69 results about "Crop pest" patented technology

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 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 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 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 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 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.

The invention discloses a crop pest intelligent identification method and detection system, and the method comprises the steps: collecting crop pest image data, carrying out the timing image collection in a crop growth region through an unmanned aerial vehicle and ground camera equipment, obtaining the image data of a plurality of typical pests, the method comprises the following steps: constructing an insect pest image training set and an annotation data set, manually annotating an image by adopting a LabelImg tool, constructing a standard data set in a YOLOv8 input format, annotation content including insect pest target frame position and category label information, and constructing a YOLOv8 target detection model; by constructing the special crop pest image data set and combining image enhancement and standardization processing, the recognition capability of the model for various types of pests in a complex agricultural environment is improved, target labeling is performed by using a YOLO format, so that the data is highly matched with the model structure, and the training efficiency and accuracy are guaranteed.

The invention discloses a crop pest trapping and killing device for agricultural plant protection, and relates to the technical field of agricultural plant protection. The crop pest trapping and killing device for agricultural plant protection comprises a base, and a light-transmitting conductive plate with a pest trapping lamp is assembled on the top of the base; the electric cleaning brush is assembled on the side face of the base; a rotating rod is rotationally connected into the base, a transmission part used for transmission is assembled between the electric cleaning brush and the rotating rod, and a pressing plate is assembled on the outer side of the rotating rod. According to the crop pest trapping and killing device for agricultural plant protection, a light-transmitting current-conducting plate with a pest trapping lamp and an electric cleaning brush are started and matched with a first torsional spring rod, a first transmission rod, a stress rod, a second transmission rod, a sleeve rod, a rotating rod, a rotating disc and a fixing rod, a pressing plate can be intermittently attached to a base, and pests falling to the position are ground once; the pest trapping and killing efficiency of the device is improved.

This invention discloses a method and system for identifying crop pests based on visual recognition, belonging to the field of pest identification technology. A crop pest identification system based on visual recognition includes a crop visual processing module and a crop pest identification module. This invention constructs a multi-source information collaborative judgment mechanism by integrating visual image features, volatile gas concentrations, and environmental parameters, effectively distinguishing between physiological abnormalities and pathogenic diseases, reducing misjudgments and missed judgments caused by environmental interference, and significantly improving the accuracy and reliability of identification results. By combining gas sensor data to analyze crop respiration characteristics and maturity status, it can identify crop physiological abnormalities before visual symptoms appear, achieving early detection and warning of pests. Dynamically adjusting the identification threshold and sensitivity according to different growth stages supports targeted prevention and control decisions, improving the timeliness of prevention and control.

The invention belongs to the technical field of agricultural pest control and provides a method for controlling facility crop pests by using functional plants. The method comprises the following steps: transplanting cnidium monnieri seedlings into a crop field before field planting or seedling emergence of crops; hanging the cnidium monnieri in the potted flowering period above crops planted on the ground, and replacing the cnidium monnieri with a new potted cnidium monnieri in the flowering period after the cnidium monnieri enters a green fruit period; releasing egg cards of natural enemies, orius sativus, encarsia formosa and harmonia axyridis 3 days after the potted snake bed is placed; when the cnidium monnieri on the ground enters the jointing stage, cnidium monnieri seeds are sown in the original position. According to the method, the crop yield is not reduced, meanwhile, damage caused by outbreak of pest populations is avoided, meanwhile, use of chemical insecticides is reduced, and green production of crops is promoted. The functional plant cnidium monnieri is planted in the greenhouse and used for conserving natural enemy insects released in the greenhouse, conserving of the natural enemy insects and prevention and control of pests can be considered at the same time, population growth of the natural enemy insects is promoted, and the cost of manually releasing natural enemies is reduced.

