133 results about "Agricultural management" patented technology

The invention discloses a farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion, and relates to the technical field of agricultural information, and the method comprises the steps: collecting physical and chemical parameters of soil temperature, humidity, pH value, organic matter content, nitrogen phosphorus and potassium concentration and the like in real time through a multi-source sensor network; removing abnormal values by using a data preprocessing algorithm, performing standardization processing, generating a standardized soil parameter set for subsequent climate and management factor correlation analysis, automatically generating a risk early warning signal, pushing the risk early warning signal to an agricultural management decision platform, and completing full-process analysis from data acquisition to decision support; the farmland soil quality dynamic diagnosis method based on multi-source remote sensing collaborative inversion can provide a scientific basis for agricultural management decision, effectively guide farmland management practice, and improve soil quality and agricultural production efficiency.

The invention discloses an agent-driven self-adaptive farming management system, which belongs to the technical field of intelligent agriculture and artificial intelligence crossing, and comprises a farming management system, and the farming management system constructs an agricultural management agent with high autonomy, self-adaptive learning ability and scientific decision basis. The agricultural management agent comprises a sensing module, a planning and reasoning module, an action module and an incremental learning module; the system has flexibility, scientific preciseness and high autonomy and adaptability, can realize the precise, autonomous and efficient management of the whole cycle of agricultural production in a thousand-place and thousand-strategy mode, remarkably improves the resource utilization efficiency and crop output, and greatly reduces the manpower operation and maintenance cost.

The present disclosure relates to different techniques of controlling an agricultural system, as for example a controlled agricultural system, an agricultural light fixture and a method for agricultural management.Furthermore, the disclosure relates to an agricultural system, which comprises a plurality of processing lines for growing plants of a given plant type, wherein a first processing line in the plurality of processing lines is configured to move a first plurality of plants through the agricultural system along a route; and apply a first growth condition to the first plurality of plants to satisfy a first active agent parameter for the first plurality of plants.

The invention relates to an intelligent agricultural management system based on a planting-harvesting-distribution full link, and belongs to the field of agriculture, and the system comprises an online seed selection module, an order tracking module, an environment monitoring and early warning module, a pest and disease identification module, a market prediction module, a logistics scheduling module, a product estimation module and a growth state evaluation module. In the planting link, intelligent monitoring and abnormal early warning are carried out on the crop growth environment through the intelligent agent, accurate planting schemes for different crops can be formulated for farmers, and irrigation and fertilization schemes suitable for different regions can be formulated; in the production process, the intelligent agent can intelligently identify bad fruits and give out a treatment scheme, accurately identify diseases and pests and provide an effective prevention and treatment means; in the marketing link, the market trend can be predicted, the production scheme is reasonably arranged, and the production and marketing balance of agricultural products is ensured.

The invention relates to the technical field of agricultural intelligent management, in particular to a crop growth dynamic optimization decision-making method, system and device based on multi-source data and a medium, and the method comprises the steps: collecting agricultural-related multi-source data; the collected data are preprocessed; loading the pre-training model; configuring hyper-parameters of the model, and introducing a low-rank matrix into a weight matrix of the pre-training model to realize parameter re-parameterization; calculating the gradient of model parameters according to the loss value during model training, and updating the parameters of the model according to the gradient; the performance of the model is evaluated by adopting an automatic evaluation index, hyper-parameters, architecture or data of the model are adjusted according to an evaluation result, and a trained prediction model, namely an agricultural agent, is generated; the agricultural agent is deployed in the agricultural decision system, so that the agricultural agent receives and processes external real-time data in real time, and provides crop growth optimization decision service according to a prediction result. And reasonable decision suggestions are provided for agricultural production.

The invention belongs to the technical field of rice panicle shielding image data processing, and provides a rice panicle shielding image restoration method and system based on a generative adversarial network, and the method comprises the steps: collecting rice panicle images at different angles and under different illumination conditions to construct a data set, employing a target detection model to carry out the positioning of a rice panicle region, and classifying the shielding types, and extracting a visible area of the rice spike by adopting a semantic segmentation network, and repairing a sheltered area under a generative adversarial network framework to obtain a complete rice spike image. The method is suitable for complex scenes such as leaf shielding, inter-panicle mutual shielding and mixed shielding, texture details and structure consistency of the repaired image can be guaranteed, acquisition of complete phenotype information of the rice panicles is achieved, and reliable data support is provided for rice yield estimation and precision agricultural management.

