2586 results about "Pest control" patented technology

The invention provides a forestry pest and disease prediction method and system based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a dynamic monitoring data set of a target forest region, covering an environment and vegetation physiological parameter sequence collected by a plurality of monitoring nodes, and a space coordinate region corresponding to each node; extracting features of the dynamic monitoring data set to obtain a forestry environment feature set containing vegetation growth, environment fluctuation and potential identification features of plant diseases and insect pests, and analyzing the forestry environment feature set by using a pre-trained plant disease and insect pest prediction model to generate a prediction parameter set representing the occurrence probability and influence range grade of the plant diseases and insect pests; executing dynamic prediction matching, determining a disease and insect pest distribution thermodynamic map and generating a control priority strategy, and finally fusing the disease and insect pest distribution thermodynamic map and the disease and insect pest control priority strategy to generate a forest region control optimization instruction set, and feeding back the instruction set to a forestry monitoring platform to trigger resource scheduling so as to realize efficient disease and insect pest control.

The invention provides a division method and system for a forest pest control area, and relates to the technical field of data processing, and the method comprises the steps: building a forest pest control database through collecting pest data and environment data of a control area; and training a pest risk prediction model based on historical data to generate a risk distribution map. And then, in combination with a GIS technology and a deep learning image segmentation algorithm, intelligent division is performed on the target area, and it is ensured that the unmanned aerial vehicle efficiently covers the prevention and control unit. According to the method, the optimal spraying route can be planned by adopting a path optimization algorithm based on the flight capability of the unmanned aerial vehicle, the pesticide carrying capacity and the environmental conditions, spraying parameters are dynamically adjusted by monitoring the wind speed, obstacle information and the like in real time, precise pesticide application is ensured, the pest control efficiency can be improved, repeated spraying and spraying blind areas are reduced, the pesticide use amount is reduced, and the pesticide application cost is reduced. Intelligent and precise forest pest control is achieved, and the method is suitable for large-scale unmanned aerial vehicle autonomous operation scenes.

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.

The invention discloses a crop pest monitoring method and system based on artificial intelligence, and the method comprises the steps: constructing a crop pest recognition model, and recognizing the types and number of pests; the method comprises the following steps: acquiring crop infrared thermal imaging image data of multi-view fusion, and identifying insect pest types and quantity of unshielded areas in a crop monitoring area; identifying a thermal anomaly region, and determining the number of insect pests in a shielded region in the crop monitoring region; constructing an insect pest type identification model, and determining the insect pest type of the shielded area in the crop monitoring area; calibrating the number and type data of insect pests in the crop monitoring area; constructing an insect pest prediction model, and judging a future insect pest diffusion trend; and evaluating the effectiveness of the pest control strategy, and adjusting the pest control strategy and implementing the advance of the pest control strategy. According to the invention, comprehensive monitoring, accurate identification and scientific prevention and control of insect pests are realized, the problems of monitoring blind areas and prevention and control lag are effectively solved, and the efficiency and reliability of insect pest prevention and control are significantly improved.

The invention provides a method for increasing the growth rate of a citrus breeding material, and relates to the field of agricultural science. The method for improving the growth rate of the citrus breeding material comprises the following steps: selecting high-quality seedlings, and selecting citrus seedlings with excellent hereditary characteristics as the breeding material; the soil environment is optimized, the soil structure is improved, and the soil organic matter content is increased; nutrition management: formulating a scientific fertilization scheme including the proportion and the application amount of nitrogen, phosphorus, potassium and trace elements; according to the application of the growth regulator, the plant growth regulator is used for promoting cell division and elongation; disease and pest control: combining biological control and chemical control to control the occurrence of diseases and pests, and regularly monitoring the disease and pest conditions; environment regulation and control: temperature, humidity and illumination conditions are controlled, and a suitable growth environment is created. A scientific fertilization scheme is formulated according to the soil nutrient condition and the nutritional requirements of the citrus, and sufficient nutrients are ensured to be obtained in each growth stage, so that the growth rate is remarkably increased.

