3139 results about "Pest insect" patented technology

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention relates to the technical field of disease and insect pest early warning, and discloses a big data-based seed industry disease and insect pest early warning method and system, and the method comprises the steps: obtaining environment sound data and leaf image data; performing scab extraction according to the leaf image data to obtain scab morphological characteristics; performing feature extraction according to the environment sound data to obtain pest sound features; matching according to the scab morphological characteristics and the pest sound characteristics in combination with a pre-stored pest disease database to obtain a pest type and a disease type; performing matching degree calculation according to the pest type and the disease type to obtain a pest and disease matching degree; and carrying out damage assessment according to the pest disease matching degree and a preset matching degree threshold value to obtain a disease assessment result. The method has the following effect that the accuracy of early warning of plant diseases and insect pests in the seed industry can be improved.

The invention discloses an N-cyclobutyl substituted benzamide isoxazoline compound as well as a preparation method and application thereof. The N-cyclobutyl substituted benzamide isoxazoline compound has broad-spectrum and efficient insecticidal activity at a low dosage, and has a remarkable effect on prevention and treatment of diseases and insect pests in agriculture and forestry and pests in the field of sanitation; moreover, the pesticide composition is safer to non-target organisms, can reduce the damage to plants, non-target organisms and human beings due to overhigh pesticide concentration, and is more beneficial to crop protection and environmental safety.

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.

Disclosed herein is a novel electric insect zapper design incorporating a UV light source and a specialized fruit flies bait tray. This combination effectively attracts and eliminates a high percentage of fruit flies, offering a more efficient solution compared to traditional bug zappers. The bait tray, placed close to the UV light, uses diced fermenting fruits to specifically target fruit flies, leading to rapid and effective control of these pests in household environments.

The invention belongs to the field of molecular biology, and particularly relates to a transgenic soybean event LP207-1 and a detection method thereof. The invention provides a nucleic acid sequence for detecting a transgenic soybean event LP207-1, the nucleic acid sequence is selected from one or more of sequences SEQ ID NO: 1-7 or complementary sequences thereof, and the nucleic acid sequence is derived from a plant, a seed or a cell of the transgenic soybean event LP207-1. A representative sample of a seed of the transgenic soybean event LP207-1 is preserved in the China Center for Type Culture Collection on May 23, 2025, and the preservation number is CCTCC NO: P202516. The transgenic soybean event LP207-1 has good resistance to lepidoptera pests and can tolerate agricultural herbicides containing glyphosate and glufosinate-ammonium, economic loss caused by the lepidoptera pests can be avoided, and the yield is not reduced; and the breeding efficiency is enhanced, and the molecular marker can be used for tracking a breeding population and transgenic insertion fragments in offspring of the breeding population. The detection method provided by the invention can quickly, accurately and stably identify the existence of the plant material derived from the transgenic soybean event LP207-1.

The invention provides an artificial intelligence network model-based early warning and monitoring method for the occurrence amount of agricultural pests, and particularly relates to a time sequence modeling method by combining a variable structure bus module VSB with a bidirectional long short-term memory network BiLSTM, which is used for predicting the occurrence dynamic state of important pests in a field and an orchard with high precision. Comprising the following steps: selecting three monitoring sites in a main crop producing area; and establishing a time sequence data set of the corresponding relationship between the average daily temperature, the rainfall and the effective accumulated temperature and the number of pests in ten days. According to the method, a hybrid neural network prediction model is constructed, the model comprises five function modules, and the model can accurately early warn annual dynamic changes of main crop main pest populations and judge peak values, and helps farmers establish efficient pest prevention and control measures.

The invention belongs to the technical field of pesticides, and particularly relates to a mesoionic pyrimidinium compound, and an N-oxide or salt and application thereof. The compound is shown as a general formula I: # imgabs0 #, wherein X represents a cyano group, a cyano alkyl group, an aryl group or a heterocyclic group; q1 and Q2 independently represent O or S respectively; m1, M2, M3, M4 and M5 respectively and independently represent CR9 or N, and at least two of the M1, M2, M3, M4 and M5 are N; r1, R2, R3, R4, R5, R6, R7, R8 and R9 independently represent hydrogen, nitro, amino, cyano, halogen and the like respectively. The compound has excellent prevention and control effects on pests such as chilo suppressalis, spodoptera frugiperda, armyworm, prodenia litura, peanut aphid and rice planthopper, and is safe to bees.