The invention provides a corn yield prediction method based on cooperation of an unmanned aerial vehicle and satellite remote sensing, and relates to the technical field of deep learning, and the method comprises the steps: S1, constructing a multi-source basic data set which comprises a ground real data set, an unmanned aerial vehicle remote sensing data set and a satellite remote sensing data set; s2, key feature images in the remote sensing data set are extracted for the phenological period of corn yield estimation, and a feature data set is constructed; s3, constructing an improved corn yield prediction model; and S4, introducing a self-adaptive penalty loss function into the improved corn yield prediction model, quantifying the consistency and conflict in the model by using the internal state of the improved corn yield prediction model, and adaptively adjusting the penalty intensity to obtain an adjusted loss function. According to the method, large-area and high-resolution corn yield spatial distribution prediction can be realized, the prediction precision is high, the timeliness is strong, and key data support is provided for agricultural precise management.

The invention provides a multi-source data fusion variable fertilization method and device, and belongs to the field of agricultural production technology and agricultural remote sensing application, and the method comprises the following steps: data acquisition and preprocessing: collecting historical data, ground sampling data, ground sensor data and remote sensing images; spatial data processing and consistency correction; variable fertilization model construction: constructing a fertilization amount and yield effect curve, constructing a yield and growth vigor index VI distribution diagram, and further constructing a variable fertilization model representing the relationship between the fertilization amount and the growth vigor index VI; the spatial heterogeneity quantification and zoning comprises the following steps: quantifying the spatial heterogeneity of environmental factors on fertilization requirements by using a geographic detector, and dividing a region into a plurality of sub-regions; and differential fertilization strategy optimization: generating a dynamic adjustment coefficient, and determining a final fertilization amount in combination with the fertilization amount recommended by the variable fertilization model. The crop growth condition can be described more comprehensively and accurately, and accurate agricultural management can be guided.

The invention discloses a farmland furrow three-dimensional point cloud data correction method and system based on a laser radar, and relates to the technical field of three-dimensional image processing based on the laser radar, in particular to a three-dimensional image geometric correction technology. The problems that original point cloud data directly obtained under the dynamic operation condition of an existing agricultural machine has geometric distortion, and the shape of a furrow cannot be truly reflected are solved; meanwhile, the problems that furrow quality evaluation seriously depends on low-efficiency manual means, the adaptability of an existing automatic technical scheme to an unstructured field environment is insufficient, and robust, real-time and high-precision perception of a real three-dimensional shape of a furrow cannot be achieved are solved. According to the method, dynamic tilt correction is carried out through real-time acquisition of point cloud and agricultural machine attitude information, boundary feature points are extracted, boundary lines are fitted, and a three-dimensional point cloud set of a single furrow is reconstructed by fusing a self-adaptive clustering algorithm of spatial prior. The method is suitable for the fields of farmland tillage and soil preparation quality detection, precision agricultural management, agricultural machinery intelligent operation and the like under the dynamic operation condition of agricultural machinery.

The invention discloses a satellite measurement and control remote sensing integrated task scheduling method and system, and mainly solves the problems of high failure rate of separation of measurement and control and remote sensing tasks, low scheduling efficiency and the like in the prior art. According to the implementation scheme, a low-orbit satellite network transmits a data packet generated by a measurement and control remote sensing task and receives the data packet through a ground station; the ground control center generates a task list; the ground station equipment generates equipment information and generates available forecast information through interaction with the low earth orbit satellite network; the ground control center integrates and preprocesses the equipment information and the available forecast information, calculates a task state vector, and represents the task state vector as a Deque double-end queue; performing task planning according to the double-end queue to obtain a legal forecast selected by the intelligent agent, resolving conflicts between the legal forecast and other available forecast, and updating states of other related tasks; and repeating the task planning step until the planning of all tasks is completed, and outputting a legal task scheduling plan. The method improves the task planning efficiency and the task completion rate, and can be used for environment monitoring, weather prediction, agricultural management, and disaster early warning and reconnaissance.

