169 results about "Disease damage" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

The invention relates to the technical field of disease and insect pest detection, and further relates to a disease and insect pest image multispectral imaging and deep learning intelligent detection system, which comprises a multispectral radiance calibration unit, an image analysis and processing unit and a disease and insect pest probability detection unit, the multispectral radiance calibration unit is used for carrying out multiband imaging on vegetation leaf surfaces in the forest region, deducting dark field digital counts from original digital counts and then converting the original digital counts into radiance of corresponding bands; the image analyzing and processing unit is used for converting the radiance into surface feature apparent reflectivity; multiplying the scab spectrum gradient contrast index by the spectrum heterogeneity index to obtain coupled pest and disease damage characteristics; and the pest and disease damage probability detection unit is used for mapping the convolution output of each layer into a pest and disease damage probability value based on a generalized error function. The accuracy and practicability of disease and pest detection are remarkably improved.

The invention relates to the technical field of image recognition, and discloses a multispectral image and deep learning-based forest disease and insect pest automatic recognition method, which comprises the following steps of 1, carrying a multispectral camera containing a red edge wave band through an unmanned aerial vehicle to obtain a forest region image; 2, calculating a red edge normalized vegetation index of the image; 3, performing time sequence modeling on the red edge normalized vegetation index data of more than five consecutive periods, and inputting a time sequence convolutional network to generate an early lesion probability graph; 4, detecting a pest and disease damage target by adopting a multi-scale adaptive feature pyramid network; 5, outputting a disease and pest distribution thermodynamic diagram; and 6, driving the unmanned aerial vehicle cluster to execute precise pesticide spraying. According to the method, through the high sensitivity of the red-edge wave band to chlorophyll degradation and in combination with sequential convolutional network dynamic modeling, an initial lesion area can be recognized 7-10 days before disease development, the early disease recognition capability is remarkably improved, the disease discovery period is shortened, and large-scale disease diffusion is effectively avoided.

The invention relates to the technical field of agricultural disease and pest prevention and control, and discloses a big-data-driven disease and pest green prevention and control decision-making method. The method comprises the following steps: collecting multi-source heterogeneous agricultural environment data containing meteorological elements, crop physiological indexes and field biological activity information, and generating a dynamic weight coefficient according to a data source temporal-spatial resolution and a measurement dimension; constructing a disease and pest risk dynamic evaluation model, and calculating disease and pest occurrence probability indexes in different growth stages in different regions based on coefficient fusion data; establishing a green prevention and control measure knowledge base, and matching a candidate prevention and control scheme set according to crop types and growth cycles; in combination with the disease and pest occurrence probability index and the candidate scheme set, the prevention and control effect, the environmental influence and the economic cost are weighed, and an optimal prevention and control strategy sequence is generated; and implementing a dynamic adjustment mechanism, updating a disease and pest occurrence probability index according to real-time farmland feedback data, iteratively correcting an optimal prevention and control strategy sequence, and assisting agricultural green sustainable production.

The invention discloses a production method of distiller's grain fermentation liquid fertilizer specially applied to roxburgh rose. The distiller's grain fermentation liquid fertilizer is prepared bythe step of evenly mixing 50 to 80 parts of distiller's grain fermentation extracting solution, 0.5 to 1 part of microelement, 5 to 10 parts of seaweed meal, 5 to 10 parts of natural active organism,2 to 5 parts of potassium bicarbonate, 2 to 5 parts of natural active organism and 2 to 5 parts of natural surface active agent. A preparation method of the distiller's grain fermentation extracting solution comprises the steps: evenly stirring 50 to 80 parts of distiller's grain, 50 to 150 parts of rice washing water, 1 to 2 parts of EM microflora fermentation liquid and 0.5 to 1 part of sugar, performing composting fermentation at the room temperature to obtain a distiller's grain fermentation material, stirring and extracting through a sodium carbonate water solution, filtering and performing evaporation and condensation on obtained filtrate until the density is 1.1 to 1.2 to obtain distiller's grain fermentation extracting solution. The distiller's grain fermentation liquid fertilizerdisclosed by the invention has a good use effect, meanwhile can effectively prevent pest and disease damage to the roxburgh rose and is high-efficiency environment-friendly liquid fertilizer.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

