269 results about "Disease damage" patented technology

The invention relates to the technical field of agricultural intelligent detection, in particular to a fruit tree disease and pest detection method based on machine vision, which comprises the following steps: acquiring visible light, infrared light and ultraviolet light images of a fruit tree at multiple angles through a multispectral camera to obtain complete fruit tree surface information, and preprocessing the acquired images to obtain a fruit tree disease and pest detection result; comprising the steps of denoising, illumination correction and image alignment, extracting feature vectors, including the color, shape and size of scabs, pest distribution and other features, of pest and disease damage from preprocessed images through a convolutional neural network, fusing the pest and disease damage features with environmental data by using a multi-modal data fusion algorithm, and enhancing the accuracy of pest and disease damage classification. Classifying and identifying diseases and pests based on a support vector machine classification algorithm; according to the dynamically adjusted threshold value and suggestions provided by the expert system, a disease and pest diagnosis report is generated, and prevention and control measure suggestions are provided; according to the invention, efficient and accurate detection and management of fruit tree diseases and insect pests can be realized, and the intelligent level of agricultural management is improved.

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 relates to the technical field of agricultural planting management, and discloses an intelligent agricultural planting method and system. The method comprises the following steps: analyzing nutritional requirements based on environment sensing and plant image data, and generating nutrition allocation data; carrying out growth prediction through seedling image features and formulating a cultivation standard; spectral parameters are extracted according to environment condition analysis and canopy images, and an environment regulation and control scheme is formed; combining growth state evaluation and pest and disease damage characteristics to calculate health indexes; a management strategy is optimized through growth parameter time sequence analysis; and determining the harvesting opportunity and grade evaluation according to the plant maturity and quality characteristics. According to the application, environment monitoring, growth diagnosis, quality evaluation and market analysis are organically combined, so that more accurate and intelligent agricultural planting decision support is provided.

The invention discloses a system for realizing crop intelligent identification based on a CLIP multi-mode method, and belongs to the technical field of crop identification, and the method specifically comprises the steps: collecting real-time images, text description and environment data of crops; performing image processing on the real-time image; a CLIP model is used for carrying out feature extraction on the image and text description, visual features are extracted from the image, semantic features are extracted from the text, the text description comprises crop types and symptom description of diseases and pests, the environment data and the visual features are fused, and a multi-modal feature vector is generated; the multi-modal feature vectors are classified by using a pre-trained classifier, specific crop types and pest and disease damage types are identified according to extracted multi-modal features, identification results are displayed to users through a visual interface, and information is fed back to a monitoring center; according to the method, visual, semantic and environmental features are fused, so that the accuracy of crop disease and insect pest recognition is remarkably 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 discloses a mangrove forest disease and insect pest remote monitoring and intelligent prevention and control system based on the Internet of Things. A plurality of modules such as an Internet of Things sensor network, an unmanned aerial vehicle inspection system, a high-definition monitoring camera network, intelligent prevention and control equipment and a software platform are integrated; the Internet of Things sensor network monitors environmental parameters and pest and disease damage signs in real time, the unmanned aerial vehicle inspection system provides aerial inspection support, and the high-definition monitoring camera network realizes 24-hour uninterrupted monitoring on the ground; the software platform is responsible for data acquisition, transmission, analysis and early warning release, and provides a remote control interface for management personnel to perform accurate prevention and control; the intelligent prevention and control equipment automatically or remotely executes prevention and control tasks according to the monitoring data and the early warning information; the system also has additional functions of energy management, network communication, user authority management and the like, and continuous and stable operation and data security of the system are ensured. The implementation of the system can effectively improve the intelligent level of mangrove forest pest control, and provides powerful support for protecting a mangrove forest ecological system.

