329 results about "Crop disease" patented technology

The invention discloses a crop disease diffusion prediction method and system based on multi-modal fusion, and the method comprises the following steps: S1, collecting and preprocessing an RGB image sequence and a sensor data sequence of a crop growth environment, and generating an RGB image time sequence difference result and a sensor difference result through time difference processing; s2, mapping the RGB image time sequence difference result and the sensor difference result to a shared time sequence space through a time alignment algorithm, and generating a sensor alignment result and an RGB alignment result; s3, an FD-ViT prediction model is constructed; inputting the sensor alignment result and the RGB alignment result into an FD-ViT prediction model for prediction, and generating a prediction result; and S4, generating a disease diffusion thermodynamic diagram and early warning information according to a prediction result. According to the method, RGB image data and sensor network data are fused, a Transform-based time sequence prediction model is constructed, and early recognition and diffusion trend prediction of crop diseases are realized.

The invention discloses a multi-modal agricultural question and answer method and system for generating RAG based on retrieval enhancement, and the method comprises the steps: collecting and constructing crop disease image data containing a farmland complex background and corresponding text description, and forming an agricultural image-text knowledge database; based on the agricultural image-text knowledge database, multi-modal features of an input image and a query text are extracted, image-text joint similarity retrieval is carried out, and knowledge image-text candidates are obtained; inputting the knowledge image-text candidates into an image-text rearrangement module for rearrangement; taking the reordered image-text knowledge as condition input, accessing a large language model, and generating diagnosis description and prevention and treatment suggestions for the current crop diseases; and outputting a multi-modal question and answer result according to the image and question input by the user. According to the method, the agricultural image-text database is constructed, and multi-modal retrieval, rearrangement and large language model generation technologies are combined, so that accurate and efficient crop disease multi-modal question answering is realized, and the reliability of diagnosis and prevention suggestions is improved.

The invention provides a whole-crop explainable disease and pest diagnosis method and system based on a multi-mode large model. The method comprises the following steps: constructing a knowledge extraction model of a two-way cross attention mechanism based on relation guidance, extracting structured knowledge from authoritative agricultural data, and constructing a pest and disease knowledge enhancement database to dynamically retrieve prior knowledge of a target crop; a hierarchical image processing strategy is adopted, and global, local and target area multi-level feature information is extracted from an input image; inputting and priori knowledge are integrated into a comprehensive diagnosis instruction, and a thinking chain guiding module is introduced to guide a large model to carry out multi-step reasoning according to a reasoning path; and performing unified reasoning by using the multi-modal large model, and outputting a disease and pest diagnosis result and a diagnosis basis thereof. The method does not need manual marking of multi-modal data or retraining, can realize efficient diagnosis of whole crop diseases and insect pests under the condition that the multi-modal data and computing resources are limited, has low cost, strong generalization ability and high interpretability, and is suitable for large-scale agricultural production practice.

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 crop disease multi-modal diagnosis and classification method and system, and relates to the technical field of data processing, and the method comprises the steps: collecting disease multi-modal data, and carrying out the preprocessing; constructing a disease multi-modal diagnosis model, respectively inputting images and text sequences in the multi-modal data into a visual feature extraction subnet and a text feature coding subnet of the disease multi-modal diagnosis model, and extracting global visual features, sequence state features and global text features; calculating a global matching score based on the global visual features and the global text features, executing fine-grained local interaction on the global visual features and the sequence state features, introducing a category channel attention mechanism to correct the features obtained by interaction, and weighting to generate multi-modal fusion features; and constructing a loss function based on the global visual features, the global text features and the multi-modal fusion features, training a disease multi-modal diagnosis model, inputting to-be-classified data into the disease multi-modal diagnosis model, and outputting a classification result.

The invention provides an agricultural pest diagnosis and prevention method and device, electronic equipment and a storage medium, and relates to the technical field of agriculture, and the method comprises the steps: extracting image features, shooting time and a shooting position from a crop pest image of a target region; inputting the image features into a multi-modal recognition model to obtain a first diagnosis result output by the multi-modal recognition model; inputting the shooting time and the attribution environment data corresponding to the shooting position into the multi-source data mining model to obtain a second diagnosis result output by the multi-source data mining model; and inputting the first diagnosis result and the second diagnosis result into a disease and pest knowledge map for fusion reasoning, and generating a plant protection medication management suggestion. According to the invention, through the multi-modal information fusion and step-by-step reasoning technology and in combination with the disease and pest knowledge graph, the accuracy and interpretability of disease and pest diagnosis and the individuation of the prevention and control scheme are realized.

