189 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 provides a deep learning-based farmland disease and pest monitoring and prevention method, system and device, and the method comprises the steps: carrying out the data collection of a target farmland through a plurality of sensors, obtaining the sensor data corresponding to the target farmland, carrying out the multi-level data preprocessing, obtaining the structured sensor data of the target farmland, and carrying out the multi-level data preprocessing of the structured sensor data; and performing data analysis on the crop growth image to obtain growth parameters of crops, performing pest recognition, positioning and counting on the pest situation information image through a deep learning algorithm to obtain a pest population dynamic state, constructing a pest occurrence prediction model to obtain a pest occurrence probability, and generating early warning information of a pest control opportunity. And finally obtaining a farming management decision scheme of the target farmland. According to the method, environmental factors, crop growth conditions and insect pest information are comprehensively considered, the occurrence probability of target farmland diseases and insect pests is predicted, corresponding prevention and control measures are provided, and reference is provided for farmers and managers.

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 discloses a multi-modal data fusion agricultural crop intelligent monitoring system and method, and aims to realize accurate monitoring of crop growth and soil health through comprehensive analysis of image and audio data. The system comprises an image processing module, an audio acquisition module, an audio processing module and an early warning module. The image processing module acquires crop images in real time and analyzes the growth stage of crops so as to determine the active depth range of the root system. The audio acquisition module acquires audio signals in soil, and the audio processing module performs preprocessing, screening and feature extraction on the signals through an audio separation technology so as to obtain target audio information related to root activity. And further calculating an evaluation parameter based on a spectrum parameter obtained by audio analysis. And the early warning module generates corresponding early warning information and coping strategies according to whether the state parameters are abnormal or not. According to the invention, the accuracy and real-time performance of crop and soil health monitoring are improved, and more accurate decision support can be provided for agricultural management.

The invention provides a method and a system for estimating organic carbon reserve change of soil in a vegetable field in a short time, and belongs to the technical field of soil measurement. The method comprises the following steps: acquiring net ecosystem carbon income and expenditure (NECB); and estimating the organic carbon reserve of the vegetable field soil by using net ecosystem carbon income and expenditure (NECB). According to the method, the organic carbon reserve of the soil in the vegetable field can be quickly estimated, the influence of different farming measures on the organic carbon reserve of the soil in the vegetable field in a short period can be known, and farmers and agricultural managers can optimize management measures such as fertilization, irrigation and planting modes; carbon emission and fixed conditions of a farmland ecosystem can be determined, so that the speed and the degree of climate change can be better predicted, and reasonable vegetable planting management measures can be taken in time.

The invention discloses an intelligent agriculture platform construction method based on big data analysis and a visual model, and the method comprises the steps: arranging a sensor network, and monitoring the key environment parameters of a farmland in real time; using the unmanned aerial vehicle to shoot crop images; according to the shot crop image, identifying a part with pest and disease damage characteristics in the crop image; deducing the age of the crop leaf according to the shot crop image; a cloud platform is built, farmland related data is displayed in an integrated mode through the cloud platform, and decision suggestions are provided based on big data analysis according to all data input into the cloud platform. The system integrates multiple functions such as farmland data display, unmanned aerial vehicle fixed-point automatic cruise, disease and pest recognition and early warning, agricultural question and answer assistant and the like, and provides one-stop intelligent agricultural management service for farmers. The platform provides scientific guidance suggestions for agricultural production based on big data analysis, and assists in improving yield and quality.

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 discloses a method and a system for detecting metabolism kinetics of organic carbon and microbial carbon in deep soil, and relates to the technical field of soil science and environmental science. Comprising the following steps: acquiring the content and structural characteristics of organic carbon in the soil of the rice field, and determining the composition, function and dynamic response mechanism of microbial communities to environmental change; setting a control group and a treatment group, and obtaining soil organic carbon reserve change rates, CO2 emissions and influence factors in the control group and the treatment group at different time points; and constructing a data analysis model according to the soil organic carbon reserve change rate, CO2 emission and influence factors in the control group and the treatment group at different time points, and generating a comprehensive report of deep soil organic carbon and microbial carbon metabolism kinetics. According to the invention, a scientific basis is provided for optimizing agricultural management and coping with climate change, the positive influence of biomass charcoal addition on soil health is disclosed, and the development of sustainable agriculture is promoted.

The invention provides an improved YOLOv10 network for detecting a small target in a remote sensing image. The improved YOLOv10 network is used for enhancing the detection performance of the small target in the remote sensing image. Existing first-stage detection methods, such as YOLOv10, can realize rapid detection, but still face challenges in small target detection under a complex background. According to the method, the HATSC module is constructed by introducing the mixed attention converter and the space and channel reconstruction convolution module, so that the feature extraction capability of the model on local details under a complex background is effectively improved. Meanwhile, the normalized Wasserstein distance is used as a part of a loss function, so that the sensitivity to the position deviation of the small target is reduced. The HATSC module is integrated in the HATSC-YOLOv10 network, optimization is carried out in the stages of feature extraction, feature fusion and detection heads, the detection precision and efficiency of small targets in remote sensing images are improved, and the method is suitable for multiple fields such as environment monitoring, agricultural management and urban planning.

