1502 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.

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 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 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

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.

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 present invention relates to microencapsulated preparations comprising biopesticide, bio-fungicide, and plant growth promoting agents and uses thereof, as well as to methods for controlling pests and for producing said preparations for managing insect pests. In particular, the invention relates to microencapsulated preparations comprising at least one insect-pathogenic (entomopathogenic) fungus selected from Metarhizium spp. and Beauveria bassiana, and at least one pathogenic Bacillus spp. bacterial strain, preparations and particles comprising these, their use on plant material, methods for controlling pests therewith, and processes for their production.

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 biosensor identification and prediction method and system based on deep learning. The method and system are suitable for pest monitoring and intervention in storage environments such as grain piles. According to the method, multi-modal sensing data of temperature and humidity, oxygen, carbon dioxide, VOCs and the like are preprocessed, a continuous scalar field of temperature and humidity gas and the like is reconstructed by adopting a multi-modal fusion Transform, a multi-scale space gradient is calculated, and an environment gradient map with confidence is generated; constructing a potential function and a stress field based on a gradient map, identifying target features such as an attraction source, a rejection source and a migration channel, and combining observation evidences such as trap triggering and VOCs abnormity; and under stress guidance, performing sampling and online updating on an individual activity track by using a diffusion type generation model, calculating a comprehensive risk index and performing graded early warning. According to the method, the closed loop from discrete sensing reading to force field-track-risk-intervention is realized, the accuracy and timeliness of early warning are improved, on-demand and accurate prevention and control are supported, and the false alarm and prevention and control cost is reduced.

The invention discloses an intelligent monitoring method and device based on multi-modal information fusion, and the method comprises the steps: (1) collecting multi-modal data; (2) carrying out self-adaptive preprocessing on the microwave signal; (3) video image self-adaptive preprocessing; (4) multi-domain feature extraction; and (5) carrying out weighted fusion classification output. The method and the system are suitable for monitoring main grain storage pests such as maize weevil and grain beetles in the scenes of national grain depots and civil granaries, can realize real-time identification, positioning and counting of pests from the surface layer of a grain pile to the depth of 0.5 m, and solve the problems of insufficient performance of a single-mode sensor (vision, radar and acoustics) and poor adaptability of a multi-mode fusion environment in the prior art. The method adapts to intelligent transformation of the existing granary, and meets the requirements of high precision, full coverage and strong robustness of modern grain storage.

The invention provides a composition for luring bactrocera cucurbitae and application thereof, and relates to the technical field of green prevention and control of agricultural pests. The composition comprises the following active ingredients in percentage by weight: 10% of muscovone, 0.1% of heptanal and 0.1% of hexene. The main components of the composition all come from natural plant volatile matters, and the composition is easy to degrade in the natural environment, has no residual risk and is friendly to the ecological environment. Meanwhile, the composition has high safety to bees, pollination insects and other non-target organisms, and ecological side effects possibly brought by traditional chemical attractants are effectively avoided. According to the implementation of the method, an efficient and specific tool is provided for field monitoring of bactrocera cucurbitae populations, the occurrence dynamic of the bactrocera cucurbitae can be accurately mastered, the early warning capability is improved, a new technical path and an implementation basis are provided for promoting green comprehensive treatment of the bactrocera cucurbitae, and the method has good popularization and application prospects.

The invention provides a method for monitoring the population density of rice planthoppers by an unmanned aerial vehicle for the field of agricultural pests. In order to solve the problems that rice planthoppers are seriously damaged and efficient monitoring methods are lacked, the method for monitoring the population density of the rice planthoppers by collecting rice multispectral images through an unmanned aerial vehicle is provided and is characterized in that the unmanned aerial vehicle is used for carrying a multispectral camera to collect rice canopy multispectral images, and the population density of the rice planthoppers is investigated; extracting green, red, red edge and near-infrared reflectivity and texture indexes of the rice canopy; utilizing a continuous projection algorithm to screen reflectivity and texture indexes sensitive to the rice planthopper population density, and utilizing the screened indexes to establish a rice planthopper population density monitoring BP neural network and a random forest model; performing unmanned aerial vehicle multispectral image acquisition on rice in a to-be-detected area, extracting spectral reflectivity and texture indexes, and inputting the spectral reflectivity and the texture indexes into the model to monitor the rice planthopper population density. According to the method, the unmanned aerial vehicle is used for collecting the multispectral image simply and quickly, the monitoring precision is high, and a new method is provided for monitoring large-area rice planthoppers.

The invention discloses a crop insect pest detection method and system based on improved ConvNeXt, and relates to the technical field of crop insect pest detection, and the method comprises the steps: constructing an EDA-ConvNeXt model, training the EDA-ConvNeXt model to obtain a crop insect pest detection model, according to the method, the detection precision is improved through the EDA-ConvNeXt model, the multi-scale features are captured by the backbone network, the boundary information is enhanced by the edge enhancement branch, the dynamic feature extraction branch adapts to the morphological change, and the key features are highlighted by the multi-branch fusion. The classification layer ensures the recognition reliability, and finally higher accuracy is achieved in a complex field scene.

