119 results about "Crop pest" patented technology

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 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 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 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.

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 utility model discloses a physical trapping device for crop insect pests. The physical trapping device comprises a cavity, an insect trapping module, insect sticking paper and a winding and unwinding mechanism, wherein the insect trapping module, the insect sticking paper and the winding and unwinding mechanism are arranged in the cavity. A plurality of holes are distributed in the side wall of the cavity at equal intervals, and the sizes of the holes enable pests to enter the cavity from the outside of the cavity; the pest trapping module is arranged in the cavity to attract pests located outside the cavity to move towards the interior of the cavity; the insect sticking paper is arranged in the cavity and wraps the insect trapping module, an adhesive layer of the insect sticking paper faces the cavity to form an insect catching area, and insect trapping information of the insect trapping module can penetrate through the insect sticking paper to be diffused to the outside of the insect sticking paper; the winding and unwinding mechanism is arranged in the cavity and can wind the insect sticking paper stuck with the pests in the insect catching area and unwind the sticky insect sticking paper to cover the insect catching area, and the insect sticking paper is automatically replaced regularly through the winding and unwinding mechanism.

The present application relates to the technical field of intelligent identification system of crop pests, in particular to a plant field pest fine-grained identification method, system, device and storage medium based on deep learning. The identification method provided by the present application is specialized in high-precision identification of real field scenes, and can provide technical support for important work such as future development of field inspection robot, automatic identification and monitoring system of field pests and the like. In addition to pest monitoring, the field biological safety test of genetically modified plants is gradually carried out at present, and by using the identification method provided by the present application, the dynamic change of farmland insect community can be quickly and accurately identified and predicted, so that the efficiency and accuracy of ecological investigation are greatly improved.

This invention relates to the fields of pesticide formulations and pesticide pest control, specifically to a composite nano-slow-release suspension of emamectin benzoate and chlorantraniliprole, its preparation, and application. The nano-slow-release suspension comprises emamectin benzoate, chlorantraniliprole, a nanocarrier, a wetting agent, a dispersant, a synergist, a thickener, an antifoaming agent, an antifreeze, a preservative, and water. The nanocarrier is an NX-CMCS & MOF composite nanocarrier. Spraying this composite nano-pesticide onto crops improves the adhesion and penetration of the pesticide on leaves, enhances its resistance to rain washout, and effectively kills major crop pests such as bollworms, diamondback moths, cowpea thrips, and rice planthoppers. Compared with traditional pesticide formulations, it has a significant effect of reducing dosage and increasing efficacy, and has broad application prospects in the field of agricultural pest control and promoting green agriculture.

The utility model relates to portable crop pest killer technical field especially portable crop pest killer, including pest killer shell, the pest killer shell is provided with connecting mechanism, the connecting mechanism contains the first assembly of setting in the pest killer shell upper section, the second assembly is provided with in the pest killer shell middle section, the third assembly is provided with in the pest killer shell lower section. Portable crop pest killer adopts the physical pest killing mode of double pest killing lamp first pest killing lamp and second pest killing lamp combination airflow suction, avoids the use of chemical pesticide, effectively reduces the pollution to the ecological environment such as soil, water source, the top drive motor drives the fan blade to produce downward airflow, the bottom exhaust fan provides strong suction, both cooperate with each other, accelerate the pest to move to the pest killer inside and transport to the collecting cylinder, again cooperate with the control of the falling rhythm of the pest of rotary plate and fixed baffle, significantly improve the pest trapping and collection efficiency, and the pest killing effect is greatly promoted.

The invention discloses a paddy field planthopper trapping and collecting device, and belongs to the field of crop pest governing, the paddy field planthopper trapping and collecting device comprises a mounting table, a lifting rod structure is mounted at the bottom of the mounting table, a collecting structure is mounted on the end face of the lifting rod structure, and a blue light lamp is mounted on the collecting structure; the lifting rod structure comprises an inner cylinder mounted at the bottom of the mounting table and a supporting outer cylinder mounted on the outer wall of the inner cylinder in a sliding manner; by arranging the lifting rod structure, the height adjusting function of the plant hopper trapping and collecting device is achieved, and the problems that the height of an existing pest trapping device for the paddy field cannot be adjusted, the pest trapping device cannot be adjusted according to rice of different height qualities, and the use flexibility of the pest trapping device is poor are solved; the height of the plant hopper trapping and collecting device can be adjusted according to the use of rice with different heights, so that the use flexibility of the trapping and collecting device is improved.

The present invention relates to the technical field of pesticides, and in particular to an active foliar fertilizer with insecticidal efficacy. An insecticidal composition, characterized in that its active ingredient is a binary compound of cyclohexaprid and farnesol. The insecticidal mechanisms of farnesol and other active ingredients in the insecticidal composition of the present invention are different. When used in combination, they can effectively delay the development of insecticide resistance and extend the service life of the insecticidal composition. Secondly, the pest control effect can be improved, the amount of active ingredients used can be reduced, and the cost of pest control can be reduced. In addition, the active foliar fertilizer of the present invention contains both insecticidal ingredients and water-soluble fertilizer ingredients. When used, it can not only prevent and control crop pests, but also provide nutrients for crops. The two cooperate with each other to improve the yield and quality of crops.

The present invention belongs to the field of pesticide insecticide technology and discloses an insecticidal composition containing trifluanid and its application. The active ingredients in the insecticidal composition include active ingredient A and active ingredient B, wherein active ingredient A is trifluanid and active ingredient B is either bifenthrin or isoxathiapiprolin. The mass ratio of active ingredient A to active ingredient B is 1:50 to 30:1. The insecticidal composition has excellent control effects on various crop pests. Due to the emergence of pests resistant to existing drugs, this application develops a new type of agricultural and horticultural insecticide.

