2371 results about "Pest insect" patented technology

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

The invention 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.

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.

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 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.

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 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.

The invention relates to an insect pest detection processing method, system and device, and belongs to the field of insect pest detection.The method comprises the steps that a crop image in a target area is collected, black spot recognition and malformation recognition are conducted on the crop image, and black spot parameters and malformation parameters are obtained; according to the black spot parameter and the malformation parameter, calculating to obtain a first insect pest parameter, and configuring an insect body detection range and an insect body detection scale; collecting a detection image set, and carrying out insect body recognition to obtain an insect body recognition result; and processing according to the insect body identification result to obtain a second insect pest parameter, and calculating to obtain an insect pest parameter as an insect pest detection processing result in combination with the first insect pest parameter. According to the method, the technical problems of inaccurate detection and low treatment efficiency caused by the fact that pest distribution characteristics are not considered in mangosteen pest detection in the prior art are solved, and the technical effects of analyzing the pest distribution characteristics through crop pest symptoms, optimizing a detection strategy and improving pest detection accuracy and treatment efficiency are achieved.

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, Y2, Y3, Z1, Z2, R1, R2, R3, R4, R5, R6, R7, and R8 independently represent hydrogen, halogen, a cyano group, a nitro group, an alkyl group, etc. The compound has an excellent prevention and treatment effect on pests such as spodoptera frugiperda, armyworm and prodenia litura.

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 X and Z respectively and independently represent hydrogen, alkyl, alkenyl, alkynyl, naphthenic base and the like; y represents chlorine or bromine. The compound has an excellent prevention and treatment effect on pests such as spodoptera frugiperda, armyworm and prodenia litura.

The invention discloses a corn pest detection method based on DAS-YOLOv8, and relates to the technical field of target detection. Comprising the following steps: 1) collecting image data of different types of corn pests; 2) preprocessing the collected corn pest data, and constructing a corn pest data set; 3) constructing a DAS-YOLOv8 network model: constructing a C2fDS module, replacing a C2f module of YOLOv8 with the C2fDS module, replacing part of traditional convolution in the YOLOv8 model with an ADown down-sampling module, adding a SimAM attention mechanism into the YOLOv8 model, and optimizing a loss function based on WIoU; (4) training a DAS-YOLOv8 model by using the preprocessed data set, so as to obtain a trained DAS-YOLOv8 model; and 5) loading the optimal model weight, and inputting a corn pest image to obtain a final detection result. According to the constructed network model, the parameter quantity and the calculation quantity can be effectively reduced, the higher convergence speed and the more accurate regression result are achieved, and an effective technical scheme is provided for corn pest detection.

The invention relates to the technical field of insect condition monitoring, and provides an insect condition monitoring method, system, device and equipment, a product and a storage medium. The method comprises the following steps: acquiring grain surface data in a granary; the grain surface data comprises grain surface RGB image data, grain surface environment data and grain surface coordinate data; inputting the grain surface data into an insect condition monitoring model to obtain an insect condition monitoring result output by the insect condition monitoring model; the insect condition monitoring model is obtained by training on the basis of a large model; the large model comprises a grain surface pest category identification network and a granary pest quantity prediction network, the grain surface pest category identification network is used for identifying the category of grain surface pests, and the granary pest quantity prediction network is used for predicting the quantity of granary pests. On one hand, the requirements of monitoring efficiency, monitoring accuracy and early warning timeliness can be met, and on the other hand, the requirements of monitoring accuracy and whole granary pest quantity estimation in a complex scene can be met.

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; y represents hydrogen, an alkyl group, an alkenyl group, an alkynyl group 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 utility model provides an insect condition monitoring station, and relates to the field of insect condition monitoring. Comprising an insect trapping mechanism, the insect trapping mechanism comprises a supporting frame, an electric net plate and an insect trapping lamp, and the electric net plate and the insect trapping lamp are detachably installed on the supporting frame; the insect trapping mechanism is mounted at the top of the monitoring box; an insect discharging hole is formed in the center of the top of the monitoring box; the interior of the monitoring box is transversely divided into a first area, a second area and a third area which are adjacent in sequence through partition plates. A plurality of first conveying units are arranged between the first area and the second area in a penetrating mode, and a plurality of second conveying units are arranged between the second area and the third area in a penetrating mode. A collecting plate is arranged on the conveying unit; in the initial state, the multiple collecting plates are all located in the second area. The multiple camera devices are located in the first area and the third area respectively and located above the conveying units respectively. The pest collecting device can independently collect pests in different time periods and monitor the pests in real time, so that the monitoring efficiency is improved, and the labor intensity is reduced.

