825 results about "Crop disease" patented technology

The invention relates to the field of computer vision, in particular to a crop disease and insect pest real-time identification, prevention and control decision-making method and system based on multi-modal edge calculation. Comprising the following steps: acquiring an unmanned aerial vehicle multispectral image, an infrared thermal image and an NDVI index, performing preprocessing in combination with field Internet of Things sensing data, and generating a multi-modal feature input sequence; constructing a spectrum-environment fusion feature matrix on the basis of a Transform attention mechanism, and performing disease and pest detection by using a YOLOv7-Spectral model to generate a disease and pest distribution heat map; calculating a disease risk index (BRI) and a pesticide application priority map based on historical data; planning a pesticide application path of the unmanned aerial vehicle by adopting an ant colony optimization algorithm, and optimizing a pesticide application scheme in combination with wind speed and humidity parameters; the unmanned aerial vehicle performs precise pesticide application according to the optimized path and monitors the disease change trend; adjusting the model through pesticide application feedback data, and optimizing a disease and pest prevention and control strategy by adopting federal learning. According to the invention, the identification precision is improved, the pesticide use is reduced, and accurate, efficient and intelligent prevention and control are realized.

The invention relates to the technical field of crop disease recognition, in particular to a crop early disease recognition method and system based on time sequence feature fusion, and the method comprises the steps: obtaining an RGB image and a temperature feature image of a target leaf, and collecting multispectral feature data through a spectral imaging device; based on the time sequence image sequence, generating a coordinate transformation matrix of the first frame image by using a PnP algorithm, and aligning subsequent images according to the coordinate transformation matrix; multi-modal features are extracted by using a deep neural network, and the deep neural network comprises a 3D convolutional layer and an adaptive attention mechanism and is used for extracting features of space and time dimensions; realizing cross-modal feature fusion through a graph convolutional network based on the spectral data and the multi-modal features; inputting the fused features into a classifier, and generating the disease category and the disease severity of the target leaf based on the output of the classifier; outputting the disease transmission risk and the early warning result, and capturing the tiny dynamic change in the disease development process.

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 provides an agricultural pest early warning method and system based on big data, and the method comprises the steps: firstly obtaining a crop leaf image set of a target farmland region from farmland image big data, and carrying out the leaf region segmentation of the crop leaf image set, so as to distinguish a healthy region from a potential lesion region; performing disease feature extraction on the potential lesion area image to generate a key disease feature set, performing abnormal state detection on the key disease feature set by using a pre-trained disease and pest recognition model, determining a disease and pest type and predicting a diffusion trend of the disease and pest type; and finally, based on the disease and insect pest type identification and the diffusion trend prediction data, generating a disease and insect pest early warning instruction containing geographic positioning information, and sending the instruction to a farmland management system to trigger prevention and control response operation, thereby realizing accurate monitoring and early warning of crop diseases and insect pests.

The invention discloses a crop disease remote sensing monitoring and early warning system based on time sequence spectral information analysis, and relates to the technical field of agricultural disease and insect pest monitoring. Computer drawing software is utilized to divide a corn planting field in equal proportion, and sensors are deployed to monitor the growth environment change in each grid in real time and generate microenvironment data information; the method comprises the following steps: calculating a microenvironment difference index Xbd according to microenvironment data, identifying a microenvironment difference aggregation area and sending a risk analysis instruction, after receiving the risk analysis instruction, capturing related spectral data information of the microenvironment difference aggregation area by using an unmanned aerial vehicle and a multispectral sensor, and constructing a multispectral fluctuation coefficient Xgp by analyzing related spectral data, and comparing the calculated rust disease risk assessment index Zpg with a preset threshold W in combination with the microenvironment difference index Xbd and the trained risk assessment model, and obtaining corresponding grade early warning according to the rust disease risk degree.

The invention relates to the technical field of environmental data monitoring, and discloses a crop disease and insect pest monitoring and early warning system based on satellite remote sensing and an AI algorithm, and the system comprises the following steps: regularly collecting a farmland multispectral image covering a farmland area through a satellite remote sensing technology, and carrying out the preprocessing of the farmland multispectral image, and obtaining a farmland three-dimensional space image; performing pest and disease analysis on the farmland three-dimensional space image to extract a pest and disease image and spatial distribution thereof, analyzing historical pest and disease data based on an AI algorithm analysis model, obtaining pest-inducing influence factors corresponding to different target types of pests, and obtaining a pest and disease development trend corresponding to the target types of pests based on the pest-inducing influence factors; and compiling an intelligent contract for the disease and pest influence degree corresponding to the current disease and pest image, recording the timestamp of the disease and pest influence degree in the intelligent contract, and establishing a disease and pest early warning strategy based on evaluation reports of different time periods.

