47 results about "Population density" patented technology

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention provides a fumigation control method based on pest and disease damage trend prediction, and the method comprises the steps: carrying out the spectrum analysis of audio data information through employing a fast Fourier transform algorithm, extracting the frequency domain feature vector of the chirp sound of each pest species, and enabling the frequency domain feature vector to comprise a fundamental frequency, harmonic energy distribution, chirp duration and chirp rhythm; according to the extracted chirp frequency domain feature vector, chirps of local main agricultural pests are identified and classified by adopting a support vector machine algorithm, and the types and life cycle information of the pests are judged by matching with a preset pest species chirp feature template library; and performing spatial mapping on the predicted population density distribution data by adopting a Kriging interpolation algorithm to generate a spatial distribution diagram, determining the aggregation degree and diffusion trend of pests in each region of the field, and dividing a plurality of prevention and control regions and buffer regions.

A method for delineating an ecological corridor based on a maximum similarity model determines ecosystem type according to the determined ecological source, and assigns a habitat suitability index; and according to a maximum similarity matrix between different ecosystems, assigns the habitat suitability index to peripheral pixels of the selected ecological source, to obtain a similar value of the habitat suitability index. The habitat suitability index is corrected according to the surface curvature data and population density data, and resistance surface data is calculated according to the corrected value. Based on the resistance surface data, an ecological corridor is delineated comprehensively to find one or more paths corresponding to a minimum cost distance as ecological corridors. The influence of ecological environment on ecological corridor establishment is considered more comprehensively, so that the ecological corridor is delineated more accurately, and applied into practice to maximum extent.

The invention discloses a fishery ecological environment restoration method and system based on a symbiotic competition mechanism, and the method comprises the steps: collecting all environment monitoring parameters of each fishery ecological region, carrying out the statistics of the vegetation coverage area of various types of plants and the biological population density of various types of organisms, obtaining an environment time-varying sequence and a community time-varying sequence, and carrying out the calculation of the environment time-varying sequence and the community time-varying sequence. Constructing a symbiotic competition specification matrix and an ecological environment strong dependency matrix, calculating an ecological diverse degeneration index, dividing an ecological degeneration sign area and an ecological diverse area, calculating a balance proportion of each species and a competition superior and disadvantage imbalance factor of each species in each ecological degeneration sign area, and calculating a symbiotic competition standard matrix; and obtaining ecological restoration key species of each ecological degradation sign area, and performing fishery ecological environment restoration on each ecological degradation sign area. According to the fishery ecological environment restoration method and system based on the symbiotic competition mechanism provided by the embodiment of the invention, the fishery ecological environment restoration efficiency is improved.

A method is provided for determining soil properties for an area of land, from soil spectrum data. In an embodiment, the method includes receiving soil spectrum data records from hyperspectral sensors that represent a mean soil spectrum of a specific geo-location of the area of land and removing interference signals from the spectrum data records to create soil spectral bands. The method also includes predicting a plurality of soil property datasets based on a partial least-square regression and the soil spectral bands and selecting specific soil property datasets from the plurality of soil property datasets to represent soil properties of the specific geo-location, wherein the specific soil property datasets include property data and spectral band data for spectral bands used to determine the property data. The specific soil property datasets may then be used to generate a crop prescription of recommended hybrid seeds or population densities for the specific geo-location.

The invention provides a fish school density distribution similarity measurement-based bait casting decision-making method, which comprises the following steps of: acquiring static pictures, dynamic videos and real-time video streams of a water area containing fishes, and preprocessing to obtain an input data set; an Ifeed model is constructed, and the Ifeed model comprises a multivariate input preprocessing and image foreground target extraction module, a fish school feeding behavior analysis and density distribution estimation module, a target key point extraction and full connection distance calculation module and a fish school gathering trend visualization and bait casting decision support module; inputting the input data set into the Ifeed model, and training the Ifeed model through the input data set; static pictures, dynamic videos or real-time video streams of a water area to be fed are collected and input into the trained Ifeed model, and gathering degree visual data and feeding strategies of fish schools in the water area to be fed are obtained. Feed waste can be reduced, water quality pollution is reduced, cost is reduced, efficiency is improved, and the purpose of accurate feeding is achieved.

