6154 results about "Agricultural crops" patented technology

The invention provides a crop whole growth cycle identification method and system based on deep learning, and the method comprises the steps: obtaining a multispectral image sequence of a target crop in a continuous time period through an image collection device, and carrying out the standardized illumination adjustment processing of each image frame in the multispectral image sequence, obtaining a standard illumination image set corresponding to the multispectral image sequence; carrying out crop region segmentation processing on each image, extracting a local feature region related to a target crop, generating a standardized crop image set containing the local feature region, inputting the standardized crop image set into a pre-trained multi-task deep learning model, extracting combined features through parallel convolution branches, and carrying out image segmentation processing on the combined features; and executing cross-stage correlation analysis in the full connection layer, outputting a multi-task classification result corresponding to the target crop growth cycle, and generating a stage identification report corresponding to the target crop full growth cycle. According to the invention, the crop whole growth cycle identification precision and the agricultural management efficiency can be improved.

The invention discloses an intelligent crop growth prediction and optimization method based on multi-source data fusion, and belongs to the technical field of crop analysis, and the method specifically comprises the steps: obtaining space remote sensing data, ground sensor data and meteorological data, and carrying out the space-time alignment processing to generate fusion data representation; inputting the fused data representation into a prediction model, and outputting a crop future growth state sequence and a yield prediction result by coupling a crop growth mechanism and a dynamic environment response relationship; based on a yield prediction result, simulating long-term effects of different management strategies in a virtual growth environment, and screening out an irrigation scheme and a fertilization scheme with the optimal target function; transmitting the irrigation scheme and the fertilization scheme to a farmland execution terminal; collecting crop state feedback data and environmental parameter feedback data, and updating internal parameters of the prediction model based on the feedback data; according to the invention, crop growth management is converted from passive response to active regulation and control, and a whole-process intelligent solution is provided for precision agriculture.

The invention discloses an operation method of a humanoid agricultural robot based on path planning, which relates to the related technical field of agricultural robots and comprises the steps of multi-sensor data acquisition, data processing and map generation, map updating, target crop image recognition and positioning, global path planning, local path obstacle avoidance and picking action execution. The invention further discloses an operation system of the humanoid agricultural robot based on path planning. The operation system comprises a humanoid robot body, a visual sensor, a laser radar, a depth camera, an inertial measurement unit, an auxiliary sensor, an actuator module, a control system, a communication module and a power supply system. The inventor-shaped robot can recognize the positions and shapes of the tools in advance by using a sensitive visual perception system of the inventor-shaped robot, can skillfully cross over or bypass the tools by virtue of the flexibility of the inventor-shaped robot, can accurately determine the positions and distribution conditions of crops in a three-dimensional space, and generates a comprehensive picking environment map.

The invention relates to the technical field of electric digital data processing, in particular to an agricultural planting optimization system and method based on big data analysis. The method comprises the following steps: acquiring agricultural planting environment parameter data, and performing preprocessing and standardization to obtain a standardized environment feature vector; acquiring image, sound and smell data of crops; performing deep feature extraction on the image, sound and smell data to generate a multi-modal feature representation matrix; and performing multi-modal data fusion based on the standardized environment feature vector and the multi-modal feature representation matrix to obtain a comprehensive feature space. According to the invention, through multi-modal feature fusion and digital twinborn modeling, the precision and real-time performance of crop growth risk and pest and disease prediction are improved, and optimal regulation and control and efficient resource utilization of precision agriculture are realized.

The invention provides a crop monitoring system and method based on multispectral remote sensing and deep learning, and the system comprises a data preprocessing module which is used for carrying out the data preprocessing of a multispectral remote sensing image, and generating a standard reflectivity data set; the feature extraction module is used for extracting a high-dimensional spectral feature vector from the standard reflectivity data set through a multi-scale convolutional neural network; the time sequence dynamic analysis module is used for performing time sequence correlation analysis on the high-dimensional spectral feature vector through a long short-term memory network to generate a weighted time sequence feature vector; the physiological parameter quantification module is used for mapping the weighted time sequence feature vectors into quantitative indexes of crop physiological parameters; and the monitoring result generation module is used for performing dynamic deduction according to the quantitative index and generating dynamic trend prediction data of the crop growth state. The system can dynamically sense the growth stage characteristics of crops and adaptively adjust the spectral feature extraction strategy, thereby improving the crop monitoring precision in a complex agricultural environment.

