3446 results about "Agricultural crops" patented technology

The invention provides an agricultural environment monitoring method and system based on the Internet of Things. According to the method, multi-source data weights are dynamically distributed through a soil sensor array connected with Internet of Things nodes, and a global soil parameter set is generated; deploying a micro-electro-mechanical micro-fluidic chip in the coverage area to carry out soil solution selective permeation, converting the target ion concentration into an electric signal, and synchronously transmitting the electric signal and soil parameters; historical time series data are extracted, coherence is established through time correlation analysis, and undeployed area data are filled in combination with a spatial interpolation algorithm to construct a dynamic prediction model; according to the nutrient space change trend output by the model, crop growth requirements are matched to generate a fertilization amount adjustment instruction, and the fertilization amount adjustment instruction is issued to field fertilization equipment through the Internet of Things to execute dynamic regulation. Space-time precise sensing of soil nutrients and self-adaptive fertilization regulation and control are realized, and the utilization efficiency of agricultural resources and crop growth sustainability are improved.

The invention relates to the technical field of water and fertilizer control, in particular to a water and fertilizer integrated control system and method based on the Internet of Things. The method comprises the following steps of obtaining environment measured data, historical environment data and regional geographic information of a farmland region, performing spatial modeling and time sequence learning on the farmland region, and constructing a farmland digital twinborn model; simulating the state of an area without sensors by using a farmland digital twinborn model to obtain virtual sensing node data; collecting multispectral image data of crops, and extracting physiological feature data of the crops; and constructing a crop physiological state model by utilizing the environment measured data, the virtual sensing node data and the crop physiological feature data, and generating crop physiological state evaluation data. Through multi-source data fusion and three-dimensional visualization integration, data-driven accurate regulation and control and whole-process intelligent management of the water and fertilizer integrated system are realized, and the intelligent level of irrigation and fertilization and the resource utilization efficiency are comprehensively improved.

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention discloses a planting optimization method and system based on crop yield estimation in a saline-alkali land area, and relates to the field of crop yield prediction and planting optimization, and the method comprises the steps: data collection and fusion processing; performing dynamic prediction on soil water and salt migration, and realizing water and salt space-time distribution prediction by adopting a space-time cubic modeling self-adaptive model combined with physical correction; crop salt tolerance modeling: establishing a multi-interval salt tolerance model based on physiological response fitting and threshold segmentation, and introducing covariable correction; carrying out migration salt tolerance coupling modeling, introducing a salinity memory factor and a stage sensitivity regulation factor, and dynamically simulating growth response of crops under salinity stress; and crop yield estimation and planting optimization: generating a regional yield prediction and sowing configuration scheme based on coupling model output, and forming an optimization strategy in combination with measures such as irrigation and fertilization. According to the method, model-driven integrated optimization from yield estimation to planting decision of crops in the saline-alkali region is realized, and the method has high precision and wide applicability.

The invention provides a crop planting area intelligent extraction method and system based on multispectral remote sensing, and relates to the field of remote sensing image processing, and the method comprises the steps: obtaining and correcting a multiband remote sensing image; calculating a vegetation index and constructing a crop growth characterization index to extract canopy features; obtaining a multi-period feature map, calculating a spatial distribution entropy, and establishing an evaluation function to determine a time sequence fusion weight; a spatial constraint function is established based on the spectral distance, and a segmentation criterion is constructed by combining the spatial constraint function with time sequence features for classification iteration. According to the method, the accuracy and the discrimination degree of crop planting area extraction are improved, and crop identification requirements in a complex agricultural environment are met.

The invention discloses a pure bio-based PHA aqueous dispersion as well as a preparation method and application thereof, and belongs to the technical field of pure bio-based polymer aqueous dispersion. According to the dispersion, polyhydroxyalkanoate is used as a unique film forming matter, Pickering solid particles and a natural surfactant are adopted for synergistic stabilization, and a bio-based plasticizer can be selected. The emulsion or slurry with the polyhydroxyalkanoate content of 40-85wt% and the particle size of 0.2-3m is prepared through a melting high-pressure homogenization method, a low-temperature wet grinding method or a solvent replacement method. The coating does not contain petrochemical polymer protective colloid and perfluorinated and polyfluoroalkyl substances, and the bio-based content is greater than or equal to 99%. Due to the excellent stability, rheological property and film-forming property of the modified starch, the modified starch becomes an ideal basic material for constructing a multi-layer programmable controlled release and degradation functional coating system, can be used in the fields of paper-based barrier coatings, adhesives, advanced crop seed coats and the like, realizes excellent barrier, bonding and controlled release properties, meets the industrial compost and repulping property standards, and has wide application prospects. The method has green, environment-friendly and industrial potential.