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 relates to the field of disease and insect pest monitoring, and discloses a garden plant disease and insect pest intelligent early warning system based on Internet of Things, comprising an environment monitoring module for collecting environment data related to disease and insect pest breeding based on a sensor network; the pest trapping and counting module is used for trapping pests by using an intelligent trapping device of a pest attractant and is provided with a counting sensor and a pest classification and identification unit to obtain the population density of different types of pests; the plant phenotype monitoring module captures plant phenotype changes by deploying high-definition multispectral camera equipment, and automatically diagnoses pest and disease damage types by using an image recognition algorithm; and the disease and pest early warning module analyzes by using a preset early warning model based on the data of the environment monitoring module, the pest trapping statistics module and the plant phenotype monitoring module to obtain disease and pest indexes, compares the disease and pest indexes with a preset threshold value, and immediately sends out early warning information once the disease and pest indexes exceed the preset threshold value.

The invention discloses a cross-architecture knowledge distillation method based on fruit and vegetable disease and insect pest image classification, and relates to the technical field of fruit and vegetable disease and insect pest image classification. Constructing a teacher network model based on visual Transform and a student network model based on a convolutional neural network; realizing logs distillation by utilizing a cross attention mechanism; intensifying the capture of global and local features by the student model through inter-sample and intra-sample relation distillation; and training the student network based on multi-loss weighted optimization to obtain a classification model with high precision and light weight. According to the method, the global perception ability and the deep semantic knowledge contained in the pre-trained visual Transform teacher model are efficiently migrated to the lightweight convolutional neural network student model, so that the recognition ability and the classification performance of the student model on complex pest and disease damage characteristics are remarkably improved.

The invention provides an unmanned aerial vehicle target identification and positioning method and system based on multispectral fusion. The method comprises the following steps: firstly, acquiring multispectral image data, then analyzing the multispectral image data of multiple time phases into pixel-level data, then identifying an early infection area of the plant diseases and insect pests, then constructing a geometric distortion correction model, and then determining the relative position of the early infection area of the plant diseases and insect pests through multi-view space intersection calculation. And finally, fusing the real-time differential global navigation satellite system positioning data of the unmanned aerial vehicle and the relative position of the disease and insect pest early-stage infection area to output absolute geographic coordinates of the disease and insect pest early-stage infection area. According to the technical scheme provided by the invention, accurate conversion from a local coordinate system to a global geographic coordinate system is realized, an exact spatial position basis is provided for precise agricultural operation, and precise positioning of a pest and disease damage area is realized.

The invention belongs to the technical field of knowledge maps, and discloses a grape disease and insect pest automatic question-answering system based on a multi-modal knowledge map. Comprising a data collecting and processing module, a knowledge graph construction module, a main control module, a named entity recognition module, a text classification model module, a multi-modal fusion module, a knowledge graph storage module, an entity matching module and a query evaluation module. Constructing a multi-modal knowledge graph in the field of grape diseases and insect pests; the system analyzes characteristics of grape disease and insect pest data, studies a structured expression and integration method of grape disease and insect pest knowledge, constructs a grape disease and insect pest multi-mode knowledge graph ontology concept framework, and provides support for unified management and efficient utilization of related knowledge; the named entity recognition (NER) is carried out by utilizing a global normalization thought aiming at the condition that a hierarchical or nested relationship exists between entities in a knowledge graph construction process. An entity linking technology is adopted for data of different modes, and a grape pest and disease damage multi-mode knowledge graph is constructed.