This application provides a method and an apparatus for determining click-farming in a live room. The method for determining click-farming in a live room includes: determining user distribution information associated with a target live room, and determining a user distribution ratio based on the user distribution information; obtaining population density distribution information, and determining a population density distribution ratio based on the population density distribution information; and determining whether click-farming exists in the target live room based on the user distribution ratio and the population density distribution ratio. This method enriches means for determining click-farming in the live room, and effectively improves accuracy of determining click-farming.

The invention belongs to the technical field of ecological environment protection, and particularly relates to a method and system for determining a biodiversity key area based on the population number in an area range, and the method comprises the steps: collecting and analyzing key data, such as an environment influence coefficient, a species diversity index and a population density value; and combining the established threat factor quantification standard and the weight distribution system, and adopting a scientific calculation model to obtain the threat coefficient of each sample region. Then, through setting a reasonable threat coefficient threshold value, screening out a key region of biodiversity protection; spatial analysis is carried out by utilizing a GIS technology, distribution and mutual relations of the key areas in geographic space are determined, and priority ranking is further carried out according to threat degrees and protection values. The invention aims to provide accurate, rapid and resource-focused decision support for biodiversity protection.

The invention provides a key species population spatial distribution quantification method and system in a complex ecological system, and the method comprises the steps: carrying out the feature extraction of biological and environmental data collected in a target ecological region, and carrying out the fusion of the extracted multi-modal features, and generating a comprehensive feature vector; based on the target large model, analyzing the comprehensive feature vector, identifying species in the target ecological region, and obtaining species identification information of each species; based on the species identification information and the comprehensive feature vector corresponding to the target ecological region, quantifying a species population space distribution condition, and generating species population quantification information at least comprising a population distribution diagram, a population density diagram and a population dynamic change diagram; and based on the species population quantitative information and the collected original data, generating and displaying a quantitative report. According to the method, the species can be intelligently, efficiently and accurately identified based on the large model, the distribution condition and dynamic change of the species can be visually displayed, a quantitative report is provided, and a powerful basis is provided for ecological protection and management.

The invention discloses a single-point calculation method and system for single-point sampling of microbial population analysis and an inversion method.The method comprises the steps that a sampling device is controlled to sample multiple target intervals, and multiple parallel samples are collected after old water replacement in a pipeline is completed based on preset or instantly-calculated trigger interval time Tc; calculating the variety and population density of single-point microorganisms; and after uncertainty evaluation is carried out on the single points, integrating microorganism species and population density data of all sampling points in the target interval, and generating a microorganism species set and population density of the target interval. Therefore, the model breaks through the boundary effect limitation of the traditional laminar flow theory, so that the triggering interval time Tc is accurately calculated, and sample cross contamination caused by retention of old water particles is thoroughly eliminated. When the parallel sample deviates, the linear characteristic of low-fluctuation data is kept by adopting arithmetic mean; and the high-fluctuation scene is switched to the root-mean-square calculation mode, so that abnormal noise interference is effectively suppressed.

The invention discloses a hyperspectral and morphological feature fused rice planthopper stage and sex identification method, and aims to improve the classification accuracy of the variety, age and sex of rice planthoppers in the initial stage of nymphs. Spectral data of rice planthoppers are obtained through a hyperspectral imaging technology, morphological characteristics are extracted by combining a morphological image processing technology, and the spectral data and morphological characteristic data are combined by adopting a multi-modal data fusion technology. And training and classifying the fused data by using a machine learning algorithm, and accurately identifying the type, age and gender of the rice planthopper. In order to solve the problem of data imbalance, an SMOTE algorithm is adopted to carry out sample oversampling, and class distribution balance is ensured. The severity of insect pests is evaluated in real time through automatic counting and population density generation, and support is provided for prevention and control measures. The screening of characteristic wave bands is expected to provide key technical support for the development of special multispectral cameras. In addition, the model interpretability is improved by adopting SHAP analysis, and the contribution of the features to the classification result is quantified.