The invention relates to a multi-source remote sensing data quantitative inversion regional crop water consumption demand data fusion method. The method comprises the steps of data acquisition, data assimilation, data fusion and formation of a multi-source heterogeneous database. A distributed multi-source heterogeneous database is built by using advanced technologies such as comprehensive grid computing and cloud computing, a high-performance storage retrieval multi-source heterogeneous database and a data product oriented to a complex irrigation data system are built, agricultural information is collected in real time, a database is formed, and a database management system is established. And finally, efficient data management and sharing are realized. Related agricultural irrigation processes are systematically monitored, including monitoring of water consumption of crops, monitoring and forecasting of agricultural irrigation and the like. An agricultural production management system and an agricultural expert system are developed and are mainly used for diagnosing water shortage, fertilizer shortage and pest and disease damage of crops. The data acquisition forms are diversified, the parameter optimization quantity is large, the acquisition of large-range farmland parameters is realized, and a complete system is formed with remote sensing irrigation monitoring.

The invention provides an agricultural product production process intelligent supervision system and method based on big data analysis. The system comprises a data acquisition assembly, a data analysis assembly and a management planning assembly. The method comprises the following steps: deploying various sensors in a field for automatically collecting target data, and uploading the target data to a data analysis component in real time; analyzing the target data by using the collected target data through an agricultural product production data analysis algorithm to obtain the change trend of the soil nutrient content, and predicting when to apply fertilizer and the amount of the fertilizer; meanwhile, pest and disease damage is predicted, and a corresponding prevention and control scheme is provided; suggestions are provided for irrigation according to historical data and current environmental conditions; the analysis result is applied to an agricultural product planting area; the agricultural product planting area plans the planting layout of agricultural products by means of satellite remote sensing and a geographic information system. The agricultural production efficiency and economic benefits are improved, and powerful technical support is provided for agricultural sustainable development.

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 provides a forest cultivation disease and pest early warning method combined with multi-source data analysis, and relates to the technical field of disease and pest early warning, and the method comprises the steps: determining a disease and pest type list and a disease and pest distribution position weight list of a preset forest cultivation region; classifying the plant disease and insect pest type list according to the diffusion mode of plant diseases and insect pests in vegetation; determining activity areas of various types of diseases and insect pests in the first type of disease and insect pest list and the second type of disease and insect pest list; a first monitoring data set is collected, vegetation internal damage features are analyzed, and a first diffusion situation list is generated; vegetation surface damage features are analyzed, and a second diffusion situation list is generated; and according to the first diffusion situation list and the second diffusion situation list, carrying out disease and pest early warning. The technical problem of high difficulty in multi-sensor data fusion in the prior art can be solved, efficient fusion and accurate analysis of the multi-sensor data are realized, and the technical effect of improving the disease and pest monitoring precision is achieved.

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 provides an unmanned aerial vehicle-based accurate spraying system for forest diseases and insect pests, and relates to the technical field of disease and insect pest treatment, and the system comprises a real-time monitoring module which is used for carrying out real-time monitoring on a forest region; the division and identification module is used for carrying out detection and classification, generating a data classification result, carrying out division and identification on the forest region and determining grid parameters of the forest region; the diffusion prediction module is used for performing disease and pest diffusion prediction to obtain a disease and pest diffusion coefficient; the synchronous updating module is used for constructing a pest and disease prediction spreading trend; and the precise spraying module is used for formulating a spraying task, executing and carrying out effect evaluation, carrying out dynamic feedback optimization, generating a spraying optimization task and carrying out precise spraying of diseases and insect pests. The technical problems that in the prior art, small differences between plant diseases and insect pests and healthy plants are often difficult to distinguish accurately, especially when the plant diseases and insect pests are distributed dispersedly or hidden, spraying leakage or spraying non-uniformity exists, and then the spraying effect is insufficient are solved.