The invention discloses a bionic gastrodia elata cultivation method based on modular mushroom sticks and an ecological prevention and control system, and belongs to the technical field of ecological cultivation of traditional Chinese medicinal materials. The cultivation method specifically comprises the following steps: (1) adopting modular mushroom sticks; (2) adopting a pest and disease damage prevention and control method; (3) adopting an ecological cooperation and environment adaptation cyclic utilization method; (4) an intelligent and automatic production method is adopted; (5) adopting a bionic cultivation process; and (6) adopting a gastrodia elata-morchella esculenta rotation method. According to the method, the wood cost is reduced by 80%, the disease occurrence rate is smaller than or equal to 3.5%, the insect attack rate is smaller than or equal to 4%, the chemical pesticide use amount is reduced by 90%, the yield of gastrodia elata per mu reaches 450 kg, the yield is increased by 20%, the yield of fresh mushrooms per mu is 310 kg through rotation of morchella esculenta, the comprehensive income is increased by 6000 yuan / mu, and the method is suitable for large-scale planting of resource-saving gastrodia elata in plains and under-forest planting scenes at the altitude of 800-1200 m.

The invention discloses a rice field insect pest detection method and system based on deep learning, and relates to the technical field of deep learning, and the method comprises the following steps: receiving the soil humidity and environment temperature of a rice field, and determining a temperature and humidity abnormal region of the rice field based on the soil humidity and environment temperature of the rice field; acquiring field image data in the temperature and humidity abnormal area of the rice field; processing the field image data in the temperature and humidity abnormal region of the rice field based on morphological closed operation and morphological gradient operation to obtain processed field image data, inputting the processed field image data into a pre-established YOLOv5s + target detection algorithm model, and outputting to obtain a pest and disease damage identification result; and performing image splicing generation based on the field image data in the temperature and humidity abnormal region of the rice field and the pest and disease identification result to obtain a disaster grading map.

The invention relates to the technical field of data analysis, and further relates to a forest pest and disease identification method based on data analysis, and the method comprises the steps: 1, obtaining an aviation orthoimage of a target forest region and a trapping point count in a set time interval, and associating the trapping count to a sample region unit according to the recent affiliation; 2, a composite simplex complex composed of a plaque layer, a sample area layer and a trapping layer is constructed on a forest map grid, counter examples are screened out through a two-stage verifier, and then suspected pest and disease damage triggering units are output; step 3, establishing a disease and insect pest atlas database, and describing typical modes by disease type topological fingerprints; and carrying out similarity judgment on the topological evidence packet and the disease type topological fingerprint to generate a disease type, a range and a credibility level, and carrying out layering labeling on a forest map grid. According to the method, the fine-grained structure change of diseases and insect pests can be captured in an early stage, and the recognition accuracy and interpretability are improved through multi-source evidence synthesis.

The invention discloses a ridge-crossing type variable-spraying-width strawberry spraying robot structure and an operation control method thereof, and aims to meet the requirement for precise operation in a complex environment of a strawberry garden, improve the operation efficiency and reduce the labor cost. The chassis adopts a hub motor driving wheel set and a universal wheel set and is combined with a height adjusting device, so that flexible running and terrain adaptive capacity between ridges of the strawberry garden are realized; the spraying system supports dynamic adjustment of the spraying width and the spraying amount through the design of a carbon fiber folding rod and a modular nozzle, and the variable spraying requirement is met. The robot utilizes a depth camera to collect strawberry plant and inter-ridge environment information, accurately recognizes plant positions, growth states and pest and disease damage conditions through a Fast R-CNN image recognition model, and realizes autonomous navigation and path planning in combination with a GNSS navigation module and an MPC model prediction control algorithm. The composite sensor group monitors the running state and the working environment of the robot in real time, and provides data support for spray volume adjustment and path optimization.

