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

The invention belongs to the technical field of microorganisms and biological control, and particularly relates to paenibacillus polymyxa and application thereof in biocontrol and growth promotion. Specifically, a strain of paenibacillus polymyxa GLZ219 is obtained through screening, and experiments prove that the paenibacillus polymyxa GLZ219 has a relatively high bacteriostasis rate on pathogenic bacteria of tomato neck and root rot; meanwhile, the strain has a good antagonistic effect on nine common plant pathogens such as Rhizoctonia solani and the like, and can be widely applied to green prevention and control of various crop diseases. Besides, the paenibacillus polymyxa GLZ219 has nitrogen fixing and potassium dissolving capabilities, can secrete heteroauxin and siderophores, and can effectively promote the growth of tomato plants, the plant height of tomato seedlings treated by fermentation liquor of the paenibacillus polymyxa GLZ219 is remarkably increased, diseases can be prevented and controlled, the growth vigor of crops can be improved, the yield and quality of the crops can be improved, and the paenibacillus polymyxa GLZ219 has a wide application prospect.

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 belongs to the technical field of image processing, and particularly relates to an intelligent identification prevention and control method and system based on crop disease severity, and the method comprises the steps: firstly carrying out the enhancement of a crop leaf image through super-resolution reconstruction, and improving the edge and texture details, thereby guaranteeing the precision of a subsequent segmentation task; then, double-stage segmentation is adopted, in the first stage, a leaf area is extracted, in the second stage, a scab area is finely segmented, and a corresponding mask image is generated; according to the integrated intelligent identification prevention and control method, on the basis of disease spot masks, calculation of disease spot area proportions, extraction of information such as color features, shape features, crop types and environment temperatures, calculation of severity scores, mapping of the severity scores into seven severity levels, and finally generation of structured prompt words, the provided intelligent identification prevention and control method is an intelligent identification prevention and control method. And the accuracy, intelligence and practicability of crop disease identification are comprehensively improved.

The invention provides a disease and pest epidemic prevention system based on image recognition, and the system comprises a data collection and preprocessing module which is used for collecting optical images, sound wave images and environment information of crops, and carrying out the preprocessing and feature extraction; the image enhancement module is used for processing the sound wave features, enhancing the optical features through a sound wave weight map obtained through processing, and marking the pest and disease region through a neural network to obtain an optical enhancement image; the image segmentation module is used for carrying out region division on the optical enhancement image and then carrying out pixel-level segmentation to obtain a to-be-identified region map; and the disease and pest recognition module is used for outputting a disease and pest recognition result graph through a disease and pest recognition model after learning a double mapping relation between the to-be-recognized area graph and the environment weight vector through a neural network. According to the invention, through comprehensive application of multi-modal information fusion and a deep learning technology, the efficiency and precision of crop disease and insect pest detection are significantly improved.

The invention provides a crop disease and insect pest judgment method and system based on image feature recognition, and relates to the technical field of picture recognition processing, and the method comprises the steps: carrying out the disease and insect pest recognition of a to-be-judged crop image through a pre-trained disease and insect pest analysis model, and obtaining a first disease recognition result; presetting similar pest and disease combination and corresponding combined spot features, judging whether the similar pest and disease combination exists in the first pest and disease recognition result, if not, outputting the first pest and disease recognition result, and if the first pest and disease combination exists, extracting a spot area of the to-be-judged crop image; traversing spot areas of the to-be-judged crop image, and if joint spot features appear in the spot areas, taking two diseases and pests in the similar disease and pest combination as output results; if the combined spot features do not exist, the pest and disease damage with the minimum confidence degree in the similar pest and disease damage combinations is removed; and updating the first pest identification result.