The invention relates to the technical field of trapping equipment control, and discloses a trapping equipment cooperative control system based on multi-sensor fusion, which comprises a multi-source data acquisition module, a target identification module, a risk assessment module, a cooperative relationship modeling module, a path optimization module, a decision fusion module and an execution control module. The multi-source data acquisition module generates an environment perception map containing temperature, humidity and illumination; the target recognition module analyzes the signals and generates data containing the target size and the motion category in a classified mode; the risk assessment module divides risk areas and inverts threats to generate a risk distribution map; the collaborative relation modeling module extracts collaborative parameters such as communication delay; the path optimization module dynamically plans a path; the decision fusion module performs weighted fusion on the data generation indexes; and the execution control module matches actions and adjusts parameters. The system realizes multi-dimensional perception, accurate identification and cooperative control, improves the trapping efficiency, and is suitable for pest control and other scenes.

The invention belongs to the technical field of agricultural supervision, and particularly relates to an intelligent agricultural planning decision-making system based on the Internet of Things, which comprises a planting plan decision-making unit, a resource allocation decision-making unit, an irrigation and fertilization monitoring unit, an agricultural planting management and analysis unit and an agricultural management and control terminal, planting plan decision-making information is generated through the planting plan decision-making unit to improve planting benefits, and monitoring analysis of the resource distribution decision-making unit and the pest and disease damage monitoring decision-making unit is beneficial for achieving precise irrigation and fertilization and timely preventing and treating pest and disease damage. The irrigation and fertilization monitoring unit analyzes the nutrient water content suitability abnormal performance of the corresponding area in the detection period, enhances subsequent irrigation and fertilization supervision when an irrigation and fertilization alarm signal is generated, and comprehensively evaluates the planting management performance of the corresponding area when an irrigation and fertilization qualification signal is generated; and subsequent planting supervision is enhanced when the planting management alarm signal is generated, so that the growth of crops in corresponding areas is ensured, and the intelligent level is high.

The invention relates to the technical field of peach tree disease and insect pest control, in particular to a peach tree disease and insect pest control method and system.The method comprises the following steps that through peach orchard field investigation and survey, the number of branches is recorded, branch distribution and diseased leaf positions are detected, spot shapes and insect population cardinal numbers are shot, worm egg distribution and tree age information are summarized, and annual rings are checked; and measuring daily variation, soil nutrients and air temperature, and storing into a field table. According to the method, current state parameters of the peach trees are formed by recording key parameters such as the number and distribution of branches of the peach trees, diseased leaf positions, spot shapes, cardinal number of insects, tree age, moisture content, soil nutrients and air temperature, and a basis for dynamic monitoring of diseases and insect pests is established. On the basis of current situation parameter analysis, branch surface color spots, diseased leaf corrosion degree and fruit damage are compared, insect pest types are divided, parasitic characteristics are counted, abnormal data are further decomposed, and deep optimization of disease and insect pest classification and feature recognition is achieved.

The invention discloses a traditional Chinese medicinal material standard base pest and disease damage intelligent monitoring and precise control method and control system, and relates to the technical field of agricultural intelligence. The Internet of Things technology, the block chain and the artificial intelligence algorithm are integrated to improve monitoring accuracy and prevention timeliness, environment and plant states are acquired through the sensor and image data, safe storage of data is ensured by using the block chain, early recognition of diseases and pests is performed by using the convolutional neural network, and the accuracy of disease and pest control is improved. A disease risk index is calculated in combination with environmental parameters, a reinforcement learning algorithm dynamically optimizes a control strategy, control measures are automatically executed through Internet of Things equipment, an execution result is recorded on a block chain, in addition, the strategy is continuously adjusted according to a control effect evaluation result, intelligent and precise disease and pest management is realized, resource consumption is effectively reduced, and the economic benefit is improved. The traditional Chinese medicinal material production safety and efficiency are improved.