This invention relates to the field of crop pest identification, specifically to a pest identification device and method capable of real-time removal of pest carcasses, thereby improving the accuracy of pest identification. The solution includes an insecticidal lamp body 1, an insect-attracting lamp 2, a voltage detection module, a control module, and a pest identification module. The insecticidal lamp body 1 includes an insect collector 5, a device box 103, and a high-voltage grid 104. The pest identification module includes a camera 3 and an edge computing board. The device box 103 is fixedly connected to the high-voltage grid 104. The insect collector 5 is installed below the high-voltage grid 104. The voltage detection module, control module, and edge computing board are installed in the device box 103 and electrically connected to the control module. The camera 3 is fixed below the insect-attracting lamp 2 and electrically connected to the edge computing board. The edge computing board deploys an improved YOLOv8-based pest identification model for pest identification. This invention is applicable to crop pest identification.

This invention discloses an AI-driven method for precise control of the oriental fruit moth during its growth stages, belonging to the field of crop pest and disease control technology. Addressing the shortcomings of poor targeted control, extensive pesticide application, and lack of precise decision-making and closed-loop optimization in peach orchards in the Jiangsu and Zhejiang regions, this invention constructs a four-dimensional feature library containing crop, pest, environment, and control methods (each dimension labeled with plant protection-related weights). Through a multimodal AI model fusing visual, sensor, and phenological data, it achieves precise determination of the oriental fruit moth throughout its entire growth stage (accuracy ≥95%). Using weighted collaborative filtering and the NSGA-III multi-objective optimization algorithm, it dynamically matches the optimal combination of microemulsion pesticides, physical control, and auxins, meeting the targets of control efficacy ≥85%, pesticide reduction ≥30%, cost ≤15 yuan / mu, and phytotoxicity rate ≤0.5%. A closed-loop system is formed through a field efficacy feedback iterative model and the feature library. This method improves peach yield and quality, reduces pesticide residues and costs, is suitable for the climate of Jiangsu and Zhejiang, and has broad application prospects.

The invention discloses a pest recognition model and a pest recognition method based on improved YOLOv11, belongs to the technical field of crop pest recognition, and solves the problems that a YOLOv11 model in agricultural pest recognition in the prior art is large in parameter quantity, difficult to deploy on edge equipment, high in small target pest omission ratio and high in pest recognition efficiency. And the positioning error of the bounding box in a dense scene is large. According to the method, accurate recognition is realized through Ghost convolution and Ghost Bottleneck lightweight trunks, FPN-PAN fusion features, EMA attention enhancement small target response, Focus-CIoU optimization regression and image preprocessing. The method adapts to agricultural edge equipment through lightweight design, enhances small target pest recognition, optimizes the precision of a dense scene bounding box, improves the pest recognition efficiency and accuracy, and adapts to a complex farmland environment.

The invention belongs to the technical field of pesticides, and particularly relates to a pesticide formula for efficiently preventing and treating insect pests of crops, which is prepared by compounding lapachol and emamectin benzoate or afidopyropen. Wherein the compounding mass ratio of the lapachol to the emamectin benzoate is (1-8): (16-1); the compounding mass ratio of the lapachol to the afidopyropen is (1-3): (18-1). The lapachol and emamectin benzoate or afidopyropen show a synergistic effect in a certain mass ratio, which indicates that the medicament formula can improve the prevention and treatment effect on crop insect pests, can reduce the application dosage, can reduce the risk of drug resistance, prolongs the service life of the medicament, and reduces the cost. A support can be provided for developing a novel efficient medicament for preventing and controlling crop insect pests, namely spodoptera frugiperda.

The invention provides a pest and disease damage accurate monitoring and recognition system, and relates to the field of agricultural intelligent robots, the pest and disease damage accurate monitoring and recognition system comprises a searchlighting recognition system, a walking mechanism, a mechanical arm assembly, an electric water pump, a central control system, a liquid medicine box, a chassis and a sensing system.The specific recognition control method comprises the steps that 1, addition correction is conducted firstly, then binocular stereoscopic vision calibration is conducted, and a calibration result is obtained; 2, monitoring insect pests of the crops in real time by adopting an improved YOLOv8 algorithm, and identifying corresponding types and numbers of the insect pests; 3, acquiring three-dimensional space coordinates of the insect pest image through the insect pest image; and 4, controlling the dosage of the pesticide according to the three-dimensional space coordinates and the corresponding types and quantity of the insect pests. The system can carry out all-weather and all-directional insect pest recognition, is high in detection precision and high in real-time performance, and effectively improves the accuracy and timeliness of cotton bollworm pest prevention and control.