To provide an inhibitor for suppressing expression of an antibacterial peptide in brown planthopper and an application thereof.SOLUTION: A dsNlPCE4 inhibitor designed based on the NlPCE4 gene of the brown planthopper is obtained. The inhibitor, when applied to brown planthoppers inoculated on their body surface with Metarhizium anisopliae, significantly increases the mortality of the brown planthopper. The present invention combines two technical strategies for pest control, biocontrol fungi and RNA interference (RNAi), to provide a new target for brown planthopper control technology based on cooperative mediation of RNAi.SELECTED DRAWING: Figure 5

The invention discloses a deep learning-based poplar pest image recognition method and system, and the method comprises the steps: obtaining poplar pest domain knowledge, carrying out the knowledge distillation enhancement, constructing a poplar pest knowledge graph, determining a poplar variety, constructing a poplar pest ecological deep recognition model, and carrying out the recognition of a poplar pest image. Inputting a to-be-identified poplar pest ecological picture into the poplar pest ecological depth identification model to obtain a first poplar pest ecological feature, processing the multi-band pest picture to obtain a secondary pest feature, performing feature crossing to obtain a second poplar pest ecological feature, determining a spatial-temporal feature, and identifying the poplar pest ecological depth identification model; and inputting the spatial-temporal characteristics, the poplar variety and the second poplar pest ecological characteristics into the poplar pest knowledge graph to obtain a poplar pest prediction result. The method not only can improve the efficiency and accuracy of poplar pest image recognition, but also has good interpretability, and can be directly applied to a poplar pest image recognition system.

A scent generator includes a supply chamber configured for receiving a volume of an olfactory stimulant, the supply chamber including an electrically conductive foam configured to volatilize a portion of the olfactory stimulant; and a power supply configured to provide an electrical signal to the electrically conductive foam and cause the olfactory stimulant to volatilize and be ejected from the supply chamber. A pest trap using the scent generator and a computer program product for controlling the scent generator are disclosed.

The invention belongs to the technical field of agricultural compositions, and particularly relates to a composition for preventing and treating papaya spider mites. The active ingredients of the composition are hexythiazox and quercetin, the mass ratio of hexythiazox to quercetin is 1: (0.5-20), preferably 1: (7.5-12.5), and an indoor toxicity determination test shows that the composition has a remarkable synergistic effect on tetranychus urticae within the ratio range. The invention also provides a water emulsion, a suspending agent, missible oil and other pesticide preparations containing the composition. Field efficacy tests prove that the composition provided by the invention has a good control effect on tetranychus urticae on papaya, is long in lasting period, and can effectively slow down the development of pest drug resistance. The invention provides a novel scheme with high efficiency and low dosage for controlling the carica papaya spider mites.

The invention discloses a pesticide composition containing a pyrazolecarboxamide pesticide and application of the pesticide composition. The pesticide composition comprises an active component A which is the pyrazolecarboxamide pesticide and a component B which is at least one other pesticide defined in the specification, specifically, the invention relates to a pesticide composition for preventing and treating common lepidoptera, coleoptera, diptera, thysanoptera and homoptera pests of plants.

The invention provides an m-diamide compound as well as a preparation method and application thereof. The m-diamide compound is a compound represented by a formula I, or a stereoisomer, a tautomer, an isotope derivative and a pesticide acceptable salt thereof. The m-diamide compound disclosed by the invention has high insecticidal activity, still has a good insecticidal effect under a low dosage, and is more beneficial to environmental protection; moreover, the insecticidal composition has excellent persistence and quick-acting property on killing pests, and does not easily generate cross resistance with existing m-diamide compounds; the plant safety is realized.

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

The invention relates to the technical field of granary monitoring, and discloses a granary storage environment adaptive supervision system and a control method.The system comprises an environment monitoring module, an intelligent analysis module, a supervision execution module and a cloud learning platform. Comprising temperature, humidity, carbon dioxide concentration and pest activity data, data preprocessing and feature extraction processing are carried out, accuracy and feature effectiveness of environment data are guaranteed, meanwhile, a granary environment original data set is generated in time through real-time transmission and preliminary verification, monitoring comprehensiveness and reliability are guaranteed, and a granary environment monitoring system is established. A high-quality data basis is provided for environment state evaluation; by comparing the granary environment characteristic data with the preset environment safety threshold value, the comprehensive environment state index is generated, and the safety state, the early warning state and the dangerous state are divided, so that the system can intelligently identify the environment abnormity level.

A system is provided.SOLUTION: A system including means for acquiring image data of a plant or soil using a camera mounted on a smart device, means for communicating with a server including artificial intelligence for analyzing the acquired image data and receiving an analysis result, means for feeding back a growth state of the plant or presence or absence of a pest to a user based on the received analysis result, and software means for providing a growth prediction of the plant and an optimal coping method.SELECTED DRAWING: Figure 1

The invention relates to the technical field of image recognition, and discloses a traditional Chinese medicine storage pest detection method and device, terminal equipment and a storage medium, and the method comprises the steps: obtaining a monitoring image in a traditional Chinese medicine warehouse; the monitoring image is input into a pre-trained pest detection model, the monitoring image is identified, a backbone network of the pest detection model obtains image features in the monitoring image, and the image features are input into a neck network; the neck network performs classification processing on the image features to obtain intermediate output features, and inputs the intermediate output features into a detection network; and the detection network performs target detection according to the intermediate output features to obtain a pest detection result, and outputs the pest detection result. Through the pest detection model, pest detection operation of traditional Chinese medicinal material storage is realized, and the pest detection accuracy under the background of traditional Chinese medicinal materials is improved.

Compounds of the formula (I) wherein Q is as defined in claim 1. Furthermore, the present invention relates to agrochemical compositions which comprise compounds of formula (I), to preparation of these compositions, and to the use of the compounds or compositions in agriculture or horticulture for combating, preventing or controlling animal pests, including arthropods and in particular insects, molluscs, nematodes or representatives of the order Acarina.