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 discloses a pest control management system and method for forest tree breeding, and relates to the technical field of forest tree breeding and seedling raising, and the method comprises the following steps: collecting environment data and soil rhizosphere data by collecting multi-mode images of leaves and trunks of a pinus massoniana young forest and videos that pinus massoniana caterpillars gnaw leaves and cluster diffusion; constructing a disease and insect pest recognition model by using a convolutional neural network, performing definite diagnosis on pine wood nematode disease, pine caterpillars and monochamus alternatus hope, and quantifying the disease and insect pest occurrence area and population density of each sub-region; calculating a pine wood nematode disease control demand index, comparing the pine wood nematode disease control demand index with a threshold value, and triggering a corresponding control strategy; the diffusion coefficient of the pine caterpillars is calculated and compared with a risk threshold value, and a prevention and control scheme is formulated; calculating the propagation coefficient of the monochamus alternatus hope, judging the propagation risk and giving a prevention and control strategy; according to the invention, intelligent pest identification, risk quantification and control strategy closed-loop control are realized, treatment and scientific prevention and control are facilitated, and the pest control efficiency of the pinus massoniana young forest and the forest stand health level are improved.

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 relates to an intelligent monitoring system for prevention and control of diseases and insect pests for bean planting, in particular to the field of intelligent monitoring of prevention and control of agricultural diseases and insect pests, and can realize extremely early precise recognition and early warning of diseases and insect pests of bean crops. The weak lesion spectral features and the continuous deterioration trend of the plant can be sensitively captured in a stage which is difficult to perceive by naked eyes, real diseases and environmental interference are effectively distinguished, the system can automatically locate a suspicious lesion area by using a weak supervised learning mechanism and only needing image-level labeling, the dependence on expensive pixel-level labeling data is greatly reduced, and the accuracy of the system is improved. Finally, a high-confidence early warning signal is output through multi-source information collaborative decision, a scientific basis is provided for accurate and green prevention and control, blind pesticide application is effectively avoided, and crop health and yield are guaranteed.

The invention relates to the technical field of agricultural informatization and intelligent decision making, and discloses a vegetable growth cycle pest control decision making system and method based on a knowledge graph. According to the method, an unstructured agricultural data stream including vegetable varieties, growth stages, environmental parameters, pest and disease records and geographic information is collected from an original database. A data tuple set with a time stamp is generated by performing scanning identification and content extraction on a data stream. Then, clustering is carried out according to the growth stages to which the tuples belong, and stage feature clusters sorted according to the growth cycle are formed; and performing multi-dimensional feature correlation analysis on the stage feature cluster, and establishing a dynamic coupling relationship among the environmental parameters, the disease and pest history and the geographic space features under the growth cycle dimension. And based on the dynamic coupling relationship, generating disease and pest control decision parameters of the vegetables in the specific planting area in each growth cycle stage. According to the invention, accurate matching between the control decision and the crop physiological time sequence and regional characteristics is realized.

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.

To provide a novel pest control agent composition, particularly a bactericidal composition.SOLUTION: A bactericidal or bacteriostatic composition comprising an active ingredient A selected from pyrazole compounds or salts thereof represented by formula (I): [G represents G-1; G1 represents C1-C6 haloalkyl or the like; T represents an oxygen atom or the like; RX represents C1-C6 alkyl or the like; RY represents a hydrogen atom or the like; R1 represents a hydrogen atom or the like; R2 represents a hydrogen atom or the like; R3 represents a hydrogen atom or the like; Ra represents a hydrogen atom or the like; Z represents Z-1 or the like; Z1 represents C1-C6 alkyl or the like; m5 and n5 each represent an integer of 0, 1, 2, 3, 4, or 5; and p represents an integer of 0 or 1] and an active ingredient B selected from known bactericidal or bacteriostatic compounds.SELECTED DRAWING: None