The invention relates to the technical field of disease and pest control, and provides a crop disease and pest sample collector which comprises a floating plate, a support is fixedly connected to one side of the floating plate, a bottom plate is fixedly connected to the upper portion of the support, a transfer cabin is fixedly connected to the upper portion of the bottom plate, and a switching frame is rotatably connected to the middle of the transfer cabin. A storage box is arranged at one end of the switching frame; by arranging the floating plate to be matched with the grading baffle structure of the survival cabin, the device can stably float in the paddy field environment to meet the sample collection requirements of aquatic crops such as rice, grading storage can be achieved according to the aquatic adaptability of different pests, and sample damage caused by unified and centralized storage is avoided; the integrity and survival state of the sample are guaranteed, and reliable materials are provided for subsequent identification and analysis.

The utility model relates to a crop disease and insect pest identifying and sampling device which comprises a sampling part and a clamping part arranged at the bottom of the sampling part, a shooting device is arranged in the sampling part, the clamping part is arranged at the bottom of the sampling part, and the clamping part moves up and down in the sampling part. The clamping part is used for clamping the corresponding part of the crop at the bottom of the sampling part and then carrying out shooting and sampling operation. The device is small in overall structure and convenient to carry, can shoot and sample different parts of crop plants in real time, and is a good device for identifying and sampling crop diseases and insect pests.

The present application relates to the technical field of pest control, and particularly relates to a pest outbreak process mining risk prediction method and system based on multiple source factors. The method comprises the following steps: measuring the regional meteorological data corresponding to the crop pest monitoring point, the regional crop soil fertility degree and the regional pest occurrence data in real time; obtaining the corresponding pesticide spraying usage of the crop pest monitoring point, and performing regional occurrence density accounting and pest outbreak mining analysis on the regional pest occurrence data based on the pesticide spraying usage, to obtain the corresponding regional pest outbreak characteristic mode under the meteorological condition and the correlation characteristic relationship between the regional soil fertility and the pest outbreak; performing multi-source factor risk prediction and prevention and control decision support analysis on the crop pest monitoring point, to generate a pest outbreak area prevention and control suggestion scheme. The present application can provide scientific and accurate pest risk early warning for agricultural production.

The invention relates to the technical field of crop disaster early warning, and provides a crop pest early warning method and system based on multi-modal perception. The method comprises the following steps: synchronously acquiring image and audio signals and preprocessing to obtain a standardized data packet; extracting visual feature vectors and acoustic feature vectors from the standardized data packet, and combining the visual feature vectors and the acoustic feature vectors into a node feature set; performing cross-modal fusion judgment on the basis of the node feature set, and generating a node state list containing pest existence and activity intensity indexes; performing spatial interpolation and time sequence prediction on the node state list, and constructing a pest dynamic risk field containing prediction risk information; and based on the pest dynamic risk field, applying a preset threshold to carry out risk partitioning, and generating a structured early warning instruction set. According to the method, a regionalized and prospective quantitative prevention and control basis is provided for farmland insect pests.

The application discloses a kind of crop pest control equipment and pest control vehicle, belong to pest control equipment technical field.The present application provides a kind of crop pest control equipment including control frame, soil loosening mechanism and pest control mechanism, on the one hand, the soil is inserted hole by soil loosening mechanism, it is convenient to make pest to crawl out, to facilitate the management of pest in land, on the other hand, the cover of pest control mechanism is in contact with land and covers the crop and land, then the loose land is sprayed with pesticide assembly to attract insecticide, to realize the pest in land is attracted to ground, and then cooperate with physical method and biological method to de-insect.