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 relates to the technical field of image enhancement, in particular to a field-level crop image enhancement system based on multi-source remote sensing data, and the system comprises an artifact joint detection module which analyzes the mutation or periodicity of a statistical mean value and a variance value in the row and column directions of an input field-level remote sensing image, and recognizes an artifact stripe region. According to the invention, the method achieves the high-precision removal of thin clouds and stripe artifacts through carrying out the mutability analysis of the local mean and variance of the input remote sensing image in the row and column directions, positioning a stripe artifact region, and building an attenuation numerical relationship in combination with the spectral information of an adjacent clear sky reference region, and reduces the ground feature information loss in the image. Multi-scale coefficient decomposition is further performed on the image after artifact removal, texture features are extracted from homogeneous crop canopies in adjacent undamaged areas, and high-frequency coefficients of damaged areas are dynamically replaced and reconstructed, so that the texture structures of the damaged areas are highly consistent with those of the adjacent areas, and the regional texture continuity and authenticity are 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 embodiment of the invention provides a modern agriculture digital authentication large model, and belongs to the field of agricultural big data and artificial intelligence, an agricultural data authentication method comprises the steps that data information of a target farm is acquired, and the data information comprises crop information, farm information and production information; according to the crop information, the farm information and the production information, a block chain is constructed, and the block chain comprises a main chain, a logistics sub-chain, a quality inspection sub-chain and a production sub-chain; block chain evidence storage is carried out on the block chain to obtain an authentication certificate of each crop in the target farm, and the block chain evidence storage comprises production data evidence storage, environmental data evidence storage, management data evidence storage, processing data evidence storage and tactile data evidence storage; and performing data tracking on the crops in the target farm according to the authentication certificate. According to the method, the agricultural yield and value are accurately estimated, and the agricultural planting scheme is optimized.

The invention belongs to the technical field of visual processing, and particularly relates to a time sequence NDVI crop distribution extraction method based on a mask auto-encoder, which mainly comprises four steps. Firstly, time sequence NDVI data are prepared, a multi-temporal remote sensing image in a complete growth cycle of target crops in a target area is obtained and processed, and the recognition precision is improved by using NDVI feature changes in the growth stage of the crops. And then performing time sequence transformation on the ViT model, dividing data space dimensions, completing Patch flattening and embedding, enabling the Patch to be adaptive to time sequence data, and simultaneously performing image processing advantages. Then, model training is carried out, a mask auto-encoder is pre-trained in a self-supervised mode through a large amount of unlabeled data, and then supervised fine tuning is carried out through a small amount of labeled data; and finally, processing new data by using the fine-tuned model, obtaining pixel-level crop category prediction, and generating a complete distribution map. According to the method, by means of time sequence data and model transformation, crop types and growth stage differences are effectively distinguished, and accurate extraction is achieved.

The invention discloses a crop disease and insect pest image recognition algorithm based on dynamic adaptive multispectral fusion Transform, and belongs to the crossing field of agricultural information technology and computer vision. The objective of the invention is to solve the problems of insufficient multi-spectral feature fusion, poor complex background adaptability and insufficient precision in traditional recognition. Acquiring pest and disease damage images of crops in different wave bands (visible light, near-infrared light and the like) to construct a data set; through a dynamic adaptive fusion module, spectral weight distribution is learned in real time based on an attention mechanism, weights are adjusted according to spectral response differences of disease and insect pest areas, and accurate feature aggregation is achieved; the fusion features are input into an improved Transform model, a self-attention mechanism of crop semantic priori knowledge is introduced, focusing of key features of diseases and insect pests is enhanced, and background interference is inhibited; and finally outputting the disease and pest category and confidence. According to the method, through dynamic fusion and Transform cooperation, the recognition accuracy and robustness in a complex scene are improved, support is provided for early warning and prevention of diseases and insect pests, and the application value is remarkable.

The invention discloses an unmanned aerial vehicle multi-scale crop detection method based on a neurodynamics model, and belongs to the technical field of crop detection, and the method comprises the steps: obtaining a visible light image, a long-wave infrared image and hyperspectral data of a crop under the multi-scale condition of an unmanned aerial vehicle; performing registration fusion processing on the visible light image, the long-wave infrared image and the hyperspectral data to obtain fused hyperspectral cube data; constructing a neurodynamic model for crop detection, inputting the fused hyperspectral cube data into the neurodynamic model for crop detection for training, and outputting a crop detection result; and calculating a loss function of the neurodynamic model for crop detection based on a crop detection result, and feeding back the loss function to the model for constraint. According to the invention, visual processing, deep learning and agricultural scene perception technologies are combined, and efficient and accurate crop monitoring is realized by using the parallel computing capability and anti-interference characteristics of the neurodynamics model.