The invention relates to the technical field of forestry intelligent management and control, and discloses a forestry optimization management and control method and system based on artificial intelligence. The method comprises the following steps: collecting multi-source environment monitoring data such as soil humidity, illumination intensity and vegetation growth indexes in a forestry area, aligning a time sequence through a dynamic time warping algorithm, and separating steady-state and transient components; detecting anomalies through an isolated forest algorithm and generating a thermodynamic diagram, and obtaining a potential disease and pest outbreak area through causal reasoning in combination with historical records; constructing a dynamic growth reference curve, and identifying a disturbance sensitive area by using a generative adversarial network and a convolutional neural network; and integrating and generating a management and control priority partition map, optimizing resource allocation through deep reinforcement learning, outputting a task sequence, and driving equipment to execute irrigation, fertilization or pest control operation. According to the method, multiple artificial intelligence algorithms are fused, accurate control over the whole forestry process is achieved, and the method adapts to the dynamically-changing forestry environment.

The invention relates to the technical field of pest control, in particular to a pest outbreak process mining risk prediction method and system based on multi-source factors. The method comprises the following steps: measuring regional meteorological data, regional crop soil fertility degree and regional disease and pest occurrence data corresponding to crop disease and pest monitoring points in real time; the corresponding pesticide spraying usage amount is obtained through the crop disease and insect pest monitoring points, and regional occurrence density accounting and disease and insect pest outbreak mining analysis are carried out on regional disease and insect pest occurrence data based on the pesticide spraying usage amount. Obtaining a corresponding regional pest and disease outbreak characteristic mode under the meteorological condition and a correlation characteristic relation between regional soil fertility and pest and disease outbreak; and performing multi-source factor risk prediction and control decision support analysis on the crop disease and pest monitoring points to generate a disease and pest outbreak area control suggestion scheme. According to the invention, scientific and accurate disease and pest risk early warning can be provided for agricultural production.

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 provides a garlic planting full-process database service system based on agricultural big data, which relates to the technical field of agricultural planting and comprises a planting planning module, a seeding management module, a growth monitoring module, a topdressing irrigation management module, a pest control module and a harvesting decision module. The planting planning module accurately matches varieties and base fertilizer formulas according to soil and yield data; the seeding management and growth monitoring module dynamically optimizes planting measures in combination with real-time data and historical experience; the topdressing irrigation module, the pest control module and the harvesting decision-making module utilize technologies such as image recognition and spectrum detection to realize a precise and scientific management strategy, effectively improve the resource utilization rate, reduce the influence of environmental fluctuation on garlic growth, reduce the use of chemical agents, realize full-process data driving by integrating multi-source data, and improve the management efficiency. Meanwhile, a complete planting file is formed to support strategy iteration, large-scale and efficient development of garlic planting is facilitated, and then the economic benefits of garlic planting are effectively improved.

The invention relates to the technical field of pest control, and discloses a natural enemy insect release device for forest pest control, which comprises an unmanned aerial vehicle body capable of identifying and detecting the state of a tree, a putting mechanism arranged below the unmanned aerial vehicle body and a feeding pipe arranged in the putting mechanism, and a pull rope is arranged at the upper end of the feeding pipe. A fixing shaft is fixedly installed at the upper end of the pull rope, a plurality of guide vanes are fixedly installed on the outer side of the fixing shaft, a fixing cylinder with an opening formed in one end is fixedly installed on the guide vanes, the opening is located in the end, away from the fixing shaft, of the fixing cylinder, a balance weight bead is movably installed in the fixing cylinder, and a winding wire is connected between the balance weight bead and the cylinder wall of the fixing cylinder; the lower end of the feeding pipe is provided with a blocking piece which enables the feeding pipe to be communicated inside and outside when the feeding pipe is put. The natural enemy insect releaser aims at solving the problems that a feeding pipe of an existing natural enemy insect releaser is put on the ground, so that insects cannot quickly reach target trees, and diseases and pests of the target trees cannot be prevented and controlled.