The invention relates to the technical field of disease and pest prevention and control, and provides an agricultural disease and pest prevention and control method and system based on deep learning. Firstly, multi-source data of a citrus planting area is collected. Then, after space-time alignment and radiation correction are carried out on the collected original data, edge computing nodes are input, and standardized preprocessing data are generated; thirdly, constructing a dynamic agronomic knowledge graph, and performing association modeling on the data; and then, inputting the standardized image data into a Huanglongbing recognition model based on a convolutional neural network, optimizing a disease recognition result through a deep reinforcement learning algorithm, and generating a pesticide application scheme according to the knowledge graph. Finally, according to population density change data collected after pesticide application, environment capacity parameters in the agronomic knowledge graph are updated, and a closed loop feedback optimization mechanism is formed to improve the prevention and control effect. According to the method, accurate disease monitoring, prevention and control can be realized in a complex agricultural environment, and the decision accuracy is improved.

The invention provides a fishery resource dynamic monitoring method and system based on underwater detection equipment, relates to the field of dynamic monitoring technology analysis, and realizes fine management of water area monitoring by collecting fish school density data in real time, constructing a fish school density matrix and combining grid unit division and geographical hash coding. The gradient value of the adjacent grid units is calculated based on the density matrix, the gradient matrix is generated, the fish school density abrupt change area can be accurately recognized, the abnormal area is marked, then the sonar sampling frequency of the underwater detection equipment is dynamically adjusted, and the monitoring precision of the abnormal area is improved. Compared with the prior art, the method has the advantages that the monitoring precision, the anomaly detection response capability and the resource utilization efficiency are remarkably optimized, and the adaptability and the prediction capability of a monitoring system are improved particularly in the aspects of fish swarm migration trend prediction and grid division dynamic adjustment.

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 relates to the technical field of pest resistance identification, and discloses a rapid identification method for resistance of broad beans to aphids. Insect inoculation and observation are carried out under the manual control condition, the problems of uneven population density and the like in the natural environment are avoided, the identification period is shortened, only several days to several weeks are needed from insect inoculation to obtaining of a reliable identification result, compared with several months in the prior art, the efficiency is greatly improved, and the method is suitable for large-scale popularization and application. Manual control conditions can ensure that environmental conditions of each experiment are consistent, interference of environmental factors on experiment results is reduced, meanwhile, strict experiment operation and a data processing method also improve the accuracy and repeatability of the results, the identification results are more reliable, operation is relatively simple and convenient, complex equipment and a large amount of labor force are not needed, and the method is suitable for large-scale popularization and application. And the identification process can be completed only by carrying out simple insect inoculation and observation recording work in a manually controlled environment.

The invention relates to a corn and peanut intercropping environment monitoring and feedback regulation system in the field of agricultural Internet of Things, and aims to solve the problems of incomplete monitoring, lagging regulation and low resource utilization rate in traditional intercropping planting. The method comprises the following steps: firstly, deploying soil, weather, high-definition cameras and vibration sensors in a distributed manner, and collecting initial data such as soil humidity, pH value, air temperature and humidity, insect population density, corn stalk vibration amplitude and the like; the method comprises the following steps: acquiring numeric data, transmitting the numeric data to a cloud platform through ZigBee and a 4G / 5G network, performing format verification and 3 sigma abnormal value elimination on the numeric data, preprocessing image data, and then identifying insect population density by using a YOLOv5 model to generate preprocessed data; the cloud platform calculates a comprehensive stress index of moisture, nutrition and insect pests in combination with the pre-processed data and precipitation in the future 24 hours, and generates irrigation, fertilization and medication regulation and control decisions; the local control unit drives the equipment to execute, the sensor verifies the effect after regulation and control, secondary regulation and control are carried out if decision triggering judgment is still met, and the method is suitable for corn and peanut intercropping fine planting.