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 provides a self-supervised mulberry disease and insect pest recognition method and system fused with a multi-mode YOLO algorithm, and the method comprises the steps: providing a Multi-Modan-YOLO algorithm fused with a multi-mode information processing technology, namely, the multi-mode YOLO algorithm, and carrying out the self-supervised mulberry disease and insect pest recognition method fused with the multi-mode YOLO algorithm; and audio information and thermal imaging information are combined to further improve the accuracy of the identification result of the pest and disease damage types. A self-supervised learning framework driven by a multi-modal large model is introduced, when the identification deviation risk exceeds a preset threshold value, the multi-modal large model is called to generate a category label, and the model is endowed with the ability of dynamically generating a new label for a new type of pest and disease damage. Therefore, misrecognition caused by non-uniform distribution of training data or missing of a certain category of data is effectively prevented; according to the method, targets such as disease spots and insect pests on mulberry leaves are accurately identified, so that the efficiency and accuracy of mulberry disease and insect pest prevention and control are improved, the influence on silkworm production is reduced, and yield increase is promoted.

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 provides an agricultural pest prediction optimization method, and relates to the field of pest prediction. The method comprises the steps of collecting agricultural pest and disease damage prediction key information, preprocessing agricultural pest and disease damage prediction key information data, constructing an agricultural pest and disease damage prediction model, training the agricultural pest and disease damage prediction model by using a training set, and testing the agricultural pest and disease damage prediction model by using a test set. The agricultural pest prediction model is composed of a feature conversion module, a time correlation module, a variable interaction module and nonlinear feature interaction fusion; wherein the feature conversion module converts input preprocessed data into feature representation suitable for model processing, the time correlation module captures a dependency relationship between time steps in a time sequence, the variable interaction module focuses on a mutual relationship between different variables, and nonlinear feature interaction fusion is used for integrating information of different dimensions, so that the time sequence is obtained. And the mode of complex data can be accurately understood. The method provided by the invention can accurately predict agricultural pest and disease damage conditions in a period of time in the future.

The invention discloses a forestry pest and disease damage intelligent monitoring and early warning system based on the Internet of Things, and relates to the technical field of pest and disease damage management. The forestry pest and disease damage intelligent monitoring and early warning system based on the Internet of Things comprises a data acquisition module used for collecting an environment characteristic data set in real time; the preprocessing module is used for carrying out data vectorization, data cleaning, data synchronization and data standardization processing on the environment characteristic data set collected in real time; the transmission module is used for selecting an optimal data transmission path for data transmission according to the node state analysis result; and the abnormity checking module is used for carrying out data abnormity detection on the real-time environment characteristic data set transmitted by the transmission module. The problem that in an existing forestry pest and disease damage intelligent monitoring and early warning system based on the Internet of Things in the field environment, equipment may be interfered by non-pest and disease damage factors, a sensor may be touched by mistake, and false alarm is caused is solved.

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 provides an apple orchard pest and disease damage intelligent monitoring and early warning robot, and relates to the technical field of artificial intelligence. The apple orchard pest and disease damage intelligent monitoring and early warning robot comprises a power supply module, a pest luring and capturing module, a data processing and analyzing module, a data transmission and communication module, a walking and navigation module, a user interface and control module and an image acquisition and processing module. Identification, counting, monitoring and early warning and knowledge questioning and answering of diseases and insect pests are realized. The apple orchard pest and disease damage intelligent monitoring and early warning robot provided by the invention realizes efficient, accurate and real-time pest and disease damage monitoring and early warning of the apple orchard, and improves the automation level and the intelligent degree of apple orchard pest and disease damage management.

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 discloses a green prevention and control integrated method for litchi diseases and insect pests. According to the comprehensive green prevention and control method for the litchi diseases and insect pests, a litchi fitness cultivation technology, a light insect trapping and killing technology, a color insect trapping and killing technology, a biopesticide prevention and control technology, a one-spraying-three-province synergistic and decrement pesticide application technology and a precise forecasting technology are integrated. The method has the advantages that operation is simple, litchi planting operators can easily understand the method, a series of green prevention and control technologies are integrated, the green prevention and control effect is obvious, the amount of fertilizer and pesticide for the litchis is reduced remarkably, good economic benefits are obtained, and the method is suitable for green and sustainable development of the litchis.

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.