The invention discloses a soil flame sterilization method and device. The sterilization method comprises the steps that the rotary tillage speed and the rotary tillage depth of rotary tillage operation and the temperature and the humidity of operation soil are obtained in real time; performing a layered disinfection strategy on the operation soil based on the rotary tillage depth, and dynamically adjusting combustion parameters of sterilization flame based on the collected temperature, humidity and rotary tillage speed of the operation soil; wherein the layered disinfection strategy is that the combustion mode of sterilization flame is dynamically adjusted according to the rotary tillage depth. According to the method, the rotary tillage speed and the rotary tillage depth are collected in real time, sterilization layers are dynamically divided in combination with the rotary tillage depth, a layered disinfection strategy is carried out on operation soil, the temperature and humidity parameters of the soil are collected in real time, and the combustion temperature, duration and frequency are adjusted in real time through a closed-loop control algorithm; according to pathogen distribution characteristics and soil structure differences of soil at different depths, precise and efficient sterilization and deinsectization are achieved, and meanwhile energy waste and ecological damage are reduced.

The invention relates to the field of rice growth vigor monitoring, and discloses a rice phenological period growth vigor monitoring system and method based on an unmanned aerial vehicle, and the system comprises a rice data collection module which divides a rice field into a plurality of subareas, and carries out the image collection of each subarea through a sensor carried by the unmanned aerial vehicle; the rice image processing module is used for transmitting the collected image to a ground station and storing the collected image, and performing preprocessing operations of cutting, size adjustment and edge cutting on the collected image by using an image processing tool to obtain an image in a standard format; the rice data analysis module analyzes the preprocessed standard image, calculates a crop evaluation coefficient of each partition, and evaluates the growth vigor of the crops according to the crop evaluation coefficients; and the coping strategy module is used for judging whether the rice in the current subarea has the risk of agricultural disasters or not according to the evaluation result, and performing corresponding fertilization, irrigation or pest control according to the types of the agricultural disasters.

The invention relates to the technical field of pepper planting, and discloses a pepper planting method for improving pepper quality, which comprises the following steps: soil and matrix treatment, seed treatment and sowing, field management and regulation, fertilization strategy, pest control and ecological balance, and plant pruning and fruit optimization. According to the pepper planting method, through the synergistic effect of all links, the pepper planting effect and quality are comprehensively improved from soil improvement to seed treatment, from field management to pest and disease control and then to plant pruning and fruit optimization. Compared with a traditional planting method, the quality indexes such as vitamin C content and capsaicin content of the chilies planted through the method are remarkably improved, the growth cycle is shortened, the yield is increased, meanwhile, use of chemical pesticides is reduced, and the method is environmentally friendly, has remarkable economic, social and ecological benefits and is worthy of popularization and application. Powerful support and guarantee are provided for sustainable development of the pepper planting industry.

The invention discloses a forestry new technology intelligent disease and pest prevention and control system and method, and relates to the technical field of disease and pest prevention and control, and the system comprises a data collection module which collects the real-time data of diseases and pests in a forest; the pest and disease damage identification and analysis module is used for analyzing the collected real-time data, carrying out pest and disease damage identification by utilizing an artificial intelligence algorithm and calculating the threat level of the pest and disease damage; the pest and disease early warning module sends out pest and disease early warning signals and provides prevention and control suggestions according to recognition and calculation results; the prevention and control measure recommendation module intelligently recommends prevention and control measures according to the early warning result and the specific disease and pest species; and the execution module is used for performing automatic prevention and control operation through intelligent equipment according to the recommended prevention and control measures. Dynamic monitoring of diseases and insect pests is provided, the recognition module based on deep learning automatically analyzes data, the diseases and insect pests are accurately recognized, threat levels are evaluated, personalized prevention and control measures are recommended, the prevention and control efficiency is effectively improved, and the ecological environment is protected.