The invention relates to the field of agricultural intellectualization, in particular to a crop disease and insect pest real-time identification and analysis system based on deep learning, which comprises an image acquisition unit, a mobile control unit, an image identification unit, a disease evaluation unit and a disease and insect pest prediction unit, the core innovation of the invention lies in that an image recognition unit introduces a Riemannian geometry multi-scale manifold learning framework and comprises a manifold construction module, a manifold feature fusion module and a manifold constraint optimization module, and the manifold construction module maps crop image features to a Riemannian manifold space; the manifold feature fusion module fuses multi-scale feature manifolds through geodesic line connection and parallel transmission; and the manifold constraint optimization module executes network parameter optimization in a Riemannian space. The disease evaluation unit evaluates the severity of the disease based on the identification result; the disease and pest prediction unit predicts the disease development trend based on historical data and environmental information, the disease and pest recognition precision is remarkably improved, and particularly the rare disease recognition capability is improved by 23%.

The invention provides an intelligent diagnosis and decision-making method and system for crop diseases in intelligent agriculture, and the method comprises the steps: extracting spatial features through employing a convolutional neural network based on standardized remote sensing image data in a unified multi-source data set, generating preliminary positioning data of a scab region, and carrying out the preliminary positioning of the scab region; the scab area preliminary positioning data comprises scab position coordinates and range boundaries; inputting the key time weighted sequence data and the scab area preliminary positioning data into a space-time attention mechanism, and fusing spatial position coordinates and time environment features through attention weight distribution to generate comprehensive disease feature vector data; and based on the preliminary prevention and control prescription data, adopting a drug resistance evaluation model to analyze the drug use frequency, the disease type and the crop variety in the historical drug use data, generating optimized prevention and control prescription data, and adjusting the drug variety or dosage to reduce the drug resistance risk.

The invention discloses a generative AI-based disease and insect pest identification evaluation and multi-modal question and answer method, and relates to the technical field of crop disease and insect pest prevention, and the method comprises the steps: obtaining multi-modal data containing a standardized image and a structured text feature; utilizing a pre-trained pest and disease target detection model to identify the spatial position of a pest and disease target in the image, and cutting to obtain a target area image; a color feature vector and a shape feature vector of the target area are extracted, text features are combined, semantic enhancement and cross-modal alignment are performed through generative AI, and an explicit feature vector of diseases and insect pests is constructed; matching and analyzing with a preset disease and pest knowledge base, and identifying disease and pest types; quantifying the damage degree of the diseases and pests according to the characteristic parameter change characteristics to obtain a damage grade judgment result; generating a question-answer reply based on the type identification result and the hazard level; according to the method, the problems that the disease and pest recognition accuracy is limited, the harm degree is difficult to quantify and intelligent multi-modal decision question and answer support cannot be provided are solved.

The invention discloses a seed coating containing an arbuscular mycorrhizal inoculant. The seed coating is prepared from the following components in parts by weight: 25 to 35 parts of the arbuscular mycorrhizal inoculant, 35 to 45 parts of a soil conditioner, 15 to 20 parts of talcum powder and 15 to 20 parts of polyvinyl acetate. The invention further provides a preparation method of the seed coating containing the arbuscular mycorrhizal inoculant and application of the seed coating to reduction of soil heavy metal pollution, increase of disease resistance and stress resistance of crops and promotion of crop growth. The seed coating containing the arbuscular mycorrhizal inoculant wraps the surfaces of plant seeds, provides good nutritional ingredients for growth and development of the seeds, and is beneficial to improving the germination rate of the seeds under heavy metal cadmium stress, reducing soil heavy metal cadmium pollution, improving soil quality, increasing soil fertility and improving disease resistance and stress resistance of crops; growth of seedlings is promoted, and crop quality and yield are improved.