The invention discloses a resource development area ecological vulnerability evaluation method, and belongs to the field of ecological environment research. Based on an SRP model, 14 evaluation indexes including elevation, gradient, topographic relief, annual average rainfall, annual average temperature, dryness, vegetation coverage, landscape restoration index, landscape fragmentation, biological abundance, population density, GDP density, per capita cultivated area and resource development intensity are selected from three dimensions including ecological sensitivity, restoring force and pressure degree; and meanwhile, on the basis of integrating the advantages and disadvantages of the subjective and objective weighting methods, a method based on game theory combination weighting is provided to calculate the index weight, so that the credibility and rationality of an evaluation result are enhanced. According to the method, the spatial relationship between the ecological vulnerability change and the evaluation indexes is analyzed by using the space-time geographic weighted regression model, and the correlation between the evaluation indexes is considered, so that the method has important significance for deeply knowing the complex cause of the ecological vulnerability of the resource development area and formulating differentiated ecological protection measures.

The present invention discloses a method for predicting population demand for emergency shelters based on comprehensive disasters, the method comprising: obtaining population density heat map data, determining permanent population density distribution data P of the area to be predicted, i And the area C corresponding to the data distribution i , get the total population M of the area to be predicted; determine the building height H in area j of the area to be predicted j 、Building area S j And the per capita area index K j ; Construct a population density redistribution coefficient formula model to obtain the resident population density distribution data P of the area to be predicted j ; Get the disaster risk area range D x , matching the disaster-affected area D of each land in the predicted area j ; Output the total number of refugees in emergency shelters in the area to be predicted M d This method proposes a more suitable and scientific prediction method for the differences in refugee population demand caused by different building and land types in the forecast area, providing a scientific basis for the site selection of urban emergency shelters.

The invention discloses a remote sensing ecological index extraction method and system considering human activity intensity and a medium, and belongs to the technical field of remote sensing ecological environment monitoring and evaluation.The method comprises the steps that S1, for the problems that in high and cold arid area large-scale habitat quality evaluation, multi-source data is difficult to obtain, precision is low, and calculation efficiency is insufficient, a high and cold arid area large-scale habitat quality evaluation is established; providing a remote sensing ecological index improvement method based on a Google Earth engine cloud platform and human activity intensity; s2, introducing a salinity index, and providing an improved remote sensing ecological index extraction method; s3, providing a human activity intensity calculation method in combination with four indexes of night light, building land percentage, population density and landscape development index; and S4, coupling the improved remote sensing ecological index, establishing the remote sensing ecological index considering the human activity intensity, and realizing rapid and refined calculation of the remote sensing ecological index. According to the method, powerful data support is provided for ecological management and scientific decision-making of the cold and cold mountainous area.

The invention belongs to the technical field of tomato planting, and particularly relates to a tomato TY virus occurrence early warning method based on temperature and bemisia tabaci population density, which comprises the following steps: establishing a correlativity model of virus gene relative expression quantity Y, temperature x1 and bemisia tabaci population density x2, and measuring the temperature x1 and the bemisia tabaci population density x2 in a tomato planting site; and when the virus gene relative expression quantity Y obtained through the correlation model exceeds an early warning value, triggering a prevention alarm. According to the method, the correlativity model of the temperature and the bemisia tabaci population density and the relative expression quantity of the virus gene of the tomato TY virus is established, the temperature and the bemisia tabaci population density are convenient to measure in a tomato planting site, and the relative expression quantity of the virus gene of the current tomato TY virus can be predicted by means of the correlativity model; a basis is provided for the infection condition of the tomato TY virus, so that growers can prevent, intervene and treat the tomato TY virus in advance, and the tomato planting loss is reduced.

The present application discloses a single-point calculation method, system and inversion method for single-point sampling of microbial population analysis, the method comprises: controlling a sampling device to sample multiple target intervals, based on a preset or instantaneous trigger interval time Tc After replacing the old water in the pipeline, multiple parallel samples are collected; the microbial species and population density at each point are calculated. After uncertainty assessment of the individual points, the microbial species and population density data for all sampling points within the target interval are integrated to generate the microbial species set and population density for that target interval. This model overcomes the boundary effect limitations of traditional laminar flow theory, accurately calculating the trigger interval time Tc and completely eliminating sample cross-contamination caused by retained old water particles. When parallel samples deviate, arithmetic averaging is used to maintain linearity for low-fluctuation data; in high-fluctuation scenarios, the calculation mode switches to root mean square (RMS) mode to effectively suppress abnormal noise interference.

