is overcome, environmental visualization is realized, a farmer can conveniently monitor the greenhouse in real time, precise irrigation and fertilization are realized, the agricultural production efficiency is improved, and resource waste is reduced.

The invention provides a crop disease and insect pest judgment method and system based on image feature recognition, and relates to the technical field of picture recognition processing, and the method comprises the steps: carrying out the disease and insect pest recognition of a to-be-judged crop image through a pre-trained disease and insect pest analysis model, and obtaining a first disease recognition result; presetting similar pest and disease combination and corresponding combined spot features, judging whether the similar pest and disease combination exists in the first pest and disease recognition result, if not, outputting the first pest and disease recognition result, and if the first pest and disease combination exists, extracting a spot area of the to-be-judged crop image; traversing spot areas of the to-be-judged crop image, and if joint spot features appear in the spot areas, taking two diseases and pests in the similar disease and pest combination as output results; if the combined spot features do not exist, the pest and disease damage with the minimum confidence degree in the similar pest and disease damage combinations is removed; and updating the first pest identification result.

The invention discloses a crop pest target detection method and system based on multi-scale feature and cross-level feature interaction. The method comprises the following steps: firstly, acquiring high-level fusion features and low-level fusion features through adaptive pooling and multi-scale convolution operation by using multi-scale features of an input image; the features are then subjected to cross-layer feature interaction processing, and context association between the features is enhanced through cross-layer interaction, so that a plurality of collaborative fusion features are obtained. Thirdly, optimizing the collaborative fusion features so as to further improve the characterization capability of the features and obtain a plurality of optimized features; and finally, combining a plurality of optimized features through a feature fusion strategy to form a positioning prediction map of the camouflage object. According to the invention, efficient and accurate identification and positioning of crop pests are realized, and the overall performance of cooperative camouflage target detection is greatly improved.

The invention discloses a crop pest detection method and device based on MS-YOLO, and the method comprises the steps: collecting crop pest images to construct a data set, and dividing the data set into a training set, a verification set and a test set; the method comprises the following steps: improving a YOLOv11 network model, and constructing an MS-YOLO network model; inputting the training data set into the MS-YOLO network model, carrying out preprocessing operation, and carrying out the training of the MS-YOLO network model; and performing performance verification on the trained MS-YOLO model by using the test data set, and finally outputting a detection result of crop insect pests. The device comprises a processor and a memory. According to the invention, the problem of poor detection effect caused by small crop pest targets and various sizes in the existing work is solved.

The invention discloses a lightweight CNN (Convolutional Neural Network) image classification method and a lightweight CNN image classification device for assisting agricultural rapid detection. The classification method comprises the following steps: step 1, designing a crop disease and insect pest detection network; 2, designing a patch merging module; 3, designing a convolution block module; 4, preparing an experimental data set; 5, testing the performance of the model; step 6, model performance index determination; and 7, comparing the model parameter quantity with the calculated quantity. The lightweight CNN image classification device is composed of an edge calculation host, a high-precision image acquisition system and a high-speed communication module, and a complete edge calculation solution is formed. The edge computing host adopts NVIDIA JetsonAGX Orin as a core computing unit, integrates a 12-core ARM Cortex-A78AE CPU (central processing unit) and an Ampere architecture GPU (graphics processing unit) with 2048 CUDA (compute unified device architecture) cores, is equipped with a 64GB LPDDR5 memory, and has the beneficial effects that efficient detection and accurate recognition are realized; edge calculation is adaptive, low in consumption and high in efficiency; a data fusion and model optimization mechanism; and light weight and hardware adaptive design are realized, so that good practicability and adaptability are realized.

The invention discloses a crop disease and insect pest detection algorithm fusing self-attention and a sample weighting mechanism. A YOLOv8 network is used as a basic model; through a two-branch self-attention DF-MSA architecture, the ability of the model to acquire pest target position information is enhanced; a cross-layer feature fusion module is introduced into the feature pyramid network structure, and multi-scale information is fused; and a sample weighting function is adopted to reduce the influence of difficult sample imbalance on a detection result, so that the detection accuracy is improved. And inputting the trained IP-YOLO by using a test set sample, and outputting a result. The method has high stability and robustness, and an effective means is provided for improving the performance of a crop disease and pest detection system.

The invention relates to the technical field of target identification, in particular to a BiFPN and multi-scale attention mechanism-based crop pest identification method and system, and the method comprises the steps: obtaining an agricultural insect image data set covering multiple scenes, carrying out the division and multi-strategy data enhancement processing of the data set, and generating a training set, a verification set and a test set, through staged cooperation of a convolution unit and a self-attention unit, multi-scale initial features containing local details and global semantics are output, an improved space attention mechanism and a channel attention mechanism are applied to the fused multi-scale features, the enhanced multi-scale features are input into a fine-grained classification module, and a fine-grained classification result is obtained. Through discriminative feature selection and feature aggregation, the model is trained and optimized by adopting a triple collaborative loss function, and by screening high-discriminative feature points and aggregating feature association information through a graph convolutional network to strengthen inter-class differences, various types of related pests can be accurately distinguished.