The invention belongs to the technical field of biological bacterial fertilizers, and discloses a Chinese herbal medicine carrier-based multifunctional microbial bacterial fertilizer, which comprises a microbial strain group, a Chinese herbal medicine active component group, an organic matter and nutrition carrier, mineral elements and auxiliary components, and a functional synergist. Through the synergistic interaction of Chinese herbal medicine active ingredients and functional microbial flora, breeding of harmful bacteria in soil is inhibited, synthesis of acetylcholin esterase of nematodes is interfered, a continuous cropping disease occurrence chain is blocked from the source, and meanwhile, organic acid and flavonoid substances slowly released by a Chinese herbal medicine carrier are utilized to activate resistance gene expression of a plant system, so that the disease resistance of the nematodes is improved. The tolerance threshold of crops to high temperature, cold damage and diseases is enhanced; in the composite carrier, humic acid and diatomite are crosslinked through hydrogen bonds to form a stable granular structure, the cation exchange capacity and porosity of soil are effectively improved, the strong hydration capacity of polyglutamic acid is combined to lock nutrients and moisture, and development of meristem of a root system and proliferation of capillary roots are promoted.

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

The invention discloses 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 bacillus velezensis BR-1 and application of the bacillus velezensis BR-1 in prevention and treatment of economic crop diseases. The preservation number of the bacillus velezensis BR-1 is CCTCC NO: M 20251379. The bacillus velezensis BR-1 disclosed by the invention is high in adaptability and wide in growth condition range, and is more beneficial to application in agricultural production. Meanwhile, the bacillus velezensis BR-1 has a remarkable inhibition effect on the growth of various phytopathogens such as verticillium dahliae, fusarium oxysporum, sclerotinia sclerotiorum and ralstonia solanacearum, can remarkably reduce the morbidity of cotton verticillium wilt, fusarium wilt and tomato bacterial wilt under a greenhouse condition, and has a wide agricultural application prospect.

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

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

The invention relates to the technical field of microbial fermentation, and discloses an agricultural ecological probiotic complex microbial inoculant, which is prepared from the following components in percentage by mass: 30 to 35 weight percent of beneficial microbial inoculant, 20 to 25 weight percent of mineral source potassium fulvate, 15 to 20 weight percent of slow release agent, 10 to 15 weight percent of nano polypeptide fermentation liquor and the balance of carrier, the processing method comprises the following steps: S1, pretreating the raw materials; s2, preparing nano polypeptide fermentation liquor; s3, complex microbial inoculants are compounded; and S4, drying and forming. According to the invention, the mineral source potassium fulvic acid and the nano-polypeptide fermentation liquor are added, so that effective regulation and control of soil micro-ecology are realized; crop growth is promoted through cooperation of multiple strains, growth hormone is secreted through bacillus subtilis and the like, nitrogen is fixed through bacillus amyloliquefaciens, root development is stimulated in cooperation with mineral source potassium fulvic acid, and the plant height, stem diameter and fresh weight of crops are remarkably increased; the particle size of the polypeptide subjected to high-pressure homogenization treatment is 80-120nm, the absorption efficiency is improved by 60%, and a crop disease-resistant signal channel is activated.

The invention discloses low-temperature-resistant rice endophytic streptomyces with biological prevention and control effects on common crop diseases and application of the low-temperature-resistant rice endophytic streptomyces, relates to the field of microorganisms, and aims to solve the problems that an existing rice blast biocontrol strain cannot play a stable effect under a low-temperature condition and is poor in rice blast control effect. The low-temperature-resistant rice endophytic streptomycete is alfalfa streptomycete F05 and is preserved in China General Microbiological Culture Collection Center (CGMCC), the preservation address is No.3, No.1 yard, Beichen West Road, Chaoyang District, Beijing, the preservation date is February 17, 2025, and the preservation number is CGMCC No.33539. The streptomyces medicaginis F05 disclosed by the invention can stably play a biological prevention and control role at a low temperature of 5-15 DEG C. The compound has broad-spectrum antibacterial performance, is used for inhibiting corn stalk rot bacteria, green bean colletotrichum, rice bakania, botrytis cinerea and rhizoctonia solani, and can also be used for decomposing starch and cellulose.

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.