The application belongs to the technical field of crop pest prediction, and particularly relates to a crop pest detection method and system based on YOLO11 lightweight improvement, wherein the method comprises the following steps: adjusting and optimizing a down-sampling module in a backbone network and a feature fusion module in a neck network of a YOLO11 network structure, introducing an ADown down-sampling module, an IEMA multi-scale attention module and a DySample dynamic up-sampling module, and obtaining an improved AID-YOLO lightweight target detection network model; labeling a crop pest image obtained in advance after data enhancement, wherein the crop pest image comprises indoor collected images and field collected images, and constructing a crop pest image dataset based on the labeling result; and training the improved AID-YOLO lightweight target detection network model based on the crop pest image dataset to realize detection and identification of a crop pest target. The application can realize rapid and accurate identification of crop pests in a complex scene, and provide technical support for crop pest monitoring and precise prevention and control.

The utility model relates to an intelligent identification device for crop pest control, which comprises a moving trolley assembly, an intelligent assembly is arranged at the upper end of the moving trolley assembly, and a marking assembly is arranged at the rear end of the intelligent assembly. According to the scheme, a vehicle body of a movable trolley assembly is matched with a stable bottom plate and forward wheels distributed in a rectangular mode, the movable trolley assembly can stably walk on ridges of a crop planting area, the crop disease and insect pest monitoring efficiency is greatly improved, and the crop disease and insect pest monitoring efficiency is greatly improved. An electric push rod in the intelligent assembly can adjust the height of a transverse linkage frame according to the height of crops, so that an intelligent recognition camera is located at a proper recognition position, the self-adaptive adjustment function ensures that the camera can clearly and accurately recognize the state of the crops, the monitoring accuracy and effectiveness are improved, and the monitoring efficiency is improved. And diseases and insect pests of crops can be found in time.

The invention discloses a field automatic inspection crop typical pest information acquisition robot suitable for tropical economic crops (mangoes, bananas, coffee and the like). The robot comprises a movement system, a control system and a pest information acquisition and inspection system. The movement system comprises a driving mechanism, a steering mechanism and a rack, and can complete movement of the robot and carrying of devices such as an industrial personal computer and a sampling camera; the control system comprises a walking system, an inspection and information acquisition system and a man-machine interaction system, and is mainly used for controlling the whole robot; the pest and disease information acquisition and inspection system comprises path identification, path planning and crop pest and disease information acquisition, and can realize identification and acquisition of crop pest and disease information. The robot is light in overall weight, low in cost and simple in design structure, and the cost for obtaining tropical economic crop orchard pest and disease information is greatly reduced; meanwhile, the sampling camera positioning mechanism can realize multi-angle monitoring of the camera, so that the disease and pest identification accuracy is improved.

The utility model discloses a novel crop pest control device which comprises a control box, and electric push rods are symmetrically installed at the top end in the control box. The device has the beneficial effects that the electric telescopic rod is arranged on the protective cover, if rainwater firstly drips on the raindrop sensor in rainy days, the raindrop sensor transmits a signal to the main controller, the main controller sends an instruction to the electric telescopic rod, and then the electric telescopic rod drives the prevention and treatment box to fall into the protective cover; the bottom end of the mounting frame abuts against the top end of the protective cover for protection, so that rainwater can be prevented from falling into the control box and being mixed with the attractant to dilute the attractant, and therefore the pesticide effect of the attractant can be prevented from being reduced; an electric push rod drives a dredging frame to descend, so that a dredging column can be inserted into an insect trapping hole in the insect trapping plate for dredging, and the convenience of dredging the insect trapping plate can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a crop pest leaf segmentation method and system based on image processing, and the method comprises the steps: obtaining a crop canopy image, carrying out the superpixel segmentation, and constructing a color feature vector; selecting a background node, calculating the minimum accumulated path cost from the superpixel to the background node by using a shortest path algorithm, and obtaining the background saliency of the superpixel in combination with the spatial position relationship; generating an initial segmentation mask according to the statistical characteristics of the background saliency; recognizing a concave area by constructing a blade convex hull, obtaining a concave index according to the depth, width and fitting radius of the concave area, and correcting the initial mask by using the concave index to obtain a complete blade segmentation mask; and carrying out pest and disease damage judgment according to the pixel number ratio of each region. The method effectively solves the problem of incomplete leaf shape caused by confusion of soil background and scabs and insect pests, and improves the accuracy of disease and pest detection.