The present invention provides a crop pest and disease identification method and system based on image feature recognition, which relates to the technical field of image recognition and processing. The present invention uses a pre-trained pest and disease analysis model to identify pests and diseases in a crop image to be identified, and obtains a first pest and disease identification result; presets similar pest and disease combinations and corresponding joint spot features, and determines whether there is a similar pest and disease combination in the first pest and disease identification result; if there is no similar pest and disease combination, outputs the first pest and disease identification result; if there is the first pest and disease combination, extracts a spot area of ​​the crop image to be identified; traverses the spot area of ​​the crop image to be identified, and if a joint spot feature appears in the spot area, both pests and diseases in the similar pest and disease combination are used as output results; if there is no joint spot feature, eliminates the pest and disease with the lowest confidence in the similar pest and disease combination; and updates the first pest and disease identification result.

The invention discloses a crop pest detection method and device, a detection model training method and device, equipment and a medium. The method comprises the following steps: performing first down-sampling on a crop image to obtain a first extraction feature; performing feature extraction on the first extraction feature to obtain a second extraction feature; the receptive field of the second extraction feature is larger than that of the first extraction feature; performing up-sampling on the first extraction feature to obtain a third extraction feature; determining a target feature according to the third extraction feature and the second extraction feature; and carrying out classification processing on the target features to obtain an insect pest area in the crop image. According to the technical scheme of the embodiment of the invention, the accuracy of crop pest detection can be improved.

The invention relates to the technical field of agricultural data analysis, in particular to a crop disease and insect pest data prediction method and system, and the method comprises the following steps: obtaining environment parameters of a planting area, positioning a mutation point, determining a species diffusion risk range, carrying out the statistics of temperature and humidity change frequencies, obtaining species diffusion frequency data, and recognizing environment factor correlation indexes. Screening a deviation node set, analyzing the response time difference, carrying out clustering analysis, and generating a dynamic change prediction data set. According to the method, by accurately capturing environmental parameter changes and monitoring key nodes of a propagation path in real time, the species diffusion range and intensity can be efficiently identified, pest and disease damage occurrence can be accurately predicted, pest and disease damage trends in different areas can be effectively predicted, and the limitation that a traditional method depends on meteorological data and historical records is overcome; the node density change is evaluated and normalized through a clustering analysis method, the dynamic change prediction precision of the planting area is improved, and more effective decision support is provided for agricultural managers.

The invention relates to the technical field of agriculture, in particular to an agricultural disease and pest intelligent monitoring system and method fusing big data and a generative adversarial big model, which can effectively identify and early warn crop diseases and pests. The system comprises a data acquisition unit; the data processing unit comprises a data cleaning module for performing data denoising, enhancement and correction on the related information of the crops; the data labeling module is used for labeling disease and insect pest images; the feature engineering module is used for extracting pest image features and crop environment features; the model training unit comprises a data sample enhancement module which generates a pest and disease simulation image based on a real pest and disease image according to a generative adversarial large model optimization mode, and improves the training stability and visual similarity according to the difference between the two images. The multi-mode identification module is used for fusing the pest image features and the environment features to generate pest identification feature vectors for identifying pests; and the intelligent application unit identifies disease and pest conditions according to the disease and pest identification feature vectors and gives out early warning.

The invention provides a bactrocera minax forked head transcription factor FOXO as well as a coding gene and application thereof, and belongs to the technical field of biological prevention and control of crop pests. A nucleic acid molecule of the citrus fruit fly forked head transcription factor FOXO is cloned, dsRNA for interfering the nucleic acid molecule of the citrus fruit fly coded forked head transcription factor FOXO is synthesized, and the dsRNA is introduced into the citrus fruit fly by using an injection method and a feeding method to carry out RNAi on the citrus fruit fly forked head transcription factor gene FOXO. After the dsRNA is injected or fed to the bactrocera minax, the survival rate, the growth rate, the weight and the overall expressive force of the bactrocera minax are remarkably reduced, and it is indicated that the fork head transcription factor FOXO of the bactrocera minax plays an important role in the growth and development process of insects. The invention provides a new method for molecular regulation and control of pests, and also provides a theoretical basis for deeply understanding the growth and development mechanism of insects and creating a new biopesticide preparation.

A crop pest monitoring and intelligent spraying control method based on deep learning is characterized by comprising the following steps that farmland images and environment data are collected in real time through a multispectral camera and an environment sensor, and the environment data comprise the temperature and humidity, the illumination intensity and the wind speed; performing pest detection on the image based on an improved YOLOv5 model, filtering a redundant detection frame by adopting a non-maximum suppression (NMS) algorithm, and dynamically adjusting a detection confidence threshold; generating a pest density thermodynamic diagram according to a detection result, and calculating a spraying priority through a fuzzy logic control algorithm in combination with environmental parameters; the flow and the direction of the spraying device are dynamically adjusted through a PID controller, and PID parameters are adaptively adjusted according to the real-time wind speed and the target coverage rate; data are collected through an unmanned aerial vehicle and an environment sensor, pest recognition is conducted through an improved YOLOv5 model, and precise spraying and intelligent decision making are achieved in combination with a fuzzy control algorithm and a path optimization technology, so that the intelligent level of pest control is improved.