The embodiment of the invention provides a rice blast disease grade intelligent evaluation and disease resistance preliminary screening method, system and equipment, and the method comprises the steps: S1, collecting image data of wild rice leaves infected with rice blast, carrying out the instance segmentation of the image data through an improved YOLOv8-Seg segmentation model, obtaining a segmentation result, and carrying out the segmentation of the image data through an improved YOLOv8-Seg segmentation model; the improvement measures comprise a GhostHierachicalNet backbone network, a CAHSFPN neck network and an LSDES Head segmentation head, and the LSDES Head segmentation head is connected with the CAHSFPN neck network and the LSDES Head segmentation head; s2, according to the segmentation result, counting the number and proportion of pixels of diseased leaves and disease areas, and obtaining a disease grade evaluation result in combination with disease resistance identification technical regulations; and S3, screening wild rice materials with strong disease resistance based on the disease grade evaluation results which are repeated for multiple times. The method can effectively improve the accuracy and efficiency of crop disease evaluation, and provides support for large-scale screening of disease resistance of wild rice, thereby promoting the development of rice blast-resistant breeding work.

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 provides a crop disease and pest recognition and pathology follow-up method and device and a medium, and relates to the technical field of crop disease and pest recognition. Coding the diagnosis hypothesis in the text format into vector representation of a fixed dimension; configuring comprehensive feature representation; through a multi-stage strategy, a soft label is generated by the teacher model, and the soft label is used as supervision information to guide the student model; model check points are saved in the training process, and student model performance is evaluated by using the verification set; predicting development condition parameters of various diseases; simulating the diffusion process of the disease in the space grid through convolution operation to obtain a propagation probability graph in the future N days; a disease progress prediction result generated in combination with the disease propagation condition is used for evaluating the development trend of crop diseases and insect pests. According to the invention, detection and diagnosis of crop diseases and insect pests are realized, the propagation path of the diseases and insect pests can be traced, and root blocking measures are recommended.

The invention relates to the technical field of information, in particular to an intelligent agricultural information big data intelligent acquisition management system and method. The method comprises the following steps: acquiring farm sensing data, and performing crop growth analysis according to the farm sensing data so as to obtain crop growth data; performing crop health state feature extraction and crop physiological feature extraction according to the crop growth data so as to obtain crop health state data and crop physiological data; performing disease and pest detection according to the crop health state data and the crop physiological data to obtain crop disease and pest data; the method comprises the following steps: acquiring farm remote sensing data, performing crop multispectral analysis according to the farm remote sensing data so as to obtain crop multispectral data, and performing crop moisture state evaluation according to the crop multispectral data so as to obtain crop moisture state data. The pesticide spraying strategy is optimized based on the information technology, and the agricultural production management efficiency is improved.

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 belongs to the technical field of crop disease and insect pest control, and particularly relates to an agricultural disease and insect pest control method fusing computer vision and a language model. Based on a vision model and an optimized language model, after the vision model and the optimized language model are integrated, dynamic scene information can be combined; the method can identify various crop diseases in real time and give more professional and detailed suggestions in the field of crop disease control, has good disease identification ability and control suggestion generation ability, further significantly improves the efficiency of disease control in agricultural production, reduces the labor cost, and is beneficial to improving the yield and quality of crops.

The invention provides Yunnan golden camellia root endophytic fungi and application thereof, and relates to the technical field of crop disease control. The Yunnan camellia nitidissima root endophytic fungus is Lecanicillium vase JHC098 and is preserved in the China Center for Type Culture Collection, the preservation number is CCTCC NO: M 2025749, and a liquid fermentation liquor crude extract of the Lecanicillium vase can be applied to prevention and treatment of crop diseases. The invention overcomes the defects in the prior art, and the crude extract of the liquid fermentation liquor of the endophytic fungus Lecanicillium viculare JHC098 at the root of Yunnan golden camellia can effectively inhibit the growth of pathogenic bacteria such as citrus canker and tobacco bacterial wilt, thereby achieving the purpose of certain biological control.

