3969 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.

An autonomous agricultural drone system for the precise spraying of pesticides, consisting of: an advanced image recognition camera integrated with an artificial intelligence (AI) module specifically trained on pest and plant health datasets, with the camera configured to detect specific pests, identify pest hotspots, and differentiate between healthy and stressed plants; a real-time decision processor that is operationally connected to the image recognition camera, the processor being configured to: Data received from the image recognition camera is interpreted to assess pest density and plant health. adjusts the pesticide output and spraying patterns of the drone based on real-time field conditions, determines the severity of pest infestation in order to avoid excessive use of pesticides and to apply pesticides precisely and only where they are needed; a multitude of intelligent spray nozzles equipped with sensors and actuators, each nozzle configured to dynamically adjust its spray rate based on pest density and plant species, as determined by the real-time decision processor, thereby minimizing chemical use and environmental impact; characterized by the fact that The drone system includes a multi-liquid injection nozzle system that allows the drone to carry and selectively apply multiple solutions, including pesticides and nutrients, in a single flight, with the system configured to switch between solutions in response to real-time data from the image recognition camera and decision processor.

The invention provides a deep learning-based farmland disease and pest monitoring and prevention method, system and device, and the method comprises the steps: carrying out the data collection of a target farmland through a plurality of sensors, obtaining the sensor data corresponding to the target farmland, carrying out the multi-level data preprocessing, obtaining the structured sensor data of the target farmland, and carrying out the multi-level data preprocessing of the structured sensor data; and performing data analysis on the crop growth image to obtain growth parameters of crops, performing pest recognition, positioning and counting on the pest situation information image through a deep learning algorithm to obtain a pest population dynamic state, constructing a pest occurrence prediction model to obtain a pest occurrence probability, and generating early warning information of a pest control opportunity. And finally obtaining a farming management decision scheme of the target farmland. According to the method, environmental factors, crop growth conditions and insect pest information are comprehensively considered, the occurrence probability of target farmland diseases and insect pests is predicted, corresponding prevention and control measures are provided, and reference is provided for farmers and managers.

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.

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

The present invention relates to a solution for assessing alterations in biological tissue in a non-invasive manner based on computer-implemented analysis of hyper and / or multispectral images of the biological tissue of interest. Alterations can be related to stresses, aging, pests, diseases, changes related to concentration of an active ingredient, and / or resistances against active ingredient, or the like as well as combination thereof. In a particular embodiment such alterations can be assigned to an alteration type and / or the degree of alteration can be quantified over time.

The invention relates to the technical field of leaf bee prevention and control, and particularly discloses a leaf bee pest adult prevention and control method, which comprises the steps of S1, data collection, S2, climate prediction model establishment, S3, precise monitoring, S4, precise prevention and control, S5, dynamic adjustment and S6, monitoring optimization. By using the unmanned aerial vehicle and the remote sensing technology, a gathering area of the adult leafbees can be accurately identified, a high-risk area can be intensively prevented and treated, blind large-area pesticide spraying is avoided, the prevention and treatment effect is remarkably improved, the climate prediction model can predict the activity peak period of the adult leafbees in advance, farmers are helped to take prevention and treatment measures at the best opportunity, and the prevention and treatment efficiency is improved. Through real-time analysis of a climate prediction model and remote sensing data, a farmer can formulate a scientific prevention and control strategy according to the data, dependence on experience and subjective judgment is reduced, the decision accuracy is improved, and according to feedback of real-time monitoring data and the prediction model, changes of pests are coped with in time, so that the prevention and control effect is improved. The continuous optimization of the control effect is ensured.

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 discloses a barn pest monitoring and early warning method, system and device based on big data and a medium, particularly relates to the technical field of pest monitoring, and is used for solving the problem that the distribution trend of newly appearing pests in a barn cannot be accurately monitored in the prior art. The method comprises the following steps: dividing a granary into a plurality of monitoring areas, arranging an independent spectrum sensor network, carrying out real-time monitoring based on a spectrum analysis technology, carrying out contrastive analysis with a granary pest library, rapidly identifying new pests, and after the new pests are monitored, generating a distribution trend chart in real time through a spectrum imaging technology, according to the method, a trend chart is obtained, whether early warning needs to be sent out is judged according to the trend chart, and the data quality can be mastered more comprehensively by evaluating the spectral distortion hidden danger and randomness of spectral data in a high-risk monitoring area, so that the accuracy evaluation of the distribution trend chart of newly-emerged pests is improved, and the reliability and accuracy of a granary pest monitoring and early warning system are remarkably improved.