Embodiments of the present disclosure may include a method for agricultural management, including receiving a trained first management policy that was trained with state information. Embodiments may also include training a second management policy using imitation learning. In some embodiments, the imitation learning uses the trained first management policy and partial state information in order to output action information, the partial state information being a portion of the state information.

The invention relates to the field of agricultural management, and particularly discloses a smart agriculture integrated management method and system based on the Internet of Things, and the method comprises the steps: carrying out the coding of multi-source time sequence environment data, such as weather and soil, through a recurrent neural network, and extracting an environment vector representing the potential risk of disease occurrence; meanwhile, a current visual feature map of the crop is extracted from the unmanned aerial vehicle remote sensing image by using a convolutional neural network. Furthermore, topological correlation analysis and saliency re-calibration are carried out on local semantics in the visual feature map through a graph neural network and an attention mechanism by taking an environment vector as guidance, so that a deeply coupled crop vision-environment state saliency fusion vector is generated. The fusion vector not only contains apparent diseases of crops, but also contains environmental causes causing the diseases. And finally, joint prediction of disease probability and severity level is carried out based on the fusion vector, so that closed-loop management from data acquisition to accurate and graded early warning is realized.

The invention discloses a field crop growth monitoring method based on low-altitude remote sensing multi-scale data, relates to the technical field of agricultural remote sensing and intelligent monitoring, and is used for solving the problem that a monitoring method integrating multi-scale remote sensing data and having high precision, high adaptability and high efficiency is lacked in the prior art. The method comprises the steps of collecting remote sensing image data of a monitoring area, performing splicing and spectrum correction on images, extracting canopy coverage, vegetation indexes, colors and texture features to construct a feature matrix, and dividing different cells based on clustering analysis; carrying out sampling analysis on the key cells at the middle-distance flight height, further obtaining cell growth vigor characteristics and determining sampling points; single plant growth vigor diagnosis is completed, and a diagnosis result is output; the method can be widely applied to field crop growth monitoring and precision agricultural management work.

The invention discloses a forsythia suspensa growth vigor monitoring method based on unmanned aerial vehicle remote sensing, and particularly relates to the technical field of unmanned aerial vehicle remote sensing monitoring. S2, calculating a vegetation index; s3, obstacle recognition; s4, evaluating the growth vigor; and S5, grade drawing. According to the method, a structured data set is constructed through integration of multispectral data and space coordinates, a high-resolution vegetation index map is generated in a pixel-by-pixel calculation mode, non-vegetation obstacles are accurately recognized by combining band ratio analysis and threshold judgment, vegetation index change trends are analyzed through time series data, and phenological period standard interval deviation is calculated. Spatial growth trend grade distribution is generated through mapping evaluation coefficients, the processing logic realizes closed-loop linkage of data acquisition and analysis, eliminates interference of non-target ground features, improves the accuracy of monitoring results, establishes a dynamic evaluation mechanism, and provides reliable basis for precise agricultural management.

The invention belongs to the technical field of digital agricultural management, and particularly relates to a digital agricultural intelligent management system and method based on the Internet of Things. Through real-time data acquisition and multi-dimensional information processing, various strategies can accurately match actual production requirements, waste of resources such as water, fertilizer and pesticide is effectively reduced, the purpose of saving cost is achieved, fertilization and prevention and control schemes are adjusted in time according to real-time crop growth states and environment changes, and therefore the influence of diseases and pests is reduced, and the crop quality is improved. Healthy growth of crops is promoted, the yield and quality are improved, stable operation of various Internet of Things devices is guaranteed through device health monitoring, management interruption or data loss caused by device faults is reduced, efficient operation of the whole intelligent agricultural system is guaranteed, and the intelligent agricultural system is safe and reliable. Spatial data integration enables a management strategy to be optimally configured according to geographical and environmental conditions of different regions, and precise agricultural management according to local conditions is realized.