The present disclosure is directed to controlling pest infestation by inhibiting one or more biological functions in an invertebrate pest. The disclosure discloses methods and compositions for use in controlling pest infestation by feeding one or more different recombinant double stranded RNA molecules to the pest in order to achieve a reduction in pest infestation through suppression of gene expression. The disclosure also discloses methods and compositions for targeted genome editing in the pest in order to achieve a reduction in pest infestation through disruption of protein activity. The disclosure is also directed to methods for making transgenic plants that express the double stranded RNA molecules and targeted genome editing constructs for use in protecting plants from pest infestation.

The invention discloses pest control equipment and a pest control method based on greenhouse vegetable planting, and belongs to the field of agricultural pest control. According to the technical scheme, the pest control equipment based on greenhouse vegetable planting comprises a monitoring and early warning module, a physical control module, a biological control module and an intelligent control module, and the monitoring and early warning module comprises a pest situation forecasting mechanism; real-time monitoring and early warning are achieved through multiple trapping modes and an AI algorithm, physical prevention and control and biological prevention and control means are combined, automatic adjustment is achieved through the intelligent control module, and therefore the problems that in the prior art, monitoring is not timely, the prevention and control means are single, and intelligent control is lacked are solved; the system has the advantages of real-time monitoring and early warning, synergistic effect of multiple prevention and control means, intelligent control and accurate prevention and control.

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.

Formulations of pesticides are disclosed, specifically pesticidal blends of essential oils and other ingredients, that have been found to enhance the activity of the active pesticidal ingredients. The formulations may be, for example, emulsions or dust formulations. For example, a composition for controlling a target pest is disclosed that comprises at least one active agent and a formulation agent, wherein: the active agent is capable of interacting with a receptor in the target pest; the active agent has a first activity against the target pest when applied without the formulation agent and the composition has a second activity against the target pest; and the second activity is greater than the first activity.

The invention discloses a lightweight rice pest detection method based on GAFNet and electronic equipment. A global attention fusion and spatial pyramid pooling module is designed to capture a global context and aggregate multi-scale features. On this basis, a C3-EFSA is provided, feature representation is optimized through depth separable convolution and a lightweight channel attention mechanism, and therefore the distinguishing ability under the complex background is improved. An enhanced Ghost detection head is constructed, and enhanced Ghost convolution (EGConv), an SE module and a SiLU activation function are integrated, so that redundancy is reduced, and a lightweight structure is further improved. And finally, an enhanced loss function FECIoU specially optimized for a complex pest detection scene is provided, the function is based on CIoU, a numerical stable term and a difficult sample weighting mechanism are introduced, and the positioning robustness of the shielded pests is further optimized. The lightweight target detection model can realize detection of different types of rice insect pests, and is suitable for actual deployment.

The invention relates to the field of disease and insect pest monitoring, and discloses a garden plant disease and insect pest intelligent early warning system based on Internet of Things, comprising an environment monitoring module for collecting environment data related to disease and insect pest breeding based on a sensor network; the pest trapping and counting module is used for trapping pests by using an intelligent trapping device of a pest attractant and is provided with a counting sensor and a pest classification and identification unit to obtain the population density of different types of pests; the plant phenotype monitoring module captures plant phenotype changes by deploying high-definition multispectral camera equipment, and automatically diagnoses pest and disease damage types by using an image recognition algorithm; and the disease and pest early warning module analyzes by using a preset early warning model based on the data of the environment monitoring module, the pest trapping statistics module and the plant phenotype monitoring module to obtain disease and pest indexes, compares the disease and pest indexes with a preset threshold value, and immediately sends out early warning information once the disease and pest indexes exceed the preset threshold value.

The invention discloses a method for monitoring and early warning abnormal behaviors in a granary. The method comprises the steps that image information in the granary is extracted in real time through a camera device; the image information is analyzed, and abnormal behaviors in the granary are judged in a one-to-one correspondence mode in combination with multiple preset analysis algorithms; in combination with a multi-level early warning mechanism, the abnormal behaviors are graded to form abnormal behavior levels; different early warning levels are formed according to different abnormal behavior levels, and an early warning is given to management personnel of the granary; wherein the abnormal behaviors comprise grain surface transaction, region invasion, environment change, pests and mildew. Through real-time monitoring and intelligent analysis and in combination with a multi-stage early warning mechanism, instant detection and quick response of various abnormal behaviors in the granary are realized, the monitoring efficiency and safety are remarkably improved, meanwhile, the workload of management personnel is relieved, effective utilization and reasonable allocation of resources are ensured, small problems are prevented from being evolved into large accidents, and the system is suitable for popularization and application. And an important reference is provided for future risk management.