The utility model discloses a trapping device for preventing insects from laying eggs, which comprises a wool felt (1) compressed into a corrugated shape, a group of bristles (2) sewed at two ends of the wool felt (1) respectively, a support rod (3) and a plastic film (4). The egg laying trapping device induces codling moths to lay eggs in the device, concentratedly destroys eggs and trapped adults, has the advantages of being simple in structure and capable of being recycled, can reduce use of chemical pesticides, guarantees food safety and quality, and reduces prevention and control cost while controlling the number of pests.

The invention discloses a termite invasion prediction method and system based on deep learning, and belongs to the technical field of pest monitoring and artificial intelligence crossing. According to the method, termite activity data at multiple monitoring points are collected, a multi-dimensional feature system including environment, time and historical activity dimensions is constructed, key features are screened by using a random forest algorithm, and the key features are input into a Ridge regression model subjected to cross validation optimization for training, so that high-precision prediction of the number of termites is realized. And constructing a dynamic prevention and control decision model in combination with a prediction result, and optimizing attractant purchase, monitoring point maintenance and emergency killing strategies by taking minimization of accumulated prevention and control cost as a target. According to the method, the problems of hysteresis and low prediction precision of a traditional monitoring method are effectively solved, a driving mechanism behind termite activity fluctuation can be accurately speculated, the short-term change trend of the number of termites can be predicted in advance, active early warning and accurate prevention and control of termite invasion are realized, and the method has remarkable economical efficiency and practicability.

The utility model provides a forestry pest trapping device, and particularly relates to the technical field of pest trapping, the forestry pest trapping device comprises a shell, the top of the shell is fixedly connected with a hook, the outer side of the hook is fixedly connected with a solar panel, according to the forestry pest trapping device, the outer side of a collecting box is wrapped with a pest sticking plate, and the pest sticking plate is limited through a first limiting frame and a second limiting frame; the pest sticking plate can collect attracted pests, an insecticide is put into the collecting disc, when the pests enter the collecting box, the pests can be guided through the guide sleeve, the effect of preventing the pests from flying out can be achieved, the pests fall into the collecting disc, the pests can be killed through the insecticide, and during installation and replacement, the pin rod is pulled, and the pest sticking plate can be conveniently replaced. The spring is compressed through the convex ring, the collecting disc is slidably inserted into the mounting groove through the mounting block, then the pin rod is loosened, the spring rebounds, and the pin rod is inserted into the second pin hole, so that the collecting disc is mounted, and subsequent cleaning is facilitated.

The invention discloses a laser robot system for removing harmful insects in a large area. The system integrates an autonomous mobile platform, a multi-sensor sensing system, an intelligent decision and control unit and an accurate laser removing device. The system adopts a layered sensing architecture, and comprises a millimeter wave radar used for global human and animal safety monitoring, a two-dimensional laser radar used for planar pest searching and coarse positioning, and a precise tracking radar used for precise positioning and tracking of pests. Through a multi-sensor fusion technology, efficient target identification and accurate removal are realized. The method has the beneficial effects that real global automatic operation is realized, a multi-level high-reliability safety protection system is constructed, the searching and clearing efficiency is improved, and the method has high intelligence and environmental adaptability. The system can be widely applied to harmful insect removal operation in large-area areas such as farmlands, orchards and warehouses.

The utility model discloses a grain situation insect gas pipeline air tightness automatic diagnosis system, which comprises an in-bin gas path system and an insect gas detection system, the in-bin gas path system comprises a plurality of gas path channels, and each gas path channel comprises an in-bin gas collecting pipe, an in-bin pipe outlet device, a control ball valve and a gas pipe connector, the in-bin gas collecting pipe, the in-bin pipe outlet device, the control ball valve and the gas pipe connector are connected in sequence; the insect gas detection system comprises an insect detection channel and a gas detection channel, the in-bin gas path system is connected with the insect detection channel and the gas detection channel through a first T-shaped tee joint, and the insect detection channel comprises an insect detection ball valve, an insect detection counting module, an insect collection bottle and a large air pump; the gas measuring channel comprises a gas measuring solenoid valve, a first filter, a replacement solenoid valve, a second filter, a one-way throttle valve, a carbon dioxide sensor, an oxygen sensor, a phosphine sensor, a diagnosis solenoid valve and a small gas pump. According to the utility model, the functions of real-time detection of gas in the granary, pest detection, counting, statistics and early warning, gas path pressure detection, gas tightness diagnosis and the like can be realized.

Provided herein is a composition comprising a Pickering emulsion comprising a plurality of core-shell particles encapsulating an entomopathogenic organism. Furthermore, a method for controlling a pest on or within a plant or at the area under cultivation is provided.

A washing method to eradicate a pest on a textile article, in a washing machine, characterized in that said washing method comprises a prewash cycle, a main wash cycle and at least three rinses cycles, and a drain step between each cycle.