The invention relates to a method, a device, equipment and a medium for monitoring spatial and temporal distribution of tiny insects in facility agriculture, and the method comprises the steps: carrying out the interpolation completion of the population density quantized value of the tiny insects corresponding to each time step at each sampling point position in a facility agriculture monitoring region; determining the population density of the tiny insects corresponding to the position of each sampling point in each monitoring grid in each time step; inputting the population density of the tiny insects corresponding to each sampling point position in each monitoring grid at the current time step into a preset discrete space-time dynamic model so as to determine the population density of the tiny insects corresponding to each sampling point position in each monitoring grid at the next time step; and constructing the population density of the tiny insects corresponding to each sampling point position in each monitoring grid in each time step in the future inspection period into a space-time distribution thermodynamic diagram, and displaying the space-time distribution thermodynamic diagram on a graphical user interface. According to the invention, the accuracy and stability of population density prediction are improved.

The invention discloses a high-yield and high-efficiency breeding method for whitebait. The method sequentially comprises the following steps of breeding water area selection, water area trash fish removal, water area pathogen killing, bait organism breeding, fry putting, ecological management and high-efficiency fishing. Wherein the seedlings are put in an alternate-catching and alternate-putting mode, and the seedlings are put in batches; 10%-20% of fertilized eggs are supplemented every year to achieve resource proliferation. According to the whitebait breeding method, fishing in autumn can make whitebait reach the commodity specification of 4-6 cm. Fishing before reproduction is achieved in March to April in spring, resource waste is avoided, and efficient fishing is achieved; the large water surface is more beneficial to natural proliferation, seedlings are put in batches, annual production is achieved, and high yield is achieved. 10%-20% of fertilized eggs are supplemented every year, and the population density is maintained. Silver carps and bighead carps are matched to regulate water quality and improve comprehensive benefits. And the reasonable yield per mu can reach 15-25 kg.

The application provides a forest small and micro water source group community construction method and system based on an ecological chain, relates to the technical field of ecological engineering, and comprises the following steps: selecting a natural catchment area according to forest topographic features, constructing a water area unit with a layered anti-seepage structure, arranging an anti-seepage layer at the bottom to realize water collection, and arranging a water-permeable transition layer at the edge to form a water-land gradient; dividing the water area unit into multiple ecological function areas according to the water depth gradient and spatial position and introducing matching species; dynamically adjusting the species ratio and spatial layout of adjacent function areas based on the functional correlation of material circulation and energy transmission by monitoring the population density and water quality indexes, forming a self-sustaining ecological chain; arranging a microorganism cultivation layer around the periphery, calculating the inoculation proportion of saprophytic microorganisms, calculating the soil organic matter conversion amount according to the population density and decomposition rate, and establishing the dynamic balance of the water supply amount of the water area, the metabolic activity of microorganisms and the physical and chemical properties of forest soil. The water source ecological system constructed by the application has high stability, can effectively improve the water holding capacity of forest soil and reduce the risk of fire.

The application discloses a regional XCO2 prediction method based on multi-source feature uncertainty modeling, and belongs to the technical field of remote sensing environment monitoring and time series prediction. Firstly, the population density feature is introduced on the basis of common features such as vegetation, weather, terrain and land cover, and a multi-source input data sequence of a unified space-time scale is constructed through spatial resampling and time interpolation. Then, a dynamic evidence multi-scale fusion mechanism is constructed, multi-scale evidence representation, credibility coefficient re-calibration and Dempster-Shafer recursive fusion are performed on the multi-source input, and enhanced multi-scale fusion features are generated. Then, the local dynamic information of the current period and the historical same period mode information based on time coding retrieval are extracted through two-way period modeling, and the two-way information is adaptively weighted and fused, and finally the regional XCO2 prediction result of the future period is output. The application improves the reliability of multi-source feature fusion and the prediction ability of complex non-stationary time series.