The invention relates to the technical field of image processing, in particular to an intelligent detection system for field wheat ear diseases and insect pests. The system comprises an image acquisition module which is used for acquiring a field wheat ear image; the illumination analysis module is used for constructing an illumination influence coefficient based on a brightness value distribution condition in a neighborhood pixel block of each pixel point; the gray analysis module is used for determining a feature influence coefficient based on the gray value distribution condition in the neighborhood of each pixel point in the neighborhood pixel block; the image enhancement module is used for adaptively adjusting a preset basic attenuation parameter based on the feature influence coefficient and the illumination influence coefficient, and performing image enhancement on the wheat ear image in combination with a non-local mean filtering algorithm; the disease and insect pest detection module is used for detecting disease and insect pests of the wheat ears through the enhanced image; during denoising, detail reservation of the pest and disease damage area is enhanced, effective denoising is carried out, and the accuracy of subsequent field wheat ear pest and disease damage detection is improved.

The invention relates to the technical field of pest prediction, in particular to a pest occurrence environment threshold prediction method and system based on a knowledge graph, and the method comprises the following steps: obtaining parameters such as air temperature, humidity, illumination and rainfall through a sensor, generating time series data through sliding window standardization, and extracting sensitive factors through K mean value recognition mutation; the equal-frequency box is combined with the behavior log to generate threshold mapping, the random forest analyzes a triggering sequence to construct a causal chain, and the triple is embedded into the map to update edge weight matching to generate a prediction path map. According to the method, environment sequence features are extracted through a sliding window, mutation events are clustered and recognized, pest sensitive factors are screened, five-level threshold values are established in a binning mode, behavior nodes are mapped, a causal chain node sequence is generated through a random forest model, a knowledge graph is constructed through triples, dynamic update weights are embedded, real-time data are matched, and nonlinear association is carried out. The sudden change behavior relationship is identified, and the early warning precision and response efficiency are improved.

The invention discloses an agricultural pest automatic identification method based on Internet of Things and artificial intelligence, and the method comprises the following steps: obtaining image information and environment information of crops through multi-dimensional image collection and environment data fusion collection; utilizing a deep convolutional neural network model and an attention mechanism to extract crop disease and insect pest features, and constructing a comprehensive feature vector; performing similarity matching calculation on the feature vector of the to-be-identified crop and a standard feature vector in a pest and disease damage feature library, and identifying the type and severity of the pest and disease damage; carrying out rationality evaluation and correction on an identification result through cross check and a data check rule; and calling a preset intelligent decision-making system according to the finally determined pest and disease damage type and grade and the growth stage and environmental condition of the crops, and generating a precise prevention and control scheme. The method improves the accuracy and efficiency of pest recognition, is suitable for different crops and environmental conditions, and provides powerful support for agricultural production.

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 relates to the technical field of agricultural information, in particular to an intelligent crop disease and pest monitoring system based on multispectral imaging and a use method thereof, and the system comprises a multispectral data acquisition and preprocessing module, a visual intelligent analysis module, an intelligent diagnosis module and a decision support module. According to the method, a high-contribution-degree spectral band is dynamically screened through a cross-band attention mechanism, and a 3D convolutional network and mutual information constrained feature decoupling reconstruction algorithm is combined, so that adaptive fusion and noise separation of multispectral features are realized to generate sparse feature representation focusing disease and pest sensitive information, and based on an Inception-v3 and LightGBM cascaded multi-model fusion algorithm, the disease and pest sensitive information is obtained. And in combination with Monte Carlo Dropout confidence evaluation, Grad-CAM interpretability analysis and TCN time convolutional network time sequence prediction, an intelligent diagnosis system with reliability quantification, decision visualization and trend pre-judgment capabilities is constructed, and accurate identification of pest and disease damage types and severity and dynamic prediction of diffusion trends in the next five days are realized.

The invention discloses an AI visual identification-based landscaping pest and disease damage intelligent monitoring and early warning system, and particularly relates to the technical field of AI visual identification pest and disease damage, and the system comprises the steps: obtaining time sequence visual image data, space-time labels and environment sensing data of a target garden area; inputting the time sequence visual image data into a pre-trained disease and insect pest AI identification model, and outputting an identification result; calculating initial occurrence density and local diffusion trend parameters of disease and pest species, and generating a spatial distribution thermodynamic diagram; constructing a multi-factor risk assessment model; dynamically generating a graded early warning signal; according to the invention, the landscaping pest and disease damage intelligent monitoring and early warning system is constructed through the multi-source heterogeneous data acquisition module, the feature extraction and identification module, the data aggregation and space association module, the multi-factor risk assessment and time sequence prediction module and the dynamic early warning and decision support module; the problems of single monitoring dimension, weak data fusion and space association capability, fragmentation of decision support information and the like are solved.