An apparatus comprising an interface and a processor. The interface may be configured to receive sensor data from a plurality of sensors. The processor may be configured to detect an intruder in response to an analysis of the sensor data, activate an AI model in response to detecting the intruder and generate a countermeasure signal in response to a countermeasure selection by the AI model. The AI model may be configured to analyze the sensor data, compare the sensor data of the intruder to a database of pests to perform a classification of the intruder, determine the countermeasure selection in response to the classification of the intruder as a selected pest, monitor the sensor data for an outcome of the countermeasure selection, and generate a text description of the classification of the selected pest, the countermeasure selection and the countermeasure outcome.

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 belongs to the technical field of pesticides, and particularly relates to a mesoionic pyrimidinium compound, and an N-oxide or salt and application thereof. The compound is shown as a general formula I, wherein X1 and X2 respectively and independently represent hydrogen, halogen, cyano, hydroxyl, amino, alkyl or halogenated alkyl; y1, Y2, and Y3 each independently represent hydrogen, halogen, or the like; q1 and Q2 respectively and independently represent CH2, O or S, and at least one of Q1 and Q2 is O; r1 and R2 respectively and independently represent halogen. The compound has an excellent prevention and treatment effect on pests such as spodoptera frugiperda and armyworm.

The utility model discloses a natural enemy releaser, and relates to the technical field of agricultural pest biological control. Comprising a hook (1), a conical rain cover (2), a sleeve (3) and a bowl-shaped container (4), the hook (1) is fixedly connected with the top of an outer cone of the conical rain cover (2); the top end of the sleeve (3) is fixedly connected with the top of the inner cone of the conical rainproof cover (2); and the bottom end of the sleeve (3) is fixedly connected with the center of the inner spherical surface of the bowl-shaped container (4). The natural enemy releaser provided by the utility model can release natural enemy at a shed frame height of 1.8-2.5 m; natural enemies can be released in rainy days; the climbing strips can be erected on branches and tendrils of plants to guide natural enemy insects to climb to the plants.

The invention provides a grain pile surface pest detection model training method, a detection method and equipment, and relates to the technical field of image recognition, and the training method comprises the steps: respectively generating different types of data samples based on grain pile surface image data marked with pest bounding box labels in a plurality of data fields in advance, respectively inputting the two model branches, so that the two model branches sequentially carry out backbone network feature extraction and backdoor adjustment on the data sample to obtain a target feature map and pest detection result data; and determining the causal feature alignment total loss corresponding to the current iteration round according to the target feature maps obtained by the two model branches so as to optimize the two model branches. According to the method, the domain generalization ability of the grain pile surface pest detection model and the adaptability to complex and changeable actual detection scenes can be effectively improved, the application convenience and effectiveness of the grain pile surface pest detection model can be improved, and then the accuracy and reliability of a grain pile surface pest detection result can be improved.

Proposed are recombinant entomopathogenic bacteria transformed using promoter replacement technique, a preparation method, and uses thereof for producing pesticides and controlling pests. Provided are recombinant entomopathogenic bacteria in which the promoter of the GXP synthase gene (gxpS gene) is replaced in the entomopathogenic bacteria with an arabinose inducible promoter using a promoter replacement technique. The recombinant entomopathogenic bacteria prepared in the present disclosure can produce large quantities of GXPs, which acts to suppress immunity of pests, by controlling the expression of the GXP synthetase gene (gxpS gene) through an addition of an inducer. The recombinant entomopathogenic bacteria prepared in the present disclosure can also effectively control pests through a response that suppresses the immunity of pests.

The invention discloses a facility vegetable tomato leaf miner pest comprehensive prevention and control system, and relates to the technical field of facility agriculture pest prevention and control, and the system comprises a space distribution obtaining module which obtains the space distribution data of a pest invasion path in a facility agriculture environment, and determines the protection level demand intensity of different regions; the protection parameter calculation module is used for calculating specification parameters and installation density of insect prevention grids in all areas according to the protection level requirement strength, and if the pest density of the peripheral area exceeds a preset threshold value, the aperture of the insect prevention grids is adjusted to the minimum specification, and a first-layer electric control isolation net protection barrier configuration scheme is obtained; the comprehensive control system for the greenhouse vegetable tomato leaf miner pests can effectively deal with the complexity and variability of pest invasion, and the pest control effect and sustainability of the greenhouse agricultural environment are remarkably improved.

The invention belongs to the technical field of pesticides, and particularly relates to a mesoionic pyrimidinium compound, and an N-oxide or salt and application thereof. The compound is shown as a general formula I, wherein Q1 and Q2 respectively and independently represent O or S; x represents a cyano group, a cyano alkyl group, an aryl group or a heterocyclic group; y1 is halogen; y2 and Y3 each independently represent hydrogen, halogen, alkyl, or the like; and Z1, Z2, R1, R2, R3, R4, R5, R6, R7 and R8 independently represent hydrogen, halogen, cyano, nitro, alkyl and the like. The compound has an excellent prevention and treatment effect on pests such as spodoptera frugiperda, armyworm and prodenia litura.