The invention relates to the technical field of deciduous forest pest prediction, and discloses a method for predicting pest population density distribution patterns in deciduous forests. The method comprises the following steps: step 1, collecting historical information of pest density at a predicted local point; step 2, predicting a spatial distribution pattern according to the historical information of pest density in the local area; step 3, when data are distributed in two different intervals, xl and xh are respectively used to represent the midpoints of the two intervals, and s=xh-xl represents the distance between the two midpoints; step 4, Ul and Uh are respectively used to represent half of the width of the two intervals; step 5, the midpoints xl and xh of the two intervals are regarded as two states, and Ul and Uh respectively represent the uncertainty of the two states; step 6, calculating K=(Ul+Uh) / s, and the spatial distribution structure of the pests can be obtained according to the value of K.

This specification provides a smart city greening management method, system, storage medium, and device. The method is executed by a management platform and includes: acquiring vegetation data of the monitoring area corresponding to the object platform through a sensor network platform, wherein the vegetation data includes at least one of species information, climate information, soil information, maintenance information, and actual growth parameters; obtaining vegetation anomaly information of the monitoring area based on the vegetation data, wherein the vegetation anomaly information includes the location and amount of vegetation anomalies; obtaining the predicted air quality of the monitoring area based on the vegetation anomaly information, combined with the traffic flow and population density of the monitoring area; and determining the priority of greening treatment in the monitoring area based on the predicted air quality and vegetation anomaly information.

The invention discloses a disease and pest monitoring method based on Internet of Things perception, and aims to solve the problems that under cross-regional migration, a wide-area wind temperature and humidity field and local surface parameters are difficult to physically and consistently fuse, risk hot area delineation lags behind, multi-source alignment is difficult and uncontrollable in uncertainty, and identification and trapping quantification are inconsistent. According to the method, a farmland space-time diagram is constructed, alignment interpolation is carried out, a Fourier neural operator is adopted to predict an arrival probability, a physical constraint operator is refined, quality conservation and nonnegativity are guaranteed through micro-projection, uncertainty is quantified through disturbance reasoning, and a risk hot area and early warning are generated through a space-time diagram model. And open vocabulary detection and segmentation are identified under the guidance of the hot area and are subjected to trapping counting consistency constraint quantification of population density and damaged area, and result reinjection forms linkage output, so that the technical effects of cross-scale physical consistency fusion, accurate delineation of risk hot areas in advance, measurable and available whole-process uncertainty and cross-modal consistency quantification are realized.

The present invention discloses a method, system and medium for extracting a remote sensing ecological index considering human activity intensity, belonging to the technical field of remote sensing ecological environment monitoring and evaluation. The steps include: S1, aiming at the problems of difficult acquisition of multi-source data, low accuracy and insufficient calculation efficiency in large-scale habitat quality assessment in alpine arid areas, an improved method for remote sensing ecological index based on the Google Earth Engine cloud platform and human activity intensity is proposed; S2, a salinity index is introduced, and an improved method for extracting remote sensing ecological index is proposed; S3, combining four indicators of night light, percentage of built-up land, population density and landscape development index, a method for calculating human activity intensity is proposed; S4, coupling the improved remote sensing ecological index, a remote sensing ecological index considering human activity intensity is established to realize rapid and refined calculation of the remote sensing ecological index. The present invention provides strong data support for ecological management and scientific decision-making in alpine mountainous areas.

This invention discloses an effective method for controlling leaf curling in macadamia nuts, comprising the following steps: selecting mineral oil with an effective ingredient content ≥99%, mixing it with water at a volume ratio of 1:100-400, then adding an emulsifier accounting for 0.05%-0.2% of the total weight of the mixture, stirring evenly to prepare a mineral oil emulsion; applying the emulsion using a misting device based on the growth characteristics of macadamia nuts during the shoot emergence period, pre-flowering period, young fruit stage, and mid-fruit development stage, combined with the differences in insect resistance among different varieties; integrating four measures: pruning and shaping control, orchard sanitation, soil regulation, and nutrient enhancement; and regularly monitoring key indicators such as thrips population density, leaf curling rate, and deformed fruit rate using a five-point sampling method, adjusting the control plan based on quantitative data. This invention achieves highly efficient control of thrips, prevents leaf curling, improves macadamia nut yield and quality, and protects the orchard ecological environment by optimizing the mineral oil emulsion formulation, precise application strategies, synergistic agricultural management measures, and dynamic monitoring and adjustment.