The invention discloses an intelligent agricultural management method and system based on computer analysis, and particularly relates to the technical field of agricultural information, and the method comprises the following steps: obtaining farmland resource information, obtaining and processing a farmland resource information set, obtaining an environment data set and a growth data set according to a processing result, and then carrying out the environment analysis and growth analysis, a growth deviation combination and an environment deviation combination are obtained, a crop classification combination set of the target farmland is further obtained, category code numbers are generated, corresponding abnormal type early warning signals and comprehensive abnormal levels are generated according to the category code numbers and a preset early warning strategy and summarized, and an early warning combination set is obtained; by monitoring and analyzing the environment condition and the crop growth state of the farmland in real time, problems in agricultural production can be found and solved in time, various potential problems and risk factors in the farmland can be identified, and basis data of a precise management strategy can be provided for agricultural production.

The invention discloses a smart agriculture management method based on ecological agriculture, and relates to the technical field of smart agriculture management, in a soil information processing link, through calculating a soil suitability index, scientifically evaluating the suitability of a specified area to a specific crop, assisting in reasonable planning of planting, and in a land parcel division process, determining the suitability of the specified area to the specific crop; a geographic information system and a clustering algorithm are utilized to realize dynamic land parcel division, high-value planting areas are marked according to soil suitability indexes, unmatched land parcels are improved, land utilization efficiency is improved, healthy growth of crops is guaranteed by calculating irrigation amount and prevention and treatment probability, optimizing planting strategies, and ecological benefit evaluation is realized by calculating ecological benefit scores in the aspect of ecological benefit evaluation. Whether the planting strategy reaches the standard or not is judged, agricultural sustainable development is promoted, planting decision accuracy is effectively improved, resource waste is reduced, economic benefits and ecological benefits are improved, and powerful support is provided for ecological agriculture development.

The invention relates to the technical field of agricultural digital management, and discloses a digital agricultural management system based on the Internet of Things, and the system comprises an environment sensing module which is configured to collect real-time environment data inside and outside a target agricultural facility and real-time energy consumption data of regulation and control equipment in the facility, collecting the real-time environment data and the real-time energy consumption data to form historical environment data and historical energy consumption data; and the thermal inertia modeling module is configured to receive the historical environment data and the historical energy consumption data formed by the environment sensing module. A self-adaptive regulation and control engine is adopted, an equipment control instruction is dynamically generated according to real-time environment data and a preset crop growth demand curve, dynamic optimization and accurate execution of a control strategy are achieved, and compared with an existing control scheme adopting a fixed threshold value or a rigid time program, the control scheme has the advantages that the efficiency is high; the problems that a control strategy is rigid and cannot actively adapt to external environment changes and crop growth stage requirements are solved.

The invention relates to the field of agricultural operation management information processing, in particular to a laying hen feed nutrition intelligent proportioning method and system. The method comprises the following steps: acquiring weight, laying rate and environmental data of laying hens, and performing time alignment and cleaning, feature extraction and model reasoning to generate a nutritional demand prediction result; performing demand mapping, constraint verification and raw material binding on the basis of demands and a feed raw material nutrition library to generate a formula optimization problem; carrying out cost loading, optimization solution and executable check in combination with raw material prices to generate formula issuing data; and performing parameter updating and threshold adjustment through protocol adaptation, task arrangement and execution feedback to generate revised parameters. The system has the beneficial effects of improving the feed proportioning precision, optimizing the nutrition supply, reducing the cost and realizing automatic closed-loop management.

The invention relates to an intelligent agricultural resource monitoring and management method and system, and relates to the technical field of intelligent agriculture, the technical scheme is based on a cloud-side-end collaborative architecture, agricultural resource data is acquired by deploying a multi-source sensor network, and real-time data processing and local control are performed by an edge computing node; and the cloud platform performs macroscopic trend prediction by using a CNN-LSTM hybrid artificial intelligence model, generates a comprehensive management strategy in combination with a multi-objective optimization algorithm, and finally drives execution equipment to complete precise operation. According to the scheme, the problems of data processing lag, single decision basis, extensive resource allocation and the like in traditional agricultural management are effectively solved, real-time response, accurate prediction and multi-target collaborative optimization of agricultural resource management are realized, and the resource utilization efficiency, agricultural production benefits and ecological environment benefits are remarkably improved.