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 field of crop cultivation, and particularly discloses a multi-crop interplanting method with a high land utilization rate. According to the method, a multi-crop interplanting land distribution scheme is designed on the basis of user planting requirements, planting molds are built on land parcels with serious soil problems, and the planting molds are filled with crop growth supporting materials to serve as planting soil; planters can interplant various crops in the planting soil according to a multi-crop interplanting land distribution scheme, crops with serious soil-borne diseases, such as solanaceae or medicinal materials, can be planted on the same land for many years, planting can be carried out in land parcels with serious soil problems, the land utilization efficiency is improved, and the planting cost is reduced. The crop yield per unit land area can be increased in an interplanting mode, land resources are fully utilized, meanwhile, the planting production cost is reduced, and the economic output per unit area is increased.

A numerical-control intelligent air-suction seed metering device, comprising a front shell 1, a rear shell 2, a seed metering disc 3, a built-in fan, a direct drive motor, and an individual seed metering unit intelligent controller 180, wherein a seed inlet 11, a seed outlet 12, and air inlets 13 are formed in the front shell, and the built-in fan is provided on the rear shell; seed suctioning holes 31 are formed in the seed metering disc, a seed filling chamber 7 is formed between the seed metering disc and the front shell, and a negative pressure chamber 8 is formed between the seed metering disc and the built-in fan; the seed metering disc is connected to a drive idler wheel 6; the direct drive motor comprises a stator core 5 and a plurality of magnetic steels 61, the stator core is fixed on the outer wall surface of a fixed drum, and the plurality of magnetic steels are fixed onto the drive idler wheel. In the technical solution, a built-in motor is used to directly drive the seed metering device, so that the seed metering device operates more stably and precisely; the speed of the built-in fan can be adjusted in real time by means of the individual seed metering unit intelligent controller on the basis of seeding requirements and crop types, so as to achieve a negative pressure suction force most suitable for the seed metering device, implement adjustment of the precision seeding rate of an individual seed metering device, and achieve the purpose of improving seeding precision.

The invention discloses an intelligent farmland planting method and system based on the Internet of Things, and relates to the technical field of the Internet of Things, and the method comprises the steps: marking the maturity degree of each crop; in the Internet of Things, a sub-monitoring area is determined according to the ripening degree of each crop and the position of each crop, abnormal growth characteristics of the crops in the sub-monitoring area are marked, and a planting dynamic graph of the farmland is determined based on each abnormal growth characteristic and the ripening degree of each crop. The accuracy of the planting dynamic graph of the farmland is improved; therefore, an abnormal planting area is determined according to each abnormal growth characteristic and the type of the crop, and a plurality of intelligent planting nodes and corresponding planting optimization measures are marked; the planting optimization event of each intelligent planting node is constructed based on each intelligent planting node and the Internet of Things, the accuracy of the planting optimization event of each intelligent planting node is improved, and the dynamic optimization state diagram of each abnormal growth feature is generated.

The invention relates to the technical field of agricultural remote sensing information processing, and particularly discloses a crop planting pattern spot intelligent extraction system based on remote sensing information, which constructs a multi-scale feature vector by fusing pattern spot area, compactness, time sequence vegetation index fluctuation and field ridge slope variation coefficient, and combines dynamic threshold adjustment and spatial semantic verification to obtain a multi-scale feature vector. According to the method, automatic correction of abnormal fragments, giant spots and topological conflicts is achieved, in complex scenes such as Yunnan terraced fields, through vertical field and ridge error response suppression and dynamic graph reconstruction, the pattern spot boundary precision and topological rationality are remarkably improved, and a classification result in a GeoJSON format is output.