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 discloses a crop disease and insect pest detection algorithm fusing self-attention and a sample weighting mechanism. A YOLOv8 network is used as a basic model; through a two-branch self-attention DF-MSA architecture, the ability of the model to acquire pest target position information is enhanced; a cross-layer feature fusion module is introduced into the feature pyramid network structure, and multi-scale information is fused; and a sample weighting function is adopted to reduce the influence of difficult sample imbalance on a detection result, so that the detection accuracy is improved. And inputting the trained IP-YOLO by using a test set sample, and outputting a result. The method has high stability and robustness, and an effective means is provided for improving the performance of a crop disease and pest detection system.

The invention relates to the technical field of agricultural image recognition, and discloses a crop disease and pest recognition method and system based on image recognition. The method comprises the following steps: receiving an original image of a field crop, and performing multiple filtering and cleaning to obtain a standard image; positioning a crop main body in the standard image and generating a multi-stage image slice; constructing a disease and insect pest characteristic knowledge graph representing disease and insect pest types and concurrent evolution relations thereof; carrying out conjoint analysis on the multi-level slices and the knowledge graph, and generating an intermediate result through node matching and edge association reasoning; and integrating the intermediate result, the multi-level slices and the associated path information, and outputting a final identification conclusion and confidence by the decision model. According to the method, domain logic is introduced through the knowledge graph, combined reasoning is performed in combination with multi-scale image evidences, and the accuracy and robustness of disease and pest recognition and the interpretability of the decision-making process in a complex field scene are improved.

The invention belongs to the technical field of crop disease and insect pest detection, and discloses a method and a system for classifying, identifying and detecting crop disease and insect pests. According to the method, the lightweight and efficient FasterNet is adopted as the preliminary classifier, so that crop diseases, insect pests and weeds can be quickly and accurately distinguished, and the classification precision is improved. And secondly, for a disease image, through a segmentation model based on deep semantic segmentation and by adopting a double-stage segmentation strategy, respectively segmenting a leaf region and a scab region, thereby realizing high-precision segmentation and facilitating subsequent severity diagnosis and quantitative analysis. And for an insect pest image, utilizing a YOLOv11 target detection network to accurately select an insect pest position and identify an insect pest type. For a weed damage image, the YOLOv11 target detection network is also used to identify a weed area, and rapid acquisition and positioning of a field weed distribution condition are realized. According to the invention, the lightweight network and the efficient detection architecture are introduced, the recognition precision and the reasoning speed are both considered, and the dual requirements for real-time performance and reliability in field application of farmland can be met.

The invention relates to the technical field of agricultural disease detection and crop disease resistance screening, in particular to a rice bacterial leaf blight disease resistance screening method based on an unmanned aerial vehicle and deep learning, and adopts the technical scheme that an optimized YOLOv11-OBB model is composed of a C3k2FC module, an SPPFLSKA module, a SlimNeck module and a LiteHead module, is responsible for extracting features in an image and positioning bacterial leaf blight spots, and is used for screening the disease resistance of the rice bacterial leaf blight; bacterial leaf blight can be efficiently and accurately detected; a lightweight deep learning model is used, efficient operation on a resource-limited unmanned aerial vehicle platform can be realized, and the equipment and calculation cost is reduced; through the cooperation of a plurality of optimization modules, the detection precision of bacterial leaf blight spots is remarkably improved, and a stable detection effect can be kept; the operation process is simplified, and full automation of bacterial blight disease resistance screening is realized; therefore, low-cost, efficient, automatic and high-precision bacterial blight disease resistance screening is realized by utilizing the lightweight and optimized YOLOv11-OBB model and combining module design with high calculation efficiency, and the method is suitable for large-scale field application.

The invention relates to three antibacterial peptides and application thereof in resisting fungi and / or inducing plant immunity. Specifically, on one hand, the invention provides three antibacterial peptides which are novel functional antibacterial peptides ZJUAMP1, ZJUAMP2 and ZJUAMP65, the sequences of the novel functional antibacterial peptides ZJUAMP1, ZJUAMP2 and ZJUAMP65 are respectively shown as SEQ ID NO: 1, SEQ ID NO: 2 and SEQ ID NO: 3, and the novel functional antibacterial peptides have good antifungal activity, can activate an MAPK signal channel in a plant body and induce the plant to generate systematic disease resistance, and are suitable for the fields of crop disease prevention and control and plant protection. The two antibacterial peptides are simple in structure and easy to synthesize. On the other hand, the invention further provides application of the two antibacterial peptides in resisting fungi, activating a plant MAPK signal channel, inducing plant immune response, enhancing plant disease resistance and / or preventing and treating plant diseases.

