2340 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.

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

The invention relates to the technical field of 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 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.

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 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 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.

The invention provides an unmanned aerial vehicle biological control method and system based on real-time visual perception and accurate delivery decision. The method comprises the following steps: collecting farmland environment data through multiple sensors, and performing low-illumination enhancement on a visible light image; utilizing an improved target detection network to synchronously identify diseases and insect pests and position a putting point; the two-dimensional image, thermal infrared and three-dimensional point cloud features are fused to realize accurate positioning of the target; a visual language model is introduced for cross-modal reasoning, target disambiguation and priority analysis are completed in combination with a task instruction, and a collision-free delivery sequence is generated; and finally, planning an optimal flight path, and realizing precise drug delivery through trajectory optimization and closed-loop control. The intelligent level and the operation precision of pest control in a complex farmland environment are improved.

The invention discloses an early-stage intelligent monitoring system for citrus reiculata Blanco canopy diseases and insect pests based on multispectral imaging, and belongs to the technical field of agricultural information. The system comprises a multispectral imaging module, a three-dimensional point cloud acquisition module, a data fusion module, a time sequence data analysis module, an intelligent identification module and a monitoring result output module. The method comprises the following steps: synchronously acquiring a multispectral image and laser radar point cloud data of a citrus reiculata tree canopy, and generating a point cloud model through spatial registration fusion; continuously recording model data of a plurality of time points, and extracting a time sequence feature vector; identifying disease and pest types and severity by using a deep learning model; and outputting a result to the user terminal. According to the invention, the problem that large-range and high-precision early monitoring of diseases and insect pests of citrus reiculata canopies is difficult to realize in the prior art is solved, early discovery, precise positioning and trend early warning of the diseases and insect pests are realized through air-space-ground integrated data fusion and intelligent analysis, and the intelligent level of orchard management and the disease and insect pest control efficiency are effectively improved.

The invention relates to the technical field of pest and disease damage prevention and control, in particular to a trichosanthes kirilowii maxim planting whole-process pest and disease damage monitoring-prevention and control collaborative management system, which comprises a data acquisition module, a data analysis module, a decision making module, a prevention and control execution module and a storage module, by means of agricultural prevention, physical prevention, biological prevention and chemical prevention, prevention and control measures are executed according to requirements of prevention and control decisions, and prevention and control conditions after the prevention and control measures are executed are fed back to the decision making module and the data acquisition module; and the decision making module receives the prevention and control condition feedback and then compares the association table with the prevention and control strategy again to judge whether to output the prevention and control decision again. According to the method, a'monitoring-decision-execution 'closed loop is constructed, the problems of opportunity dislocation and mutual interference of a comprehensive prevention and control strategy are solved through a prevention and control measure cooperative scheduling mechanism, and meanwhile precise prevention and control are achieved in combination with a dynamic monitoring system.

The invention belongs to the technical field of green prevention and control of agricultural pests, and particularly relates to application of a phytase gene set of orius guttatus in the aspect of RNAi-mediated pest prevention and control. The gene set comprises a gene RP382, a gene RP382L1, a gene RP382L2 and a gene RP382L3, amino acid sequences of proteins coded by the gene RP382, the gene RP382L1, the gene RP382L2 and the gene RP382L3 are sequentially shown as SEQ ID NO: 5-8, and nucleotide sequences of the gene RP382, the gene RP382L1, the gene RP382L2 and the gene RP382L3 are sequentially shown as SEQ ID NO: 1-4. According to the invention, through systematic analysis, four phytase gene family members are identified in the Eriomycorus lividus for the first time, and the phytic acid metabolism capability of pests is destroyed by interfering the expression of the genes, so that the death rate of the pest Eriomycorus lividus is obviously increased to 50%.

According to the high-yield and high-quality cultivation technology for the strong-flavor powder glutinous type areca taros, through scientific management measures such as land selection, seed selection, five times of topdressing, four times of stump ridging, irrigation and disease and pest control, the yield, quality and flavor of the areca taros are remarkably improved in combination with application of microbial fertilizers, organic fertilizers, plant ash, organic miscellaneous soil fertilizers and sheep and cow manure fertilizers.

The invention provides a plant disease and insect pest inhibition type organic fertilizer based on nanomaterial loading and a preparation method thereof, and belongs to the technical field of fertilizers, the plant disease and insect pest inhibition type organic fertilizer comprises: a nanocellulose whisker carrier derived from agricultural wastes; the plant-derived insect-inhibiting component is loaded on the surface of the carrier through an ester bond and a hydrogen bond; the sodium alginate-lignin porous gel network is used for wrapping the carrier; through double bonding of an agricultural waste nano-cellulose carrier and tea saponin-allicin and in combination with a sodium alginate-lignin gradient gel network, the residual rate of active ingredients in 60 days is larger than 40%, the rhizosphere targeted enrichment concentration reaches 3.28 times of that of a non-rhizosphere region, the problems that a traditional organic fertilizer is short in lasting period and poor in targeting property, and a synthetic carrier is polluted are solved, and the application prospect is wide. While the pest control efficiency and the crop yield are improved, environment friendliness and cost effectiveness are both considered, and a solution with innovativeness and practicability is provided for organic agriculture.

The present invention provides the use of 1-methoxyindole or its derivatives in repelling and / or killing pests, belonging to the field of pest control technology. The active ingredient provided by the present invention is 1-methoxyindole (1-MOI), which is found in Metarhizium robertsiense. When applied as a repellent, the concentration in a plane is 0.127 μg / cm 2 1-methoxyindole can repel a variety of ants, termites, and ticks, and the higher the 1-methoxyindole content, the stronger the repellent effect; when used as an insecticide, the concentration in the air is 0.14μg / cm 3 The 1-methoxyindole can kill a variety of ants, termites, ticks, thrips, mosquitoes, as well as whiteflies, brown planthoppers, spider mites and other pests, and the higher the concentration of 1-methoxyindole, the stronger the killing effect.