The invention belongs to the technical field of crop disease and insect pest recognition, and particularly discloses a crop disease and insect pest recognition analysis method based on image processing, and the method comprises the steps: collecting a state image of a plant in a dynamic environment, and eliminating the interference of the dynamic environment on the state image recognition of a plant leaf; differentiated analysis is carried out on disease and insect pest characteristics of different areas corresponding to each leaf area, so that leaf disease and insect pest types and defect data thereof are accurately identified; the color value distribution health state of leaves near a leaf vein distribution area is detected by simulating the leaf vein distribution contour of the interference area, the corresponding leaf vein structure stability of each leaf area is analyzed, and the leaf state of the remaining area after the interference area and the pest and disease damage area of the leaves are removed is identified. And assisting in evaluating the leaf risk area condition in each leaf area, and judging the pest and disease damage state in the plant leaf according to the condition. The recognition precision of the crop state image is improved, and targeted prevention and control measures can be taken.

The invention discloses a crop disease and pest identification method and system based on an unmanned aerial vehicle, and belongs to the technical field of image identification, and the method comprises the steps: building an association mapping table based on geographic coordinates, obtaining disease and pest labels obtained through rough identification of each planting collection image, and obtaining a label matrix, performing feature extraction on each planting acquisition image to construct a pest and disease damage matrix; and on the basis of the label matrix and the pest and disease damage matrix, determining a refined sub-region and the recognition precision of the refined sub-region, and according to the coarse recognition result of the non-refined sub-region and the fine recognition result of the refined sub-region, obtaining a pest and disease damage distribution map, and outputting and displaying the pest and disease damage distribution map. And an accurate spatial position and severity basis is provided for agricultural prevention and control.

The invention relates to the technical field of pest and disease damage risk prediction methods, in particular to a forestry pest and disease damage risk prediction method and system, and the method comprises the following steps: obtaining forestry pest and disease damage historical data with a preset time granularity; generating corresponding characteristic parameters; generating standardized feature data; randomly dividing a training set, a verification set and a test set according to a preset proportion; training the risk prediction model through the training set, and adjusting hyper-parameters of the risk prediction model according to the verification set; modeling long-range dependence in the time sequence based on a phase transformation method; performing model evaluation on the trained risk prediction model by using the test set; a risk prediction model is optimized according to a model evaluation result, an optimal model is finally determined, phase transformation can capture a periodic mode in disease and pest diffusion through phase encoding analysis of time sequence data, and the model can process short-term fluctuation and long-term trend at the same time in combination with long and short term dependence modeling capability of an LSTM network.

The invention provides a high-production planting method and system for realizing agricultural multi-dimensional elements based on geographic information, and relates to the technical field of geographic information, and the method comprises the steps: S1, obtaining multi-source geographic information data of a target planting region, comprising soil type spatial distribution data, climate condition statistical data, landform three-dimensional feature data and pest and disease damage occurrence records; s2, carrying out spatial superposition and correlation analysis on the multi-source geographic information data, and generating a multi-dimensional geographic element comprehensive layer integrating the soil fertility grade, the moisture infiltration capacity and the accumulated temperature illumination adaptation degree; and S3, presetting two target detection points in the multi-dimensional geographic element comprehensive layer, wherein the detection points are respectively located at a geographic feature turning point and a region center point of the planting region boundary. By integrating multi-source geographic information, accurate division of planting areas, intelligent matching of varieties and parameters and dynamic regulation and control of growth cycles are realized.