The invention discloses a crop disease intelligent identification method and system based on an unmanned aerial vehicle image, and belongs to the technical field of intelligent agriculture, and the method comprises the steps of data multi-dimensional collection, early disease identification, disease space-time deduction and crop management suggestion. According to the method, early disease recognition based on the spectrum sensitive vegetation index and the attention guidance double-branch network is adopted, the spectrum sensitive vegetation index capable of amplifying weak disease spot features is constructed, the weak disease features are highlighted through multispectral information enhancement and time sequence feature inhibition, meanwhile, the attention mechanism is utilized to focus on potential disease areas, and the disease recognition accuracy is improved. Therefore, the accuracy and stability of early disease recognition are remarkably improved. Disease space-time deduction combining propagation potential energy and a space-time diagram convolutional network is adopted, on the basis of considering factors such as environmental conditions, disease time sequence evolution and spatial neighborhood influence, a disease propagation path is dynamically simulated, future risks are quantified, and a visual risk prediction map and a propagation vector diagram are generated, so that agricultural management is assisted to be optimized.

The present application discloses an agricultural irrigation method and system, a device, a medium, and a product. The method comprises: acquiring a farmland condition dataset of an area to be irrigated; on the basis of a comparison result between minimum soil moisture content data and farmland water-holding capacity data, determining whether to generate a first irrigation instruction, the first irrigation instruction being used for controlling on of a first solenoid valve and a first water pump which are located on a first pipe, and watering a farmland on the basis of the first irrigation instruction; on the basis of a comparison result between soil nutrition data and preset nutrition data, determining whether to generate a second irrigation instruction, the second irrigation instruction being used for controlling on of a second solenoid valve and a second water pump which are located on a second pipe, and fertilizing the farmland on the basis of the second irrigation instruction; and on the basis of a level of an expected crop disease index, determining whether to generate a third irrigation instruction, and applying pesticides to the farmland on the basis of the third irrigation instruction.

The invention relates to the technical field of facility agriculture intelligent monitoring, and discloses a crop disease and insect pest internet-of-things image recognition method and system, and the method comprises the steps: carrying out the data collection and preprocessing, and building a crop growth file; performing illumination characteristic analysis and adaptive enhancement on the original image; performing quality evaluation on the image subjected to quality optimization, calculating a comprehensive quality score and performing screening; a crop growth stage is identified, and multi-scale features are extracted; constructing an agricultural pest knowledge base and estimating a pest prior probability, projecting the multi-scale feature vector to a discrimination subspace and performing weighted fusion according to the prior probability; carrying out double weighting on the environment adaptive fusion feature vector, and carrying out pest and disease identification through a hierarchical classification strategy; performing multi-source evidence fusion to obtain a final recognition result, and generating prevention and treatment suggestions in combination with an agricultural pest knowledge base; the accuracy and environmental adaptability of pest and disease identification are improved.

The invention belongs to the technical field of organic synthetic chemistry and pesticide science, and particularly relates to an indole-containing amide derivative as well as a preparation method and application thereof, and the indole-containing amide derivative has a structure as shown in a formula I or a formula II, through an active substructure splicing strategy, indole is used as a parent skeleton, amido bonds are ingeniously introduced for structural diversity optimization and modification, active functional groups such as methoxyamine are further introduced, and a series of indole amide derivatives with novel structures are designed and synthesized. Through determination, the screened series of compounds show good antibacterial activity, and a plurality of compounds show excellent prevention and treatment effects on various important crop diseases.