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 relates to the technical field of man-machine interaction, and particularly discloses a man-machine interaction implementation method and system for an embedded system, and the method comprises the steps: delimiting a crop growth region, dividing the crop growth region into n monitoring sub-regions, collecting the soil data, meteorological data and crop physiological index data of each sub-region through an embedded system integrated multi-element sensor, the soil data is stored in a system storage unit, a soil irrigation demand index is obtained by analyzing the soil data, and the soil irrigation state is judged; meanwhile, crop growth irrigation demand indexes are calculated according to crop physiological indexes, crop growth irrigation states are defined, crop growth soil irrigation evaluation coefficients are analyzed on the basis of integration of the crop growth irrigation demand indexes and the crop growth irrigation states, abnormal data are screened, and early warning is conducted in time to agricultural management personnel; and finally, constructing a human-computer interaction interface, displaying soil and crop irrigation states and sub-region numbers, providing scientific irrigation guidance and decisions, powerfully promoting precision of intelligent agricultural irrigation, and ensuring efficient and sustainable development of agriculture.

The invention discloses a large-scale irrigation and fertilization system optimization method based on a remote sensing leaf area index, and the method comprises the steps: calibrating crop parameters of a field scale crop model based on field test data; then, by using an assimilation algorithm based on the remote sensing LAI, determining sensitive crop parameters of a regional scale, and verifying the rationality of determining the regional crop parameters by using the remote sensing LAI through regional crop growth simulation; and finally, constructing a regional scale crop model, and combining the regional scale crop model with a multi-target genetic algorithm to optimize the regional INS of different typical rainfall years. The regional INSs established by estimating regional crop parameters based on remote sensing data can effectively guide irrigation and nitrogen fertilizer application plans, the utilization efficiency of agricultural resources is improved, and an important reference is provided for regional agricultural management.

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 provides a full-period management platform and management method for a novel agricultural operation subject, and relates to the technical field of agricultural management, and the method comprises the steps: carrying out the risk prediction of a target agricultural operation subject; inputting the basic information of the agricultural subject into a full-cycle agricultural supply chain; performing same-level node risk propagation prediction on the target agricultural operation subject based on the agricultural subject node; performing different-level node risk propagation prediction on the target agricultural operation subject based on the agricultural subject node; and performing risk early warning on the target agricultural operation subject based on the agricultural subject risk first coefficient, the agricultural subject risk second coefficient and the agricultural subject risk third coefficient. According to the method and the device, the technical problem that the early warning and coping capability of an agricultural operation subject to unnatural factor risks in a complex supply chain is poor due to the fact that monitoring and analysis depend on a single factor in the prior art can be solved, and the technical effect of improving the early warning capability and coping efficiency of the agricultural subject when facing various risks is achieved.

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.

To allow optimal agricultural chemicals to be selected from the viewpoint of safety in each of areas.SOLUTION: An agricultural management system for proposing application of agricultural chemicals includes: a model storage unit which stores, for each of agricultural chemicals and modes of application thereof, an evaluation model in which region-specific regional information is associated with an evaluation value according to the risk of application of the agricultural chemicals in the region; an evaluation unit which calculates, on the basis of the evaluation models and target region information which is regional information of a target region, the evaluation value in the target region, for each of agricultural chemicals and the modes of application thereof; and a proposal unit which proposes application of the agricultural chemicals, on the basis of the calculated evaluation values. The evaluations values are one or more evaluation values according to at least two of a consumer risk, an exposure risk, and a biological impact risk.SELECTED DRAWING: Figure 4

The invention discloses a tomato axillary bud real-time identification, measurement and removal point positioning method. The method comprises the following steps: acquiring RGB and depth images of a tomato plant by using a mobile phone or RGB-D sensor equipment; a lightweight model is adopted to carry out instance segmentation on the RGB image, and axillary buds and branches are identified and segmented. And then, aligning the binary mask of the axillary bud with the depth image, constructing corresponding three-dimensional point cloud data, and calculating the length of the axillary bud and judging the removability based on whether the point cloud and the axillary bud region contain axillary bud bifurcation. And after determining that the axillary buds can be removed, further determining axillary bud removal points. By means of the method, agricultural management equipment can rapidly, automatically and accurately obtain the axillary bud length and judge the removability of the axillary bud, and therefore tomato pruning operation is efficiently completed.