Methods of creating and using digital twins of vegetation / crops in relation to their growing environments. Some embodiments include creating a digital twin of the growing environment using sensor data from a subsurface sensor suite, creating a digital twin of the vegetation crop using data from an overhead sensor suite, and using at least one mathematical model to relate the digital twin of the crop to the digital twin of the growing environment. In some embodiments, the mathematical model(s) implement a root cause analysis that uses a limiting factor approach. In some embodiments, the subsurface sensor suite is on a multi-sensor soil probe. In some embodiments, the multi-sensor soil probe comprises a video microscope, a tip stress sensor, a sleeve stress sensor, and a volumetric- water-content sensor. Other related systems, devices, methods, and software are also disclosed.

The invention relates to the field of crop monitoring and analysis, in particular to a crop growth monitoring method and system based on multi-sensor fusion. The method comprises the following steps: collecting multi-dimensional crop growth environment monitoring parameters, carrying out environment multi-dimensional perception fitting, and constructing a growth environment multi-dimensional perception model; acquiring a crop full-cycle growth monitoring image and a crop physiological state original data set; performing growth state deep evolution according to the crop physiological state original data set, and constructing a multi-mode growth state evolution graph; and carrying out adaptive region filtering and denoising on the crop full-cycle growth monitoring image flow, and carrying out crop three-dimensional morphological evolution analysis to obtain a crop three-dimensional morphological evolution rule. According to the invention, the virtual simulation model is perfected by collecting real-time parameters, intelligent analysis and real-time decision making are carried out, and the stability and accuracy of crop monitoring analysis are improved.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

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 discloses an entity alignment method for a multi-modal crop knowledge graph, and belongs to the technical field of knowledge graphs and agricultural intelligent analysis, and the method comprises the steps: obtaining a data feature item set of an agricultural field, and carrying out the time sequence compensation, and generating a growth time sequence feature parameter; performing feature extraction on the data feature item set, performing fusion to generate a multi-modal fusion feature mapping graph, constructing a dynamic feature matching network based on growth time sequence feature parameters, and performing entity node traversal on a reference knowledge graph to generate a candidate alignment set and a corresponding difference unit set; filtering conflict nodes in the candidate alignment set according to a three-dimensional confidence evaluation model to generate an effective alignment chain; and generating a graph updating instruction based on the difference unit set, and reconstructing a knowledge graph topological structure in combination with the effective alignment chain. According to the method, multi-modal feature bridging, dynamic weight optimization of growth stage perception and an incremental conflict resolution mechanism are adopted, so that cross-domain agricultural entity accurate alignment and real-time adaptive updating of the knowledge graph can be realized.

The present disclosure provides non-intergeneric remodeled microbes that are able to fix atmospheric nitrogen and deliver such to plants in a targeted, efficient, and environmentally sustainable manner. The utilization of the taught microbial products will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived nitrogen fertilizer. The remodeled microbes taught herein are able to be combined with leading agricultural chemistry and elite germplasm. Furthermore, the disclosure provides seed treatments comprising remodeled microbes.

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 present disclosure provides guided microbial remodeling (GMR) methods for the rational improvement of plant-associated microbes to perform plant-beneficial functions. The GMR methods described herein allow for non-intergeneric genetic optimization of key regulatory networks within the microbes, which improve plant-beneficial functions over wild-type microbes but don't have the risks associated with transgenic approaches (e.g., unpredictable gene function, public and regulatory concerns, etc.). The present disclosure also provides remodeled microbes and compositions thereof. The utilization of remodeled microbes and compositions thereof will enable farmers to realize more productive and predictable crop yields without the nutrient degradation, leaching, or toxic runoff associated with traditional synthetically derived fertilizers.

The invention discloses a crop growth analysis system based on machine vision, and relates to the technical field of agricultural intelligent monitoring and analysis, the system comprises a multi-source acquisition module, a disturbance identification module, a growth behavior modeling module, an environmental adaptability correction module and a feedback prediction module; wherein the multi-source acquisition module can synchronously acquire visible light, near-infrared and depth images, and generates a joint index in combination with environmental data; the disturbance identification module identifies abnormal areas such as wind disturbance and shielding based on the residual image and the image structure network; the growth behavior modeling module is used for extracting internode change, leaf surface tension and bifurcation angle characteristics by using a three-dimensional structure time sequence alignment and hidden change rate coding network; the environment adaptability correction module constructs a crop-environment double-domain mapping relation; the feedback prediction module outputs personalized intervention suggestions in combination with deviation analysis and crop variety embedding vectors; the system has the advantages of high modeling precision, strong adaptability, fast feedback response and the like, and is suitable for intelligent planting management of various crops.