The invention discloses a self-adaptive sampling and hyper-space attention apricot tree disease detection model, and belongs to the technical field of crop disease and insect pest protection, and the model comprises the steps: S1, obtaining a large number of apricot tree disease images; s2, performing data annotation on the acquired apricot tree disease image; s3, preprocessing the marked data, and then processing the data by adopting four data enhancement methods of Cutout, Cutmix, Mosaic and copy enhancement; s4, performing feature extraction on the image through a multi-modal large model architecture, then performing processing by using a spatial state attention mechanism and a dynamic latent variable network model, and finally training the model by using a Focal loss function; s5, carrying out model lightweight design through network pruning and knowledge distillation technologies; according to the adaptive sampling and hyper-space attention apricot tree disease detection model provided by the invention, efficient and accurate disease detection is realized, and the model is of great significance to improvement of agricultural production efficiency and guarantee of food safety.

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 relates to the field of agriculture, in particular to a crop disease and pest recognition method and system, and the method comprises the steps: obtaining an image of a crop, carrying out the preprocessing of the image, extracting features, obtaining a multi-scale feature map, and enabling the multi-scale feature map to serve as the input of an encoder; determining the position of a reference point in each feature graph input by each layer of the encoder, for each deformable attention head, inputting the feature of the reference point into a linear layer to obtain the offset of a sampling point, taking the sampling point and the reference point as the nodes of the graph, determining the weight of an edge based on the offset of the sampling point and the reference point, carrying out one-time graph convolution on the graph, and carrying out one-time graph convolution; obtaining a reference point feature corresponding to the deformable attention head based on a result of the graph convolution; and inputting the characteristic pattern output by the encoder into a decoder, and obtaining a crop disease and insect pest identification result by using a query vector of the decoder.

The invention discloses a crop disease and pest identification method and system based on multi-task learning. The method comprises the following steps: firstly, constructing a crop disease and pest identification model comprising a multi-scale feature information fusion module, a disease detection branch and a severity classification branch; the method comprises the following steps: preprocessing an input leaf image, and extracting and fusing a multi-scale feature map by a multi-scale feature information fusion module; the disease detection branch generates disease and insect pest candidate regions by using a region proposal network, and outputs disease and insect pest positions and categories through disease detection in combination with a multi-scale fusion feature map; meanwhile, the fusion feature map with the maximum size is input into a severity classification branch to realize four-stage evaluation; a weighted loss function design thought is provided, and multi-task network branches can be guided to carry out joint training. According to the method, end-to-end multi-task cooperative processing is realized, disease and pest positioning, classification and severity evaluation are synchronously completed by sharing a multi-scale fusion feature map, the recognition efficiency is greatly improved, and the real-time monitoring requirement of an agricultural scene is met.

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 provides a staple food grain crop disease grading method and system based on a prompt-driven large model, and the method comprises the following steps: collecting and preprocessing a main food crop image, and obtaining a main food crop data set; training a ResNet-18 network based on the main grain crop data set to perform initial disease grading, and obtaining disease characterization prompt information; utilizing disease characterization prompt information to drive a prompt drive large model, and segmenting a focus area and a leaf area of the to-be-graded main grain crop image; and calculating the proportion of the lesion area in the leaf area, obtaining a final fine-grained disease grading result of the to-be-graded main grain crop image, and completing staple food crop disease grading based on the prompt-driven large model. According to the method, automatic detection and grading of crop diseases are realized by integrating deep learning and an interactive segmentation technology, so that the accuracy of disease detection and classification is improved, possibility is provided for optimizing the use of pesticides, and environmental pollution and the risk of crop drug resistance are reduced.

The invention discloses a crop leaf disease diagnosis method and system driven by a large language model. Comprising the following steps: firstly, extracting a leaf disease image of a target crop and corresponding text semantic information from a basic crop management file so as to obtain an initial crop disease knowledge database; then, the initial crop disease knowledge database is optimized and updated in combination with visual understanding of a large language model; and then, constructing a crop disease classification agent model, inputting a to-be-diagnosed leaf disease image into the crop disease classification agent model, performing crop disease diagnosis by the crop disease classification agent model according to the crop disease knowledge database, and outputting a disease identification result. Under the condition that large-scale image training is not carried out, disease diagnosis can be accurately carried out on the crop disease images, disease image diagnosis requirements of different crops can be met, and the problem that existing work is insufficient in potential in the aspects of zero sample application and expandability is solved.