The invention discloses a multi-modal open-field vegetable management decision method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining modal perception data corresponding to a target open-field vegetable field; determining a modal feature corresponding to each modal sensing data, determining a feature fusion weight corresponding to the modal feature of each time anchor point, and determining a global multi-modal fusion feature based on the feature fusion weight and the modal feature; and determining knowledge representation features from the agronomic knowledge spectrogram based on the modal perception data, and determining a target decision scheme according to the knowledge representation features and the global multi-modal fusion features. Various data sources such as weather, soil, crop states and agricultural machinery operation parameters are fused, space-time alignment and a self-adaptive weighting mechanism are adopted, a reinforcement learning technology is combined, an agricultural machinery operation strategy is dynamically adjusted according to real-time data, agricultural management measures such as fertilization, irrigation and pest control are optimized, the production efficiency is improved, and the economic benefit is increased. And the real-time performance and the accuracy of decision making are ensured.

The invention relates to the technical field of intelligent agriculture, and discloses an agricultural sensor for plant rhizome monitoring, a follow-up robot and a sensing system.The agricultural sensor comprises a data acquisition module, the data acquisition module comprises a first power module, a first communication module and a receiving sensor group, and the first power module is used for providing a stable power supply for the sensor group; long-time work is supported; the first communication module is used for transmitting the collected data to a remote cloud platform through a low-power-consumption communication technology. Through integration of the intelligent agricultural sensor and the cloud platform, real-time accurate monitoring and intelligent decision making of the soil environment are realized, and the refinement level of agricultural management is improved. The low-power wide area network communication technology ensures the high efficiency and remote operability of data transmission, and enhances the reliability and coverage area of the system. The follow-up robot dynamically adjusts the position of the sensor through an accurate transmission system, and the accuracy and adaptability of data acquisition are optimized.

The invention relates to the technical field of agricultural greenhouse intelligent management, and discloses an agricultural greenhouse full life cycle data driven fine operation and maintenance management system comprising the following steps: respectively defining illumination intensity and air humidity as a driving parameter and a response parameter, and collecting time sequence data of the driving parameter and the response parameter; a driving-response loss degree index is determined by calculating the time-delay cross correlation of the characteristic waveforms of the two, and system vitality early warning is generated based on the evolution trend of the index. According to the method, a traditional steady state monitoring mode is broken through, non-intrusive evaluation of the crop physiological state is achieved by analyzing the dynamic response relation among the environmental parameters, system function decline can be pre-judged when the environmental parameters are not abnormal, and a brand new decision dimension is provided for precise agricultural management.

The invention relates to the technical field of intelligent management, and particularly discloses an intelligent agricultural management system based on image processing, which adopts a computer vision technology based on deep learning to analyze and process farmland images, captures shallow features and deep semantic features of crops, and sends the shallow features and deep semantic features to the field of agricultural management. And semantic feature fusion is carried out on the shallow-layer features and the deep-layer semantic features of the crops to mine abundant crop growth state information, so that abnormal early warning of the crop growth state is realized. In this way, abnormal conditions in the crop growth process can be found in time, the labor cost is reduced, the agricultural production efficiency is improved, and scientific agricultural management is achieved.

The invention discloses a crop growth period monitoring method and system based on an AI algorithm model, and belongs to the technical field of agricultural information, and the method specifically comprises the steps: collecting the weather, growth cycle and soil data of a target crop in a source place and an introduction place; shooting images of the target crop at different growth stages by using an unmanned aerial vehicle, and performing feature marking; by loading and training a target detection model, identifying a growth stage of the crop; inputting real-time invisible light wave band single-channel radiometric calibration image data into the target detection model to obtain growth state and growth period information of crops; combining the introduction environment and the basic growth data, utilizing a time sequence analysis algorithm to predict the growth period of the crops, analyzing the prediction result, comparing the difference between the origin and the introduction site, and adjusting agricultural management measures to adapt to the influence of environmental conditions and genetic factors on the growth period of the crops.

