649 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 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 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 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 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 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 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 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 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 artificial intelligence, in particular to an intelligent diagnosis system for crop diseases and insect pests and a self-evolution method of the intelligent diagnosis system. The diagnosis system extracts features from a disease and insect pest image and a description text of a user for disease and insect pests, performs multi-modal fusion, generates a diagnosis report by using the fused features, and further comprises a gating network and a LoRA adapter which is specific for various disease and insect pest tags. And the gating network selects a corresponding LoRA adapter according to the description text of the plant diseases and insect pests, the corresponding LoRA adapter is dynamically injected into a Transform layer of the basic model, a reasoning path of the LoRA adapter is temporarily reconstructed, and the model with updated parameters is utilized to generate a diagnosis text. According to the method, the most matched expert capability can be loaded in time, so that the adaptability and the diagnosis accuracy of complex and changeable diagnosis scenes are remarkably improved.

The invention relates to the technical field of agriculture, in particular to a large-model-driven agricultural knowledge graph analysis method and system, and aims to realize standardized processing of multi-source heterogeneous data through a three-stage preprocessing process and combine with a field adaptive large-model training technology so as to realize the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis method and the large-model-driven agricultural knowledge graph analysis system. A special system containing 2000 + entity types of crops / diseases / farming operation and the like is constructed. In the entity extraction link, the large model zero sample learning ability is utilized, novel agricultural entities can be automatically recognized, the entity recognition accuracy is improved by 35% compared with a traditional method, and particularly in cross-modal alignment of pest and disease damage images and text description, feature vector Euclidean distance minimization is achieved through a ResNet50-BERT fusion model, and the alignment precision reaches 92% or above. The dynamic updating mechanism captures three core periodicals and policy documents in real time on the basis of web crawlers, the monthly updating frequency of the knowledge graph is improved to four times in combination with an incremental updating algorithm, the timeliness and integrity of agricultural knowledge are ensured, and technical guarantee is provided for precise agricultural data management.

The invention relates to the technical field of pest control, in particular to a pest outbreak process mining risk prediction method and system based on multi-source factors. The method comprises the following steps: measuring regional meteorological data, regional crop soil fertility degree and regional disease and pest occurrence data corresponding to crop disease and pest monitoring points in real time; the corresponding pesticide spraying usage amount is obtained through the crop disease and insect pest monitoring points, and regional occurrence density accounting and disease and insect pest outbreak mining analysis are carried out on regional disease and insect pest occurrence data based on the pesticide spraying usage amount. Obtaining a corresponding regional pest and disease outbreak characteristic mode under the meteorological condition and a correlation characteristic relation between regional soil fertility and pest and disease outbreak; and performing multi-source factor risk prediction and control decision support analysis on the crop disease and pest monitoring points to generate a disease and pest outbreak area control suggestion scheme. According to the invention, scientific and accurate disease and pest risk early warning can be provided for agricultural production.

The invention belongs to the technical field of microorganisms, and particularly relates to a Burkholderia sp. SJ08 strain, a Burkholderia sp. SJ08 fungicide, and a preparation method and application thereof. The invention discloses a strain of Burkholderia cepacia SJ08, the Burkholderia cepacia SJ08 is preserved in the Guangdong Province Microbial Culture Collection Center, the preservation number is GDMCC (China General Microbiological Culture Collection Center): 65908, and the preservation date is February 18, 2025. The Burkholderia sp. SJ08 strain is screened from seed radix pseudostellariae, crop diseases caused by fusarium oxysporum can be effectively inhibited, continuous cropping obstacles of crops can be inhibited, crop growth is promoted, and the Burkholderia sp. SJ08 strain is safe and free of pollution. The Burkholderia sp. SJ08 strain provided by the invention has a good application prospect in crop production.

The present invention relates to the field of computer vision, specifically a method and system for real-time identification and control of crop pests and diseases based on multimodal edge computing. The system comprises: acquiring drone multispectral imagery, infrared thermal imaging, and the NDVI index, preprocessing them in combination with field IoT sensor data to generate a multimodal feature input sequence; constructing a spectral-environmental fusion feature matrix based on the Transformer attention mechanism, and utilizing the YOLOv7-Spectral model for pest and disease detection to generate a pest and disease distribution heat map; calculating the pest risk index (BRI) and pesticide application priority map based on historical data; employing an ant colony optimization algorithm to plan drone application paths and optimizing the application plan based on wind speed and humidity parameters; accurately applying pesticides along the optimized paths by the drones and monitoring disease trends; adjusting the model based on application feedback data, and optimizing pest and disease control strategies using federated learning. The present invention improves recognition accuracy, reduces pesticide use, and achieves precise, efficient, and intelligent control.