The invention provides a crop planting intelligent decision inference system and method based on multi-source data fusion, and relates to the technical field of agricultural planting, and the system comprises a multi-source data fusion module which is used for extracting key characteristics of target crop growth according to multi-source data; the intelligent sensing module is used for constructing an agricultural knowledge graph according to the multi-source heterogeneous data of crop planting; and the reasoning and decision-making module is used for performing decision-making reasoning on the multi-source data and the key features according to the agricultural knowledge graph, a preset reasoning model and a decision-making model. According to the crop planting decision-making system, the defects that the precision in the planting management process is not high due to the fact that a traditional crop planting decision-making system is insufficient in decision-making ability, and the generalization ability of the system is insufficient due to the fact that most decision-making systems are optimized in a single field or specific crops and lack a cross-field multi-source data cooperative processing mechanism are overcome.

The invention discloses a non-point source pollution control and ecological agriculture decision-making method based on reinforcement learning, and relates to the technical field of new-generation information. The method comprises the steps that meteorological parameters, soil parameters and crop growth parameters are collected through a farmland Internet of Things system, and historical management data in the crop growth process and water quality monitoring information of surface runoff are collected to serve as original data; preprocessing the original data to construct a multi-modal data set; constructing an ecological agriculture multi-modal reinforcement learning model based on the multi-modal data set; integrating the ecological agriculture multi-modal reinforcement learning model to an agricultural intelligent decision-making platform, so that the ecological agriculture multi-modal reinforcement learning model is coupled with the agricultural intelligent decision-making platform; and an agricultural intelligent decision-making platform is utilized to issue operation instructions to irrigation, fertilization and pesticide application equipment so as to realize non-point source pollution management and control and ecological agriculture decision-making. Through a machine learning algorithm and real-time data analysis, many limitations of traditional ecological agriculture are overcome.

An automated system for the detection of pests and diseases in crops using image processing based on deep learning, which includes the following: ◯ an image acquisition unit (101) configured to capture high-resolution images of crops; ◯ a preprocessing unit (102) for improving image quality by applying noise reduction, contrast enhancement and segmentation techniques; ◯ a convolutional neural network (CNN) model(103) trained on a dataset of annotated images of pests and diseases and configured to extract features and classify detected anomalies; ◯ a decision unit (104) that, based on the detected pests or diseases, generates diagnostic reports and provides treatment recommendations; ◯ an integrated mobile (105) and web-based application interface that provides real-time analytics, historical data tracking and recommendations for farmers and agricultural experts.

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 relates to a crop drought degree prediction method based on unmanned aerial vehicle remote sensing monitoring. The method comprises the following steps: S1, data collection: collecting a multispectral image and a thermal infrared image of a farmland in real time through a remote sensing sensor; s2, image preprocessing: carrying out preprocessing operation on the collected multispectral image and thermal infrared image; s3, class specific feature selection: dividing the farmland into different classes according to the types, growth stages and expected drought degree grades of the crops, decomposing a multi-class classification problem into a plurality of dichotomy problems, and constructing a deep learning model for feature learning and importance evaluation for each dichotomy problem to obtain a class specific feature selection result; a class specific feature set for each class is formed, and class specific features for different classes are fused to form a comprehensive feature set; s4, model construction and training: constructing a drought degree prediction model according to the comprehensive feature set; and S5, drought degree prediction: realizing real-time monitoring and prediction of drought according to the real-time data and the prediction model.

The invention relates to the technical field of agricultural information, and discloses a visual analysis method and system based on an agricultural big data model. According to the system, crop growth time sequence data are processed by using a multi-scale feature extraction algorithm combining a recurrent neural network and wavelet transform, and key conversion points of a crop growth stage are automatically identified by using a deep learning architecture combining a variational auto-encoder and a hidden Markov model. A standard growth track model and a multi-dimensional deviation measurement index are constructed to evaluate the abnormal growth condition of crops, a dynamic threshold model based on reinforcement learning is applied to realize early warning of abnormal growth, and a multi-dimensional visual interface based on a clock metaphor is designed to visually display crop growth information. The crop growth stage recognition precision and early warning timeliness are improved, and the agricultural production management efficiency is remarkably enhanced.