This invention belongs to the fields of locust biotechnology and gene editing technology, specifically relating to a method for preparing locusts. Crz Methods using gene mutants and their application in locust control. This invention discovers that inhibiting gene mutants in locusts... Crz Gene expression can be used to control locusts. Crz The gene sequence is shown in SEQ ID NO:3. This invention successfully established a homozygous Crz mutant strain of the migratory locust using gene editing technology. It was found that the albino migratory locusts obtained after Crz deletion were more easily preyed upon by natural enemies than the wild-type locusts, exhibited significantly lower levels of the aggregation pheromone 4VA, and showed weaker adaptability to low temperatures compared to the wild type. This provides a new strategy for the biological control of migratory locusts and can also serve as a reference for the biological control of other pests.

The invention relates to an installation (1) for disinsection for the eradication of insect pests present in cereals, which comprises a chamber (4) holding cereals, a device for putting under vacuum the chamber (4) containing cereals, an agitation device moving the cereals in the chamber (4), a device for injecting (5) into the chamber (4) a gaseous fluid harmful to insect pests, said injection of the harmful gaseous fluid taking place after the vacuum has been achieved, a device (15) for extracting and neutralizing the gaseous fluid present in the chamber (4) and a device for separating the eradicated insect pests and the cereals after treatment with the gaseous fluid.The invention also relates to a method for disinfesting cereals comprising steps for eradicating insect pests present in cereals using a chamber (4), a vacuum in this chamber (4), and a gaseous fluid harmful to these insect pests. Figure for the abstract: Figure 1.

Smart Pest Trapping Apparatus for crop Protection and insect management

The invention discloses a transgenic corn insect-resistant effect evaluation method, which comprises the following steps: constructing a standardized planting system, adopting a special powder-flocculent feed to realize unified inoculation of multiple insects, standardizing survey indexes and calculation logic, and finishing data processing and insect-resistant grade judgment in combination with a Python automatic analysis system. According to the method, a complete evaluation system of standardized planting, unified insect inoculation, unified investigation and automatic analysis is formed, the insect inoculation efficiency of the method is improved by 17 times compared with that of a traditional method, the data processing efficiency of the method is improved by 1620 times compared with that of an Excel + SPSS combination, standardized technical support is provided for safety evaluation, variety certification and industrial popularization of transgenic maize, and the method has important practical application value.

The utility model relates to the technical field of agricultural planting, and discloses a polygonatum sibiricum planting land disease and pest prevention device which comprises four first telescopic rods, adjusting knobs are arranged in the middles of one sides of the four first telescopic rods, a protection mechanism is arranged above the first telescopic rods, a pest killing mechanism is arranged below the protection mechanism, and the pest killing mechanism is arranged above the first telescopic rods. The protection mechanism comprises a fixing disc, the middle of the lower surface of the fixing disc is fixedly connected to the top of the first telescopic rod, a threaded head is fixedly connected to the middle of the upper surface of the fixing disc, and an insect-proof net is arranged above the threaded head. The top of the first telescopic rod is connected with the fixing disc, the bottom of the threaded head is perpendicularly connected to the middle of the upper surface of the fixing disc, the threaded head can penetrate through the interior of the through hole, the first telescopic rod is covered with the insect-proof net, the nut can be screwed outside the threaded head, the insect-proof net is limited, and pests are blocked; therefore, the effect of preventing the pesticide from damaging the rhizoma polygonati can be achieved.

The invention belongs to the field of pest repellents, and particularly relates to application of n-octadecane in preparation of a pest repellent. According to the invention, n-octadecane (n-octadecane, with the CAS number of 593-45-3) is screened and identified from chemical signal substances on the body surface of the spider brachyta, and the n-octadecane has remarkable behavior regulation and control activity. A behavioral experiment proves that the pest repellent taking n-octadecane as a unique effective repelling component can remarkably induce the repelling behavior of cockroach pests such as cockroaches and periplaneta americana. The invention provides a novel, environment-friendly and efficient prevention and control product and a prevention and control method for pest prevention and control.

The present invention is directed to pesticidal mixtures comprising one or more cervinomycins and at least one additional compound listed below in paragraph [0011]. Further, the present invention is directed to methods of controlling a pest comprising applying an effective amount of a mixture of the present invention to the pest or an area in need of pest control.

The application provides a forestry pest damage monitoring device, and relates to the field of forestry pest monitoring. The device comprises a base and an ultraviolet insecticidal lamp. The upper surface of the base is fixedly connected with a monitoring table. The upper surface of the monitoring table is fixedly connected with support columns in a symmetrical manner. The upper surface of the support columns is fixedly connected with mounting plates. The upper surface of the mounting plates is fixedly connected with support plates in a symmetrical manner. The upper surface of the support plates is fixedly installed with a top plate. The ultraviolet insecticidal lamp is fixedly installed on the lower surface of the top plate. The device can attract pests by the ultraviolet insecticidal lamp, and the pests can be knocked off and dropped into the through groove through the guide cylinder and then dropped onto the middle of the upper surface of the monitoring table. When the pests are accumulated on the surface of the monitoring table, the accumulated pests can be swept into the collecting groove by starting the cleaning assembly, and then dropped into the collecting assembly for collection. The device can prevent a large number of pests from being accumulated on the monitoring table and overlapping together, and the long-term non-treatment can affect the monitoring condition of the infrared sensor and the monitoring effect.