The invention provides an agricultural scene adaptive multi-modal feature extraction and fusion method and system, and belongs to the technical field of agricultural monitoring, and the method comprises the following steps: obtaining multi-modal data of crops through deploying an image sensor, a soil sensor, an environment sensor and an acoustic sensor in a farmland monitoring area; preprocessing the multi-modal data, wherein preprocessing comprises the steps of performing normalization and noise filtering on the data; visual feature vectors are extracted from the image data through an improved ResNet-50 network, time sequence features are extracted from the environment data through 1D-CNN, and acoustic data are input into a lightweight MobileNetV3 network to extract voiceprint features after being subjected to Mel spectrum conversion; calculating a modal weight based on a feature fusion algorithm of an attention mechanism; and outputting the fusion feature vector, inputting the fusion feature vector into a pest classifier and a growth state regression device, and generating a pest prediction result. Through multi-modal data acquisition, preprocessing, feature extraction and adaptive fusion, the modal weight is dynamically adjusted in combination with an attention mechanism, the multi-source heterogeneous data fusion effectiveness is improved, the disease and pest feature sensitivity is enhanced, and the effect of dynamic weight distribution is achieved.

The invention provides a crop growth prediction method and system based on multi-modal data, and relates to the technical field of agricultural information, and the method comprises the steps: 1, collecting real-time environment data and crop physiological data of a heterogeneous multi-source sensor in a greenhouse environment, and constructing a multi-modal original data set; 2, performing preprocessing and space-time alignment on the multi-modal original data set to form a multi-modal data fusion matrix with a unified timestamp; step 3, based on the multi-modal data fusion matrix, selecting a reference data feature set, generating an environmental evolution mode and a physiological evolution mode, and calculating a correlation degree between the two modes to determine a feature response dimension; and respectively selecting dynamic monitoring feature sets inside and outside the feature response dimension, constructing a feature evolution trajectory according to a time sequence evolution relationship, and generating a dynamic compensation coefficient. By integrating environment and crop physiology multi-source data, the association rule of crop growth and environment is predicted, and the agricultural production efficiency is improved.

The invention belongs to the field of crop yield estimation, and discloses a corn yield remote sensing estimation method and system based on multispectral data coupling radiation transmission and a crop growth model, and the method comprises the steps: obtaining and preprocessing a Sentinel-2 multispectral remote sensing image, and obtaining the reflectivity data of a corn planting region; constructing a PROSAIL forward simulation spectrum library, and performing domain correction on the simulation spectrum by using an auto-encoder and a residual error alignment network; establishing a machine learning inversion model based on the corrected simulated spectrum and the actually measured spectrum, and generating regional leaf area index (LAI) distribution; key agronomic parameters are obtained, a localized WOFOST crop growth model is constructed, the LAI state of the model is assimilated by adopting ensemble Kalman filtering at a remote sensing observation moment, and the crop growth process is dynamically corrected; and advancing the model to a mature period, outputting the dry weight of the corn kernels, and realizing remote sensing estimation of the regional corn yield. The method can effectively improve the LAI inversion precision and yield prediction reliability, and is suitable for the fields of agricultural monitoring, grain evaluation, agricultural condition management and the like.

The invention discloses a method for detecting aspergillus flavus and aflatoxin B1 of crops based on an infrared spectrum. The method comprises the steps of crop sample material collection, rice multi-strain mould infection, spectrum collection, crop sample aflatoxin B1 determination, spectrum pretreatment, correction model construction and model verification. The invention aims at detecting the content of aspergillus flavus infection and toxin B1 thereof in crops by combining a near-infrared spectrometer and a stoichiometric method. The method has the advantages of being simple and easy to implement, high in analysis speed, high in detection precision, low in analysis cost, free of any pollution to the environment and the like, samples do not need any pretreatment, and the method has important practical value for guaranteeing the quality safety of agricultural products and reducing the carcinogenic risk of people.

The invention belongs to the technical field of crop transplanting, and particularly relates to a transplanting machine and method capable of conveniently adjusting the vegetable seedling distance, the transplanting machine solves the problems that soil adheres to the inner wall of a conveying channel during vegetable seedling transplanting, the operation efficiency is low, and watering of pits is insufficient, and the technical scheme includes that the transplanting machine comprises a moving vehicle body, a conveying belt and a transplanting structure; the transplanting structure comprises a guiding hopper and a conveying pipeline, duckbilled pieces are arranged below the transplanting structure, the duckbilled pieces are opened and closed through a linkage mechanism, pits are dug and the seedlings are released during descending, the water storage tank is connected with the annular pipe through a hose, water is injected into the guiding hopper and the inner wall of the pipeline, and soil adhesion is reduced; the vegetable seedling transplanting machine is characterized in that a duckbilled piece is matched with the inclined face of the fixed table to control opening and closing, a vertical rod of a liquid injection system is connected with a lifting base to achieve synchronous watering, and the vegetable seedling transplanting machine is used for transplanting vegetable seedlings and can automatically complete feeding, wetting and planting; the efficiency is improved; the seedling survival rate is increased.