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 discloses an unmanned agricultural production system based on an agricultural scene and an inspection robot control method. The method comprises the steps that an environment sensing module obtains environment data in the agricultural scene through a plurality of sensors; the data processing module analyzes the state of the crops in the environment data and formulates a plurality of farming tasks according to the state of the crops; a task planning module formulates an inspection robot control strategy according to the priorities of the farming tasks and the state of the inspection robot; the robot control module controls movement of the inspection robot according to the inspection robot control strategy and completes corresponding farming tasks in the moving process, and unmanned production of a farm is achieved; according to the invention, corresponding farming tasks can be formulated according to crop states, the optimal moving path and task execution sequence of the robot in the farm are planned according to the priorities of the farming tasks, the full-range patrol inspection of the farm is realized, the production efficiency is improved, and the unmanned and intelligent farm is realized.

The invention relates to the technical field of agricultural planting, and discloses a crop planting suggestion generation method adapting to different climate conditions. Real-time climate data are collected through a multi-source climate sensor, climate dynamic feature data are generated through a spatial-temporal feature fusion algorithm, and planting strategy parameters are obtained by inputting the climate dynamic feature data into a pre-trained mixed decision model. And constructing a multi-objective optimization model taking the crop adaptability matching degree and the resource utilization efficiency as objectives, and globally optimizing a planting scheme by adopting a hierarchical dynamic programming algorithm integrating fuzzy logic constraint and an adaptive decomposition strategy. A multi-level regulation and control framework comprising a decision-making layer, an adaptation layer and an execution layer is constructed, the decision-making layer generates a global planting sequence, the adaptation layer uses a sliding window optimization algorithm to adjust local parameters, the execution layer regulates and controls soil humidity and illumination intensity based on a robust feedback control algorithm, and finally a planting control instruction is output. And accurate and efficient climate adaptability planting decision is realized.

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 present disclosure provides a decision-making method and apparatus for water and fertilizer stress of crops, and a mobile phone terminal, and belongs to the technical field of intelligent agriculture. The method includes: determining a current crop coefficient and canopy temperature of a crop to be identified on the basis of a first crop image and a second crop image; determining a water stress state and a nutrient stress state on the basis of the current crop coefficient and canopy temperature of the crop to be identified; and processing the water stress state and the nutrient stress state with a fuzzy control algorithm, and determining a water and fertilizer stress decision result of the crop to be identified.

The invention provides an intelligent crop monitoring system based on spatio-temporal data, and relates to the technical field of crop monitoring, and the system comprises a data collection module which is used for collecting multi-source spatio-temporal data of a target farmland area; the multi-source spatio-temporal data comprises remote sensing images, meteorological data, soil data and farmland sensor data; the data processing module is used for carrying out preprocessing and data fusion on the multi-source spatio-temporal data to obtain a unified spatio-temporal data set; the intelligent analysis module is used for performing intelligent analysis on the growth state, the pest and disease risk and the yield of the crops according to the spatio-temporal data set based on artificial intelligence and spatio-temporal analysis technologies to obtain an analysis result; and the visual display module is used for carrying out visual display on the analysis result. The agricultural production efficiency and the management level are improved, powerful support is provided for sustainable agricultural development, and remarkable economic, social and environmental benefits are achieved.

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 belongs to the field of agricultural information perception, and relates to a plant leaf scab segmentation method and system based on RGB-D cross-modal fusion, and the method comprises the steps: collecting a color image and a depth image of a crop through a synchronous collection device; registering the color image and the depth image to obtain a registered color image and a corresponding registered depth image; constructing an initial segmentation network, and performing model training on the initial segmentation network; the initial segmentation network comprises a cross attention-based feature aggregation module and an attention-guided adaptive feature fusion module; inputting the registered color image and the registered depth image into a trained segmentation network for segmentation to obtain a segmentation result; through heterogeneous data fusion of a depth sensor and a visible light camera, a multi-dimensional feature system covering two-dimensional textures and three-dimensional deformation is constructed; in combination with a cross-modal feature complementation mechanism, the recognition robustness of weakly dominant diseases and insect pests is enhanced, and the segmentation precision in a complex illumination and branch and leaf shielding scene is significantly improved.

The invention provides an automatic navigation and positioning method for agricultural equipment, and the method comprises the steps: obtaining the root system residue distribution data of a previous crop and the soil hardening difference, building a correlation model of the crop root system residue distribution and the soil hardening difference, and determining a target tillage depth plan for the sowing of a next crop according to the correlation model; the operation track and depth parameters meeting the soil texture improvement requirements are applied to an automatic navigation and positioning system of agricultural equipment, and the operation process of the agricultural equipment is automatically controlled and dynamically adjusted; and based on the adjusted operation track and depth parameters, analyzing the long-term influence of an automatic navigation and positioning system of agricultural equipment on soil texture improvement in different growing seasons by tracking soil quality indexes, crop yields and root system distribution.