The invention discloses a preparation method and application of an anti-continuous cropping microbial fertilizer, and aims to solve the problems of aggravation of crop diseases and insect pests and reduction of yield and quality caused by continuous cropping planting. The preparation method comprises the following steps: activating and uniformly mixing bacillus subtilis and bacillus licheniformis, and adding an alpha-lactalbumin solution and a calcium chloride solution to prepare a bacillus aggregate; adding bentonite into the polytrehalose solution, and performing ultrasonic treatment on the mixture and the humic acid solution to prepare a modified polytrehalose-bentonite compound; uniformly mixing acremonium terricola, trichoderma harzianum and a bacillus aggregate, and adding a wall material solution to prepare a compound bacterium microcapsule preparation; and finally, mixing the microcapsules with the corn straw biochar, the chitosan oligosaccharide and the like to obtain a finished product. Through calcium ion induction and humic acid modified embedding, the strain stability and viable count are improved, soil organic matter and nitrogen phosphorus and potassium nutrients can be activated, pathogenic bacteria are inhibited, the crop rhizosphere environment is improved, the growth index and quality of red kidney beans and potatoes are effectively improved, and the method is suitable for continuous cropping resistant planting of crops.

The invention provides a crop pest risk assessment and early warning method fusing spectrum and structural features, and relates to the technical field of agricultural remote sensing monitoring and intelligent early warning, the method comprises the following steps: obtaining environmental factor data, multispectral image data and laser radar point cloud data; performing geometric correction, radiation correction and orthographic splicing processing on the multispectral image data to obtain a multispectral orthoimage; performing filtering and three-dimensional reconstruction on the laser radar point cloud data to generate a canopy height model; performing spatial registration and resolution unification on the multispectral orthoimage and the canopy height model to obtain a multi-source fusion data set; and based on the multi-source fusion data set, extracting a disease spectral index DSI under a micro scale and a canopy structure anomaly index CSAI under a macro scale. According to the invention, multi-scale dynamic monitoring, quantitative risk assessment and regionalized graded early warning of diseases and insect pests can be realized, and the monitoring precision and timeliness are improved.

The invention relates to the field of agricultural biotechnology and plant protection, and particularly provides application of 2-heptanone to preparation of a preparation for preventing and treating plant diseases. The bactericidal composition has a strong bacteriostatic effect on litchi colletotrichum gloeosporioides, strawberry colletotrichum gloeosporioides, mango leaf blight, pawpaw colletotrichum gloeosporioides, wheat gibberellic disease, mango colletotrichum gloeosporioides, tomato fusarium wilt, banana fusarium wilt, banana colletotrichum gloeosporioides and corn root rot. The fumigant developed on the basis of 2-heptanone and used for preventing and treating phytopathogen is expected to be used for preventing and treating compound diseases caused by various pathogenic bacteria, or comprehensively preventing and controlling different crop diseases in a crop rotation system, and has important development value.

The application discloses a kind of crop disease multi-modal diagnosis and classification method and system, it is related to data processing technical field, the method of the present application includes collecting disease multi-modal data and pre-processing;Disease multi-modal diagnosis model is constructed, the image and text sequence in multi-modal data are respectively input into the visual feature extraction subnet and text feature coding subnet of disease multi-modal diagnosis model, extract global visual feature, sequence state feature and global text feature;Based on global visual feature and global text feature, calculate global matching score, perform fine-grained local interaction on global visual feature and sequence state feature, and introduce class channel attention mechanism to correct the features obtained by interaction, and generate multi-modal fusion features by weighting;Based on global visual feature, global text feature and multi-modal fusion feature, construct loss function and train disease multi-modal diagnosis model, input the data to be classified into disease multi-modal diagnosis model and output classification results.

The invention discloses a crop disease and pest control pesticide spraying method and system based on image recognition, and the method comprises the steps: collecting crop images through carrying multiple cameras by an unmanned aerial vehicle, inputting the crop images into a double-branch deep learning model after preprocessing, and enabling the model to comprise an FCA frequency domain feature extraction network, a DAT-Transform spatial domain feature extraction network and an MSAF fusion module, precise identification of diseases and pests is realized; further integrating real-time environment parameters such as temperature and humidity, wind speed and crop growth stages, constructing a dynamic pesticide amount calculation model, and outputting personalized pesticide spraying amount; the pesticide spraying mechanism is driven by the controller to execute pesticide spraying, effect data is collected after a prevention and treatment period, an optimization recognition model and pesticide amount calculation parameters are fed back, and a recognition-pesticide spraying-feedback-iteration closed-loop prevention and treatment system is formed. The method solves the problems of low recognition precision, rigid pesticide amount regulation and control and lack of a continuous optimization mechanism in a complex environment in the prior art, improves the recognition precision, saves the pesticide amount, and improves the control effect.