The invention relates to the technical field of disease and pest monitoring, and discloses an intelligent mechanical device for agricultural disease and pest monitoring, the intelligent mechanical device comprises a trapping box, the top of the trapping box is fixedly connected with a fixing frame, the inner wall of the fixing frame is provided with a louver board, the inner wall of the fixing frame is fixedly connected with a motor, and the motor is fixedly connected with the top of the trapping box. A first reciprocating screw rod is fixedly connected to the output end of the motor, a sliding rod is movably connected to the circumferential surface of the first reciprocating screw rod, a cleaning plate is fixedly connected to the outer surface of the sliding rod, a trapping lamp is installed on the inner wall of a second connecting frame, an electric power device is arranged on the inner wall of the trapping box, and a sieve plate is fixedly connected to the inner wall of the trapping box. After insects enter, the motor drives the cleaning and wiping plate to move in a reciprocating mode to clean the trapping lamp, the situation that light is reduced due to the fact that the insects gather on the trapping lamp is prevented, attraction to the insects is kept, the trapping lamp can continuously and effectively attract the insects, and the trapping efficiency is improved.

The invention discloses a smart tea garden planting management system and method. The smart tea garden planting management system comprises a multi-source sensor network, an edge computing gateway, an AI decision unit and a control execution unit. The soil fertility and pest and disease damage are monitored in real time through a high-precision sensor, and precise recognition of disease spots and pests is achieved in combination with a YOLOv5 algorithm; the AI decision-making unit dynamically generates variable fertilization and graded prevention and control instructions according to the growth stages of the tea trees, and controls the water and fertilizer integrated device and the biological prevention and control equipment to execute operation. The system supports adaptation of multiple tea types, compatibility of new and old facilities and stable operation in an extreme environment, the fertilizer utilization rate is remarkably increased, the pesticide usage amount is reduced, and the system is suitable for intelligent and standardized management of large-scale tea gardens.

Provided in the present application are a pest situation monitoring and identification method and apparatus based on few-shot learning, and a device and a medium. The method comprises: using a pest situation visual data collection apparatus to capture pests, and acquiring pest images by means of an image collection device; performing feature extraction on the pest images to determine new-species pest features, and marking same, so as to obtain a target data set; training a pest situation detection model on the basis of the target data set and a meta-learning algorithm, optimizing initialization parameters of the pest situation detection model, and obtaining a target pest situation detection model; and using the target pest situation detection model to monitor and identify a new-species pest. In the embodiments of the present application, the pest situation visual data collection apparatus is used to induce and capture pests, and when new-species pests are detected, a few-shot learning algorithm is used to perform model training on a few new shots. In the technical solution, meta-learning and a self-attention mechanism are combined to realize effective pest type monitoring and identification, and a high accuracy can be maintained even when there is only a small number of shots.

Bacilli and combinations of Bacilli capable of germinating and growing under low temperature conditions; of associating with plant roots; of enhancing plant health, growth, and yield; and of protecting plants against phytopathogenic pests, such as Botrytis, Fusarium, Phytophthora and Pythium.

The invention provides a garden underground pest monitoring method, electronic equipment and a storage medium, and belongs to the technical field of pest detection. The method comprises the following steps: collecting background noise signals and audio signals of an underground pest activity period; denoising the audio signal based on the background noise signal; outputting an edge recognition result and confidence through a lightweight recognition model deployed at an edge computing node; when the confidence coefficient is greater than or equal to a preset threshold value, determining that the edge recognition result is a pest recognition result; when the confidence coefficient is smaller than a preset threshold value, outputting a cloud recognition result as a pest recognition result through a deep learning model deployed in the cloud; and according to the pest identification result, the soil environment data, the pest type weight and a preset diffusion coefficient, obtaining a risk index corresponding to each pest type to generate early warning information. And a garden underground pest monitoring solution which is more real in data, resistant to environmental interference, more economical and efficient in system deployment and has a prospective early warning capability is achieved.

A device and method for removing insects, specifically small hive beetles, from a beehive without harming the hive or the bees. The device includes a collection tube, a collection chamber, a vacuum, and a power source. The vacuum is fluidly connected to the collection tube such that the vacuum creates suction at the entrance to the collection tube. A user may insert the tip of the collection tube into the individual cells of a beehive to remove any pest insects from the cells of the hive. Further, a user may remove any pests from the corners and surfaces surrounding the hive. The device is configured to create an appropriate amount of suction to remove the pests without damaging the structure of the hive or harming the bees therein.