The utility model relates to a natural enemy insect carrier plant system device, which belongs to the technical field of crop pest control and is characterized in that a rain cover is mounted at the top of a tray box through a plurality of adjusting rods, a lifting ring is mounted at the top of the rain cover, a seedling raising plate is mounted in the tray box, the seedling raising plate is of a conical structure, and the lifting ring is mounted at the bottom of the tray box. A plurality of seedling raising holes are formed in the top of the seedling raising plate, the seedling raising holes extend to the bottom of the seedling raising plate and are communicated with the tray box, and a water absorption sponge is further arranged in the tray box and located at the bottom of the seedling raising plate. The parasitic wasps are attracted, and meanwhile, the population is expanded, so that the pest control sustainability and efficiency of the parasitic wasps are improved.

The invention relates to a meteorological soil factor-based crop pest spatio-temporal prediction method and device. The method comprises the following steps of: acquiring daily pest information and meteorological soil data in a full life cycle of crops; performing data checking and preprocessing on the meteorological soil data to generate research area meteorological soil raster data with consistent spatial resolution; and training an insect pest prediction model by using the training set, and taking the meteorological soil raster data as input data of the insect pest prediction model to realize insect pest quantity prediction. The crop insect pest time-space prediction method and the crop insect pest time-space prediction device provided by the invention have the beneficial effects of effectively solving the problems of excessive dependence on expert experience and complexity in relation between prediction factors and insect pests, surpassing the limitation of previous small-range prediction, and realizing accurate prediction of crop insect pest time sequence and spatial distribution.

The invention discloses a crop insect pest detection method and system based on improved ConvNeXt, and relates to the technical field of crop insect pest detection, and the method comprises the steps: constructing an EDA-ConvNeXt model, training the EDA-ConvNeXt model to obtain a crop insect pest detection model, according to the method, the detection precision is improved through the EDA-ConvNeXt model, the multi-scale features are captured by the backbone network, the boundary information is enhanced by the edge enhancement branch, the dynamic feature extraction branch adapts to the morphological change, and the key features are highlighted by the multi-branch fusion. The classification layer ensures the recognition reliability, and finally higher accuracy is achieved in a complex field scene.

The invention discloses a crop disease and pest image recognition system based on deep learning, and relates to the technical field of image processing, and the system comprises a data collection module which is used for collecting disease and pest image data of various crops under different environment conditions; according to the method, the adversarial network is designed to generate a highly vivid pest image, the image is visually similar to a real image, and consistency can be kept in pathological features, so that a training data set is greatly enriched, and effective knowledge in a large teacher model is extracted and transmitted to a student model through a knowledge distillation technology, so that the training efficiency is improved. According to the invention, the method achieves the light weight of the model, greatly reduces the parameter quantity and calculation complexity while maintaining the similar performance of the student model, and enables the student model to still maintain higher recognition accuracy while reducing the calculation burden through knowledge distillation.

The invention provides a crop disease and insect pest image acquisition point planning method based on an unmanned aerial vehicle, and the method comprises the steps: obtaining image data of a crop planting region through the unmanned aerial vehicle or a satellite at different time nodes in a specified time period, constructing an image data set based on a time sequence, and carrying out the preprocessing; inputting the processed image data set into a growth vigor analysis model for processing to obtain crop growth vigor information, analyzing crops with abnormal growth vigor according to the crop growth vigor information and determining positions, planning an unmanned aerial vehicle route according to the positions of the crops with abnormal growth vigor, and controlling the unmanned aerial vehicle to advance according to the unmanned aerial vehicle route. High-resolution images of corresponding crops with abnormal growth vigor are collected when the high-resolution images pass through collection sites in the air route, the high-resolution images are input into a pest and disease damage analysis model to be processed, pest and disease damage identification results are obtained, and pest and disease damage prediction information and prevention and control suggestions are generated according to the pest and disease damage identification results and output. And a user is assisted to take reasonable pest control measures in time.

The invention provides a crop disease and insect pest recognition method and device and a prevention and control scheme generation method and device, and belongs to the field of agricultural disease and insect pest prevention and control. Performing pest target segmentation on the to-be-identified pest image based on the large segmentation model to obtain a segmentation mask; and performing pest type identification according to the segmentation mask and the to-be-identified pest image to obtain a pest classification result. According to the method, the contour of a pest and disease target, especially a small target, is efficiently and accurately identified and segmented by segmenting the image segmentation capability of the large model, the corresponding segmentation mask is generated, the type identification is performed according to the obtained segmentation mask result, the position of the pest and disease target is effectively focused in the type identification process, and the identification efficiency is improved. In this way, the image segmentation capability of a segmentation large model and the classification capability of an identification model are combined, the advantages of different models are fully utilized, and the detection effect on a small target is enhanced.