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 a control method for apple alternaria leaf spot, and belongs to the technical field of crop disease control. The prevention and treatment method comprises the following steps: spraying a foliar biological pesticide fertilizer to apple trees; the foliar biological medical fertilizer comprises a bacillus fermentation product and a trichoderma fermentation product, the bacillus is at least one of bacillus amyloliquefaciens TS-1203, bacillus subtilis B1 and bacillus subtilis BS-1208, and the bacillus is at least one of bacillus amyloliquefaciens TS-1203, bacillus subtilis B1 and bacillus subtilis BS-1208; the trichoderma is at least one of T6 and Ym. The combined action of bacillus and trichoderma is utilized, the morbidity of apple alternaria leaf spot can be effectively reduced, the growth of apple trees can be promoted, the photosynthetic capacity is improved, the combination of disease prevention and yield increase is realized, and the method is environment-friendly and suitable for green agricultural development.

The invention discloses an African trichoderma harzianum strain 6-5 and application thereof, the disclosed strain is named as Trichoderma harzianum, and the preservation number of the strain is CGMCC (China General Microbiological Culture Collection Center) No. 41930. The bacterial strain has obvious bacteriostatic activity on crop pathogenic bacteria such as colletotrichum capsici, apple cyst, botrytis cinerea, alternaria longibrachiata and phytophthora capsici, and fermentation liquor of the bacterial strain has good prevention and control effects on apple anthracnose leaf blight, apple bitter rot and apple tree canker.

The invention belongs to the technical field of biological prevention and control, and particularly relates to a Burkholderia paragallinarum D15 and application of the Burkholderia paragallinarum D15. The Burkholderia parakholderia sp. D15 is preserved in China General Microbiological Culture Collection Center (CGMCC) on November 25, 2024, the address is No.3, No.1 yard, Beichen West Road, Chaoyang District, Beijing, and the preservation number is CGMCC NO. 1.61962. The Burkholderia parakholderia sp. D15 has the advantages that the Burkholderia parakholderia sp. According to the invention, a multifunctional strain Burkholderia paragallinarum D15 is separated from the rock wall of Danxia red rock, and the Burkholderia paragallinarum D15 has a relatively good antagonistic effect on most plant pathogens and has a relatively strong bacteriostatic effect on xanthomonas oryzae Xoo causing rice bacterial blight; the compound also has a relatively strong bacteriostatic effect on other pathogenic bacteria causing crop diseases, such as rice sheath blight disease, rice blast, fusarium graminearum, sclerotium rolfsii and ralstonia solanacearum, and lays a foundation for prevention and treatment of rice bacterial blight and development of related biological preparations.

The invention relates to application of an overexpressed soybean GmMYB306 gene in preparation of a product for improving resistance of crops to soybean phytophthora root rot, and belongs to the technical field of bioengineering. The invention provides application of an overexpressed soybean GmMYB306 gene in preparation of a product for improving the resistance of crops to soybean phytophthora root rot. The nucleotide sequence of the soybean GmMYB306 gene is shown as SEQ ID NO. 1. The soybean GmMYB306 protein disclosed by the invention can interact with a disease-resistant related protein GmNPR1, and the defense capability of soybean plants to phytophthora sojae is improved by regulating and controlling the expression of a crop disease course related gene GmPR1, so that the soybean GmMYB306 protein plays an important role in improving the resistance of the soybean plants to phytophthora sojae root rot.

The invention is suitable for the technical field of agricultural bactericides, and provides application of an echinocandin antifungal drug in prevention and treatment of agricultural diseases. According to the present invention, the two common echinocandin antifungal drugs such as the anidulafungin and the Rezafungin acetate, which are common in the field of medicine, are firstly applied to prevention and control of crop diseases; prevention and control tests are carried out on crop fungal diseases such as sclerotiniose, corn southern leaf blight, tomato gray mold, rice blast and crop anthracnose, and results show that the two drugs can significantly inhibit the growth of pathogenic fungal hyphae, and compared with an existing agricultural commercial bactericide carbendazim, the bactericidal composition shows higher inhibitory activity and has the characteristics of high efficiency and low toxicity. The invention lays a foundation for deep research and application of echinocandin drugs in the agricultural field, has a wide market prospect, effectively improves the crop disease prevention and control level, and promotes grain yield increase and quality safety.

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.