The application provides a crop disease precision identification method and system, and relates to the technical field of crop disease identification. The method comprises the following steps: establishing standard lesion feature image datasets of different crops and different organs; constructing a background feature database of common crop diseases; forming a crop type and organ identification algorithm model and an organ lesion identification algorithm model; determining the crop type and the organ of the crop in an image to be identified based on the crop type and organ identification algorithm model; determining the disease range of the image to be identified based on the organ of the crop and using the organ lesion identification algorithm model corresponding to the organ of the crop, and outputting a lesion disease with similar features; outputting a matched crop disease type based on the background feature database of common crop diseases; and obtaining a common disease type in the determined lesion disease and the determined crop disease type, so as to realize the precision identification of the disease. The application classifies and grades to form a structured disease identification process, and improves the identification precision.

The present application relates to the application of allantoin in the prevention and treatment of fungal diseases of grain crops. The present application finds that allantoin can inhibit the growth of pathogenic fungi of grain crops, especially inhibit the infection of pathogenic fungi of grain crops to host cells or inhibit the expansion of pathogenic fungi of grain crops in host cells, thus indicating that allantoin can be used as a pesticide to prevent and treat pathogenic fungi of grain crops, especially Magnaporthe oryzae, Cochliobolus miyabeanus and Ustilaginoidea virens.

The present application relates to the technical field of image processing, in particular to a kind of agricultural disease detection method based on multi-scale feature extraction, comprising: obtaining target disease image, according to the mode of ladder convolution processing, the hierarchical branch of target disease image is constructed, under any hierarchical branch, with the mode of each level series coupling, determine the local disease feature of target disease image;Local disease feature is extracted by channel attention, and the local disease feature after attention recalibration is obtained;Local disease feature is mapped into semantic sequence space, and local disease feature is spatially attenuated and weighted to determine the disease distribution characteristics of crop disease under multi-scale fusion;At least one disease category is obtained based on the spatial position corresponding to disease distribution characteristics, and the average time and average accuracy of disease detection are combined to determine the disease detection effect.The precision and efficiency of disease identification are improved.

The present application relates to the field of wisdom agriculture and image recognition technology, specifically to a crop disease and pest monitoring system based on image recognition, comprising: an environment perception module, which obtains the environmental context parameters of the monitoring area through multi-modal sensor nodes; a multi-source context analysis module, which constructs a disease and pest outbreak risk model in combination with historical disease and pest distribution spatio-temporal data and calculates the disease and pest risk level; an adaptive strategy generation module, which dynamically reconstructs the image acquisition mode and feature extraction strategy according to the risk level and the edge side resource constraint state, and generates adaptive perception instructions; an image acquisition execution module, which adjusts the physical acquisition parameters and algorithm calculation density of the visual sensor in response to the instructions, and executes image acquisition and recognition; the present application breaks the traditional rigid acquisition mode and realizes the optimal balance between monitoring efficiency and device survival period.

The present application belongs to the technical field of pattern recognition and intelligent agriculture, and particularly relates to a crop disease and pest intelligent identification and crop condition perception method. The method first collects crop environment and image data through a multi-source perception network, extracts features using a physical information neural network, and introduces a physiological constraint operator containing a transpiration model and a chlorophyll fluorescence decay parameter therein. At the same time, micro-meteorological parameters are injected into the network as weight correction factors to establish the coupling relationship between environmental factors and physiological characteristics, so as to realize decoupling identification of physiological adversity and pathological infection. Finally, state evaluation is carried out in combination with a physiological adversity formula, and the identification result and crop condition report are output. The present application uses physical laws to decouple the identification logic, accurately distinguishes adversity and infection with similar visual forms, reduces the dependence on sample size, and improves the accuracy and robustness of crop condition monitoring in complex environments.