This invention discloses a biological combination preparation for controlling crop pests and its application, belonging to the field of agricultural biotechnology. The biological combination preparation includes the plant inducer alpha-linolenic acid (ALA) and the pesticide chlorantraniliprole. For application, each 1g of dry seeds is thoroughly mixed with 1mL of an ALA solution at a concentration of 4-400mg / L and 2.5-5mg of a 20% chlorantraniliprole suspension before sowing. This invention is the first to utilize ALA in synergistic seed treatment with chemical pesticides, achieving a high lethality against pests while reducing the amount of chemical pesticides used. The treatment method of this invention does not affect seed germination rate or crop growth and development; it reduces the amount of chemical pesticides used by half compared to conventional seed treatment methods; and it is an intensive, simple, and labor-saving operation with significant social and ecological benefits.

The present application relates to the field of agricultural informatization and intelligent information processing technology, in particular to a pest control level evaluation method and system based on a large language model. The method comprises: obtaining crop pest control technical rule texts, inputting the texts into a first large language model after cutting by a sliding window to extract substantive semantic units; clustering the substantive semantic units into multiple topic clusters after encoding them into high-dimensional semantic vectors; inputting the topic clusters into a second large language model after hierarchical sampling, and outputting three-level indicators with semantic confidence; inputting the three-level indicators into a third large language model to obtain two-level indicators, constructing an initial evaluation system and calculating weighted three-level indicators; further constructing a complete pest control level evaluation system; obtaining pest control data of an area to be evaluated, and evaluating based on the system. The present application can accurately extract substantive technical rules from functionally heterogeneous texts, and improve the construction accuracy of the evaluation system and the authenticity of the evaluation results.

This invention discloses a method and device for risk assessment of pests and diseases in facility agriculture, belonging to the field of agricultural monitoring technology. The invention comprises five stages: device deployment, multi-source heterogeneous data acquisition, creative data processing, creative risk assessment, risk output, and linkage with prevention and control. It integrates an improved ResNet50 and Outlook attention mechanism to construct a high-precision identification model for multiple crop pests and diseases in facility agriculture. Combined with an adaptive fusion algorithm for multi-source heterogeneous data, a dynamic weight adjustment formula based on the pest and disease life cycle and identification accuracy, and a full-cycle risk evolution prediction model, it achieves a complete closed loop of "identification-fusion-assessment-control-feedback-optimization". This invention constructs a dedicated identification model for multiple crop pests and diseases in facility agriculture by integrating an improved ResNet50 and Outlook attention mechanism. Combined with image data enhancement and transfer learning optimization, it improves the accuracy of multi-crop pest and disease identification and the relevance and accuracy of risk assessment, while reducing the cost of manual inspection.

The invention discloses an ecological regulation and control method for rice planthoppers. The invention relates to the technical field of crop insect pest prevention and control. Specific weeds are introduced to ridges of a paddy field. The specific weed is selected from any one of lemongrass and sesbania. Based on the concept of ecological regulation and control of pests, specific weeds are cultivated on ridges to conserve predatory natural enemies of the rice planthoppers, then the ecological control effect of natural enemies on the rice planthoppers is achieved, planting is relatively simple, the cost is low, use of insecticides is reduced fundamentally, and the ecological control effect of the rice planthoppers is achieved. The problem of non-target biological safety of the insecticide is effectively solved, and the safety and quality of rice products are remarkably improved.

The utility model discloses a crop disease and pest control box, and particularly relates to the technical field of disease and pest control, the crop disease and pest control box comprises a box body, a support frame is fixedly arranged in the box body, a trap lamp and a power grid are fixedly arranged at the upper end of the support frame, and the power grid is located on the outer surface of the trap lamp; a scraping and cleaning device capable of scraping pest corpses on the surface of the power grid is arranged on the outer surface of the power grid and is used for scraping and cleaning the pest corpses on the surface of the power grid; the scraping cleaning device comprises a fixing ring, the fixing ring is movably arranged on the outer surface of the power grid, an insulating brush is fixedly arranged on the inner wall of the fixing ring, and the insulating brush is tightly attached to the outer surface of the power grid; two reciprocating scraping devices capable of driving the insulating brushes to reciprocate are arranged on the inner side of the box body and used for driving the insulating brushes to scrape the surface of the power grid in a reciprocating mode. Through the cooperation of the scraping cleaning device and the reciprocating scraping device, the insulating brush is driven to scrape the power grid in a reciprocating mode, and pest corpses can be scraped off from the surface of the power grid.

The utility model relates to the technical field of crop pest control, in particular to a blocking net pest killing device for crop pest control, which comprises a bottom plate, a support mounting mechanism is arranged at the bottom end of the bottom plate, a mounting frame is fixed at the top end of the bottom plate, and a blocking net is movably mounted in the mounting frame. The device has the beneficial effects that the surface of the blocking net is brushed and cleaned by brushing strips to clean diseases and insect pests and dust adhered to the surface of the blocking net, so that the surface of the blocking net is kept clean, and the diseases and insect pests are killed through accurate and efficient electric shock of current; the inclination angle of the stabilizing plate can be adjusted according to the flatness of the land, so that the bottom end of the stabilizing plate obliquely abuts against and is inserted into the land, the vertical rod is stably supported and reinforced, and therefore the bottom plate, the mounting frame and the blocking net are stably mounted and arranged.