The invention provides a crop identification and yield prediction method based on self-attention remote sensing feature extraction and reinforcement learning. The crop identification and yield prediction method provided by the invention comprises the following steps: preprocessing remote sensing data of a target area to generate time sequence data; the feature extraction network processes the time sequence data by using a self-attention mechanism to generate high-dimensional time sequence remote sensing features, the high-dimensional time sequence remote sensing features are subjected to dimension increasing to obtain feature data features, and the feature data features are provided for a prediction network and a confidence network; the feature extraction network also generates a prediction remote sensing image according to the high-dimensional time sequence remote sensing features; and the prediction network generates predicted types and yields of the crops according to the characteristics of the characteristic data.

A spreader system includes two spreader rotors configured to rotate about upright rotation axes and to eject residue crop material centrally between the rotors in a direction tangential to the rotors. The spreader system further includes a first structure that is configured to undergo an oscillating movement, such as a rotating oscillation about a central axis oriented in the tangential direction. The spreader system further includes adjustable deflector blades. The first structure can be a main deflector having main deflector blades, wherein the adjustable deflector blades are parallel to the main deflector blades and adjustable by a translation relative to the main deflector blades in an upward or downward direction, to thereby influence a gap between the main deflector and a lower plane of the rotors. The adjustable blades may be each pivotable relative to a frame so that their angular position can be set to one of several positions.

Certain exemplary embodiments can provide systems and / or methods for producing dried biomass materials, such as from bales of biomass. Biomass materials can be converted into, e.g., pellets, briquettes, cubes, and / or other products with dried and / or densified states. These dried, densified products can be used for, e.g., bioenergy and / or animal feed applications.

The invention discloses an intelligent crop recommendation system based on a neural network, and the system comprises the following modules: an agricultural multi-source data acquisition module which collects and preprocesses agricultural data of different geographic regions and seasons; the cross-environment data division module is used for forming a plurality of independent environment data sets; the deep feature extraction network module is used for extracting high-dimensional semantic features; the feature invariance constraint optimization module is used for constructing a unified stable feature model meeting cross-regional cross-seasonal generalization requirements; the generalization performance verification module is used for quantitatively evaluating model generalization accuracy; the personalized crop recommendation decision module is used for generating a personalized crop recommendation list; and the recommendation result visual interaction module is used for displaying the recommendation result to the user. According to the method, the cross-environment generalization ability and the personalized recommendation accuracy of the crop recommendation system are remarkably improved.

The invention provides a multi-sensor fusion crop harvesting and pile-dividing cooperative control method and a multi-sensor fusion crop harvesting and pile-dividing cooperative control system. The method comprises the following steps: extracting morphological characteristics of crops from crop image data, and generating a crop maturity identification result according to a difference value between reflection spectrum data of two groups of wavebands; determining a crop quality identification result according to the morphological characteristics and the crop maturity identification result; an opening and closing control instruction is sent to a pneumatic flow guide plate at a pile separation opening corresponding to the crop quality recognition result in a multi-stage pile separation opening in the tail end of the conveying belt, so that the crops on the conveying belt are conveyed to a corresponding pile separation area; and when the pressure value of the pile dividing area is larger than a preset pressure threshold value, the harvesting speed of the crop harvesting and pile dividing all-in-one machine is adjusted according to the pressure value. According to the technical scheme provided by the invention, the shape and maturity of the crops are analyzed through the image and spectral data, piling and harvesting can be intelligently controlled, and accurate and efficient crop quality grading and harvesting management are realized.

The invention relates to the technical field of remote sensing agricultural data management, and discloses a remote sensing agricultural big data management system based on a block chain. According to the system, image data is subjected to radiation, atmosphere and geometric correction preprocessing to generate a standardized remote sensing image; the feature extraction module performs multi-scale segmentation on the crop feature region, identifies the crop feature region, extracts texture, spectrum and shape feature values, and fuses the feature values to form a multi-dimensional feature vector; the block chain storage module performs hash operation on the vector to generate a feature fingerprint, constructs a new block in combination with a timestamp and a preorder block hash value, and forms a non-tampering agricultural data chain after verification of a consensus mechanism; a path analysis module traverses the block sequence, extracts a storage path and screens a high-relevance feature path set; and the intelligent classification module extracts feature tags, and generates an agricultural data classification structure through a multi-dimensional matching algorithm. The system realizes standard processing, safe storage and intelligent application of remote sensing agricultural data, and meets the requirements of agricultural modernization development.

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%.