The utility model relates to the technical field of angelica sinensis cultivation, and discloses an angelica sinensis seed cultivation pest control device which comprises an insect prevention mechanism, a cultivation box, a fixing mechanism and a dismounting mechanism, and the insect prevention mechanism comprises a box body, four limiting grooves, four air suction plates, four arc-shaped plates, four ventilation grooves, a plurality of guide plates, a bottom plate and a fan. Two filter screens are fixedly arranged on the inner walls of the lower sides of the four ventilation grooves, a sliding groove is formed in the lower side of the end face of the rear side of the box body, an ultraviolet lamp is fixedly arranged in the middle of the inner top face of the sliding groove, and the fixing mechanism comprises a fixing plate, a sliding plate and a circular ring. According to the insect-preventing device, through the arrangement of the insect-preventing mechanism and the dismounting mechanism, insects can be absorbed in four directions, the insects are prevented from being stuck to one side of the insect sticking plate in a concentrated mode, the frequency of replacing the insect sticking plate is reduced, through the arrangement of the dismounting mechanism, bolts do not need to be rotated when the insect sticking plate is taken down, and the insect-preventing device is more convenient to use.

The invention discloses a high-yield cultivation method for enhancing the stress resistance of camellia oleifera. The method comprises the following steps: selecting acidic red soil with the soil layer thickness of not less than 1m, performing deep tillage and soil preparation, and performing field planting on Guilin camellia chekiangoleosa and other excellent varieties; farmyard manure and special compound fertilizer are applied in the planting year; in the second year, applying fertilizers in stages after spring shoots, flowering and fruit picking, and irrigating by micro-spraying belts; from the third year, fertilization is carried out in the spring shoot starting period, before flowering and after fruit picking, and annular furrow fertilization and humidity control irrigation are matched; weeding, pruning and bud picking in spring and autumn every year to improve the stress resistance and stable yield capability of camellia oleifera; according to the method, an excellent camellia oleifera variety with high stress resistance is selected, the root system environment is optimized in combination with acid soil deep ploughing and fertilization, hormone compound fertilizer is applied in stages and is matched with micro-spraying belt irrigation, and accurate regulation and control of water and fertilizer can be achieved; the survival rate, the stress resistance and the yield stability of the camellia oleifera under the adverse conditions of drought, high temperature, diseases and pests and the like can be comprehensively improved.

The invention relates to the technical field of electric power facility protection, and discloses an ultrasonic damage expelling equipment control method applied to electric power facilities. The method comprises the following steps: acquiring environmental parameter data in real time, constructing a multi-dimensional pest feature recognition system and outputting a standardized signal; establishing an ultrasonic parameter database based on a frequency characteristic conversion technology, and generating a control instruction through characteristic matching; constructing a historical pest expelling case information base; establishing a multi-factor weight distribution model, calculating a priority score and generating a task sequence; an optimal control scheme is solved by adopting an improved genetic optimization algorithm, and the targets are to maximize the pest expelling range, minimize the energy consumption and shorten the response time; and outputting a control result through the interaction platform and tracking and feeding back. According to the method, accurate identification, dynamic parameter matching and multi-target optimization control of the pest activities are realized, and the efficiency and adaptability of power facility pest expelling are improved.

The invention relates to the technical field of pest control, in particular to a forest pest prediction system based on data analysis, which comprises a data preprocessing module, a dynamic weight distribution module, a pest feature recognition module, a prediction model training module and a prediction result output module. According to the method, the feature weight is dynamically adjusted through error distribution analysis, the influence of each feature on a pest event is accurately quantified, the adaptability of weight distribution is improved, multi-feature correlation analysis is combined, key features highly related to pest and disease damage are screened, interference of irrelevant factors is reduced, the model input quality is enhanced, and the weight error change trend is dynamically calculated. According to the method, time sequence optimal weight configuration is formed, high-precision prediction of the occurrence trend of diseases and insect pests is realized, the risk distribution trend is quantified and risk areas are divided based on comparison of prediction data and actual occurrence data, a scientific space-time evaluation result is provided for prevention and control of diseases and insect pests, and accurate prevention and control resource configuration is facilitated.