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.

The invention belongs to the field of pesticides, and relates to an insecticidal composition. Effective components of the insecticidal composition comprise an active component A and an active component B, the active component A is a compound shown in a formula (I), the active component B is any one of Dampropyridaz, Oxazosulfyl and Flometoquin, and the mass ratio of the active component A to the active component B is (1: 42)-(32: 1). The insecticidal composition has the advantages of synergism, resistance delay and the like, and is used for preventing and treating resistant pests.

The invention relates to the technical field of intelligent prevention and control of agricultural pests, in particular to a monitoring, trapping and killing method for field fruit fly pests, and adopts the technical scheme that the top is fixed through an adjustable hook, the pests are attracted by a food attractant to be in contact with an annular pulse power grid, the shot-down pests enter a pest receiving device through a funnel, and the quantity is counted in real time by an infrared counter; the adjustable hook is provided with an anti-rotation buckle structure, the tail end of the anti-rotation buckle structure is integrated with a spiral anti-bird thorn, the hanging stability is improved, a solar panel is adopted for automatically tracking illumination, the electricity utilization cost is saved, and potential safety hazards are reduced; a food attractant constant-temperature slow-release system is constructed through a PTC constant-temperature heating piece, the optimal volatilization temperature is maintained, the trapping and killing efficiency is improved, real-time analysis and remote transmission of insect situation data are supported through an environment sensor and an edge calculation algorithm, and the problems that a traditional trap is low in efficiency, high in mistaken trapping rate, lagged in data feedback and the like are solved. The method is suitable for precise monitoring and green prevention and control of field fruit flies.

The invention relates to the technical field of migratory fly pest monitoring, prevention and control, in particular to an ad-hoc network type migratory fly pest prevention and control system based on an insect radar and edge computing, and adopts the technical scheme that flight behavior data of migratory fly pests monitored by the insect radar is linked with multispectral lamp trapping equipment through radar lamp control fusion, and a prevention and control strategy is guided to be dynamically adjusted; a master control resistance control lamp controls a plurality of slave resistance control lamps to work cooperatively through an ad-hoc network protocol, the cost is reduced, the lamp trapping coverage area is greatly increased, pest recognition is completed locally by adopting an edge-cloud cooperative computing architecture and an edge computing technology, a lightweight insect target detection model is deployed at a lamp end, second-level migratory fly pest recognition and counting are achieved, and the efficiency is improved. The cloud end regularly pushes the newest model weight to improve the recognition precision, precise monitoring, real-time recognition and three-dimensional interception of migrated pests are achieved, the problems of response lag, accidental injury of natural enemies, high energy consumption and limited coverage range of traditional light trapping and killing equipment are solved, and the prevention and control efficiency of migrated pests is remarkably improved.

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 belongs to the field of pesticides, and relates to a composition containing a biological source pesticide. The composition contains an active component A and an active component B, the component A is selected from a compound I-72, and the component B is selected from biological source insecticides and acaricides; the weight part ratio of the component A to the component B is (1: 99)-(99: 1); wherein the structural formula of the compound I-72 is shown in the specification, and the composition disclosed by the invention has an obvious synergistic effect and can be used for preventing and treating various pests and pest mites. # imgabs0 #

The invention relates to a preparation method and an application of a substituted benzimino thiazolidone derivative. Specifically, the invention relates to a compound as shown in a formula (I), or an isotope labeled compound thereof, or an optical isomer, a geometric isomer, a tautomer or an isomer mixture thereof, or a pharmaceutically acceptable salt thereof, and application of the compound in preparation of insecticides for pest control. The compound can achieve a better pest control effect at a lower dosage, especially for pests such as tetranychidae, microcosaridae, acaridae, tarsonidae, aleuridae and root-knot nematode, and can be used for controlling pests such as tetranychidae, microcosaridae, gall mite, tarsonidae, aleuridae and the like. # imgabs0 #

The present disclosure includes pest management apparatuses for trapping pests. For example, some embodiments of the pest control apparatuses comprise a sidewall surrounding an interior region, the sidewall including a bottom edge and multiple inwardly-extending tabs, each tab having a bottom surface and at least a portion of each of the tab bottom surfaces being co-planar with each other, the sidewall being releasably couplable to a floor, and a lid coupled to the sidewall. The floor may be releasably coupled to the sidewall, so as to form a base, the base having a bottommost surface, wherein the floor comprises a substrate to which an adhesive layer is permanently attached, the substrate having a bottom surface that comprises the bottommost surface. The lid and the base may cooperate to form a chamber that includes the interior region.

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.