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 discloses arginine methyltransferase protein from phytophthora plant pathogenic oomycetes as well as a coding gene and application thereof. The arginine methyltransferase protein provided by the invention is as shown in sequences 6-8; the coding gene is shown in sequences 1-3. Experiments prove that the protein disclosed by the invention plays an important role in growth, development and pathopoiesis processes of typical phytophthora plant pathogen oomycete phytophthora capsici, and after the protein is deleted or the expression level is reduced, the growth rate of phytophthora capsici hyphae is reduced, the sporangium yield is reduced, the number of zoospore is reduced, the pathogenicity is reduced and the like. Corresponding arginine methyltransferase inhibitors have different degrees of activity on typical plant pathogenic oomycetes such as phytophthora capsici, phytophthora sojae and phytophthora infestans. The gene provided by the invention has great application potential in the aspect of preventing and controlling the crop epidemic diseases caused by phytophthora plant pathogenic oomycetes, and a novel bactericide developed based on the protein as an action target has important practical significance in controlling the occurrence and prevalence of the crop epidemic diseases caused by phytophthora.

The invention relates to the technical field of crop disease and insect pest monitoring, and particularly provides a crop disease and insect pest intelligent monitoring system based on image recognition and machine learning, and the system comprises a reference model generation subsystem which obtains a continuous state flow in a crop growth period through a multispectral imaging array; performing micro-variation field comparison on the real-time image of the continuous state flow and the ideal form reference vector, and outputting a biological stress difference chart; the mutual exclusion association engine subsystem purifies a pathological response map, inputs the pathological response map into a pathological characteristic dissociation network, and outputs an environmental immune type disease mark set; and the disease source trajectory deduction subsystem diffuses a power chain through organisms on the basis of the initial infection focus coordinates in combination with a real-time wind direction vector field and a plant density topological graph. According to the method, the problem of high false positive rate in a complex farmland scene is solved through a dual denoising mechanism of growth rhythm decoupling and environmental interference stripping; and finally, monitoring dimension jump from single-point identification to group prevention and control is realized.

The invention discloses application of a ZmFS1 gene in corn disease prevention and control, and belongs to the technical field of crop disease prevention and control. The nucleotide sequence of cDNA of the ZmFS1 gene is as shown in SEQ ID NO.1. By inhibiting expression of the gene, resistance of corn to various diseases including southern leaf blight, northern leaf blight, southern rust disease, anthracnose, stem rot, seed rot and the like can be remarkably enhanced, a new solution thought is provided for preventing and treating corn diseases from the perspective of molecular biology, and the application prospect is broad. The method can be applied to corn disease-resistant breeding.

The invention provides a bacillus velezensis strain and application thereof. The name of the bacillus velezensis is BJ-1141, and the bacillus velezensis is preserved in the China General Microbiological Culture Collection Center (CGMCC), and the preservation number is CGMCC No.32094. The strain has broad-spectrum antibacterial activity, and has a good inhibition effect on 24 plant pathogenic fungi such as rice pathogenic bacteria, wheat pathogenic bacteria, corn pathogenic bacteria, grape pathogenic bacteria, cherry pathogenic bacteria, solanaceous crop pathogenic bacteria and the like. Wherein the growth inhibition effect on sclerotinia sclerotiorum hyphae causing eggplant sclerotinia rot is optimal, and the inhibition rate can reach 95.57%. The BJ-1141 bacterial liquid has an obvious growth promoting effect on the growth of plants such as tomatoes and lettuce. After the BJ-1141 bacterial liquid is used for irrigating roots of potted tomatoes, the tomato fusarium wilt caused by tomato fusarium oxysporum can be effectively controlled, the BJ-1141 bacterial liquid has a good prevention and treatment effect, the prevention and treatment effect can reach 85.93%, and the prevention and treatment effect is obviously different from that of a control group.