The invention relates to the technical field of orchard disease and insect pest early warning and prevention resource management, in particular to an orchard disease and insect pest early warning and prevention resource management decision support method based on multi-source data, and the method comprises the steps: firstly obtaining orchard multi-source data, and constructing a space-time fusion data set with a geographic grid as a unit through space-time alignment and standardization processing; inputting the data set into a machine learning multi-classification model, and outputting a risk quantification index of a future specific pest and disease damage; triggering a prevention and treatment decision engine based on an index, and combining a resource knowledge base to generate an optimal prevention and treatment prescription through multi-objective optimization; then, matching prevention and control resource real-time inventory with agricultural machinery space positions, and generating a resource scheduling scheme and agricultural machinery operation path planning; and finally, the integrated information is visualized on an electronic map, a control instruction is issued, and feedback is received to form closed-loop management. The method improves the accuracy of disease and pest early warning, achieves the optimal prevention and control scheme, improves the utilization efficiency of resources and agricultural machinery, and provides support for scientific management of orchard diseases and pests.

The invention relates to the technical field of agricultural planting, in particular to automatic onion planting equipment. Comprising a machine frame, driving wheels and a control box, the driving wheels are symmetrically and rotationally arranged on the machine frame, the control box is installed on the machine frame, a hole opening mechanism and a bulldozing mechanism are arranged on the machine frame, the hole opening mechanism is used for opening holes in soil, the bulldozing mechanism is used for covering the roots of onion seedlings with the soil, and a first electric sliding table is arranged on the machine frame. By arranging a structure of combining the clamping plate, the fixing plate, the spring and the first distance sensor, the onion seedling stem size can be detected in real time in the seedling taking process, whether onion seedlings are qualified or not can be automatically judged according to a preset standard, too large or too small unqualified seedlings are effectively removed, only high-quality seedlings meeting requirements are planted, and the planting efficiency is improved. The problems of bolting, flowering, seedling death and the like caused by inconsistent plant growth are avoided, so that the survival rate and the final yield are improved, a basis is provided for unified field management, and the management difficulty of later water, fertilizer, pest and disease damage and the like is reduced.

The invention provides an unmanned aerial vehicle remote sensing monitoring and evaluating method and system for field orchard diseases and insect pests, and relates to the technical field of orchard disease and insect pest monitoring and evaluating. The interference of complex background noise is effectively eliminated by constructing a ternary separation index integrating feature enhancement, background suppression and brightness normalization; a threshold value is determined based on the exponential distribution, pixels are accurately divided into a non-canopy background, a shadow canopy and an illumination canopy, and fine segmentation of an unstructured scene is achieved; constructing a gain compensation item by using a local illumination reference mean value, correcting a shadow canopy reflectivity curve, and eliminating illumination non-uniform interference; selecting a local high quantile to establish a health reference basis, coupling a discriminant item and an attenuation item, and resolving to obtain a physiological stress index, so as to realize acute capture of early weak disease characteristics; and performing power weighted accumulation on the abnormal pixel difference value, outputting a regional hazard level index, and objectively quantifying the overall disaster risk of the region through nonlinear aggregation.

The invention relates to the technical field of agricultural informatization and artificial intelligence crossing, and discloses a multi-source data fusion intelligent agricultural knowledge graph construction system and method. Comprising a data acquisition module, a data processing and analysis module, a data safety monitoring module, a knowledge graph construction module, a multi-modal feature analysis module and an agricultural large model application module, multi-source agricultural data is acquired through multiple channels of an acquisition tool and a middleware platform, and the acquired data is preprocessed and analyzed by adopting a data processing technology; based on a block chain and an identification analysis technology, security in a data acquisition process is monitored, attribute-based encryption ABE is adopted to complete dynamic desensitization transmission of data, an agricultural knowledge graph database is constructed according to an analysis result, and feature extraction is performed on crop growth states and pest and disease damage conditions to realize crop full-life-cycle management. The agricultural production benefit and the market competitiveness of agricultural products are improved.