The invention discloses an agricultural greenhouse extraction method based on an improved U-Net model. Belongs to the technical field of greenhouse range remote sensing image extraction. A U-Net network is applied to ground feature extraction operation in a remote sensing image, but the U-Net network is simple in structure and cannot extract effective features in a high-resolution remote sensing image in an environment with complex and diverse surrounding environments, so that the extracted context information is insufficient, and the problems of extraction missing and extraction difficulty occur. A traditional ground feature extraction method mostly takes RGB images as a data source, and the spectral characteristics of ground features are neglected. The method is easy to cause the same-spectrum foreign matter phenomenon, and especially under the complex background condition, target ground objects are difficult to effectively distinguish and extract. According to the method, the aspects of data set construction, feature channel design and a standard U-Net network structure are improved, the technical bottleneck of an existing model in the aspects of feature representation capability and space detail maintenance is broken through, and the method has important research value for promoting precision agricultural development and intelligent agricultural management.

The invention discloses a plant three-dimensional data acquisition device and method fused with a multi-source sensor, and relates to the technical field of plant three-dimensional data acquisition. The carbon rod assembly is used for connecting the two groups of tripods, and the autorotation body can move in the length direction of the carbon rod assembly; the autorotation body can be connected with a three-dimensional laser scanner; the extension ladder is used for carrying a sensor, the extension ladder can be connected to the autorotation body, the extension ladder is provided with a plurality of equipment carrying positions, and the equipment carrying positions are used for carrying and connecting the sensor; through multi-source sensor fusion and lightweight design, the dependence of the prior art on an indoor environment and a single data source is broken through, and an efficient and reliable tool support is provided for field plant phenotype research, precision agricultural management and ecological system monitoring. And the method has remarkable technical advancement and application prospect.

The invention relates to the technical field of smart agriculture, and provides a smart agriculture platform based on big data and Internet of Things, comprising an agricultural environment detection device; a data acquisition and transmission module; an intelligent decision module; the agricultural execution equipment comprises but is not limited to a fan, an insecticidal lamp, an irrigation system and a fertilization system, and the agricultural execution equipment performs automatic operation according to the instruction of the intelligent decision module; the display device is used for displaying the environment monitoring data, the intelligent decision result and the state information of the agricultural execution equipment in real time; the intelligent decision-making module comprises a machine learning algorithm and is used for predicting crop growth conditions and potential pest and disease damage risks according to historical data and current environment data and adjusting agricultural management strategies according to the crop growth conditions and the potential pest and disease damage risks. According to the intelligent agricultural platform, a machine learning algorithm is set in the intelligent decision-making module, and an agricultural management strategy can be adjusted and improved in time, so that the prediction and judgment capability of the intelligent agricultural platform is improved, and healthy growth of crops is guaranteed.

The invention belongs to the technical field of agricultural management, particularly relates to an intelligent agricultural equipment management system and method based on the Internet of Things, and aims to solve the problems that an existing system does not adapt to soil and environment requirements of crops in different growth periods, and management closed loops are lacked. The system adopts a four-layer architecture of'perception-transmission-processing-application ', and integrates a monitoring module, a data processing module, a model training module, an equipment control module and the like to form a complete closed loop of'acquisition-analysis-decision-execution-feedback'. A crop growth influence factor model is constructed in different growth periods, and soil and environment parameter requirements are dynamically matched; an accurate adjustment suggestion is generated based on the model, and equipment is driven to be automatically regulated and controlled; and meanwhile, long-term data storage and model iterative optimization are supported, and an automatic and manual dual-management mode is considered. According to the invention, the whole-cycle precise management of crops is realized, the resource utilization efficiency is improved, the labor dependence is reduced, various planting scenes are adapted, and the quality and efficiency improvement and green development of agricultural production are facilitated.

This application discloses a method, apparatus, equipment, medium, and product for predicting regional crop water productivity, relating to the field of smart agriculture. The method includes inputting historical and future climate data into an ecosystem process model to obtain simulated net primary productivity and simulated evapotranspiration for historical and future times; using a bias mapping method based on the simulated evapotranspiration, actual evapotranspiration, and simulated evapotranspiration for future times to obtain predicted evapotranspiration for future times; training multiple machine learning models based on the simulated net primary productivity, agricultural management data, and grain yield per unit area for historical times to obtain a crop yield prediction model; obtaining the predicted grain yield per unit area for future times based on the crop yield prediction model; and obtaining the regional crop water productivity based on the predicted evapotranspiration and the predicted grain yield per unit area for future times. This application can achieve high-precision, high spatiotemporal resolution, and high-reliability prediction of regional crop water productivity.