The invention discloses a biocontrol complex microbial inoculant for preventing and treating maize curvularia leaf spot and application thereof, and belongs to the field of crop disease prevention and treatment. The biocontrol complex microbial inoculant comprises the following active ingredients: bacillus cereus, pseudomonas chlororaphis and streptomyces microflavus, and / or metabolites thereof. The beneficial microorganisms with different action mechanisms are combined, and the prevention and control effect on the curvularia maize leaf spot disease is enhanced through the synergistic effect. Natural microbial resources are adopted to replace traditional chemical pesticides, harmful substance residues are reduced, and the ecological environment is protected. Through reasonable matching of different types of beneficial microorganisms, effective prevention and control of the curvularia maize leaf spot disease are realized, and healthy growth of plants can be promoted. Compared with a single strain preparation, the complex microbial inoculant provided by the invention has stronger stability and durability, and can significantly reduce the incidence rate of diseases.

The invention relates to the technical field of crop disease and insect pest image recognition, and particularly discloses a crop disease and insect pest image recognition method based on a large model, and the method comprises the steps: obtaining a multi-angle leaf image through high-resolution imaging equipment under a controllable illumination condition, and obtaining a target image in a unified format; extracting scab texture complexity features in combination with a local binary pattern and a gray-level co-occurrence matrix algorithm, and performing multi-channel statistical analysis on RGB and HSV color spaces to generate color heterogeneity feature vectors; further fusing the two types of features into a composite disease feature vector, inputting the composite disease feature vector into a probability model constructed based on a support vector machine and a Monte Carlo Dropout mechanism, and outputting probability distribution and confidence score of disease and pest categories; and dynamically adjusting a model training strategy according to a confidence level, triggering a feedback mechanism for a low-confidence sample, generating a synthetic image by using a conditional generative adversarial network, and optimizing model parameters in combination with incremental learning to realize stable identification modeling of rare or complex disease types.

The invention discloses a ground-air integrated agricultural detection system and method, and relates to the technical field of agricultural detection. Farmland multi-dimensional data are collected through the ground-air sensor, and the farmland environment is comprehensively monitored. Based on the soil moisture dynamic change and the crop root activity data, obtaining a root moisture utilization efficiency index; identifying crop disease symptoms and pest population distribution, predicting a pest diffusion trend, and obtaining a pest propagation risk index; the soil erosion risk index is obtained by analyzing the soil particle loss, the surface runoff intensity and the vegetation coverage change. The early warning module comprehensively evaluates the root system moisture utilization efficiency index, the disease and pest propagation risk index and the soil erosion risk index to obtain a farmland detection index, an early warning mechanism is set, and early warning information is generated when the farmland detection index triggers an early warning condition. Farmland conditions can be monitored in real time, a scientific management basis is provided, the intelligent level of agricultural management is improved, and resource waste and crop loss are reduced.

The invention provides a disease knowledge question-answering method and system, electronic equipment and a storage medium. The method comprises the following steps: acquiring disease query information corresponding to a target crop; according to the similarity between the disease query information and each entity in the disease knowledge graph, obtaining a to-be-queried entity set associated with the disease query information; performing entity relationship reasoning on the disease knowledge graph according to the to-be-queried entity set to obtain target reasoning link information between the to-be-queried entity set and the target entity set; the target entity set is a set formed based on entities having a connection relationship with each to-be-checked entity in the to-be-checked entity set; and according to the target reasoning link information, deciding and outputting a disease knowledge reasoning answer corresponding to the disease query information. According to the method, the large model illusion phenomenon is relieved, and meanwhile, the reasoning ability and the logic judgment ability in a complex knowledge question-answering scene are effectively improved, so that the complex crop disease knowledge question-answering scene can be dealt with more accurately.

The invention relates to the technical field of computer vision, in particular to a light-weight crop image super-resolution and disease identification method and a light-weight crop image super-resolution and disease identification system. Comprising the steps of extracting crop disease images in a database as a data set; preprocessing the data set, and training a disease recognition model according to a deep convolutional neural network; then training an image super-division model; distilling the image super-division model to obtain a lightweight student model; obtaining a target crop image and carrying out image preprocessing; and carrying out image super-division on the target crop image, and completing disease identification of the target crop based on the disease identification model. According to the invention, the generative adversarial network and the model distillation means are constructed in a combined manner, the generator and the discriminator are used for continuous adversarial learning, and the model distillation is used for transferring knowledge from a large-scale and complex teacher model to a small-scale and efficient student model; therefore, the number of parameters and calculation requirements of the model are significantly reduced while the image enhancement performance is maintained.