34 results about "Digital agriculture" patented technology

The invention discloses an agricultural environment monitoring method and system based on digital agriculture. The method comprises the steps that S1, original environment data signals are collected through a heterogeneous sensor network deployed in a farmland area; s2, performing space-time fusion processing on the original environment data signal to generate a standardized environment state signal; s3, inputting the standardized environment state signal into a pre-trained multi-mode diagnosis model to generate an environment-crop coupling diagnosis signal; s4, according to the environment-crop coupling diagnosis signal, an agricultural operation decision signal is generated, and the agricultural operation decision signal comprises quantitative parameters of the irrigation amount, the fertilization formula or the disaster prevention and control instruction; and S5, converting the agricultural operation decision signal into an equipment control instruction signal, and driving an execution mechanism to complete precise agricultural operation. According to the agricultural environment monitoring method and system based on digital agriculture, the problems of data splitting, extensive decision making and execution lagging in agricultural environment monitoring can be solved.

The invention relates to the technical field of digital agriculture, in particular to a farmer portrait generation method and system based on GIS pattern spots. According to the farmer portrait generation method based on the GIS pattern spots, a farmland plot is locked in real time through a satellite map with the precision of 0.5 m, and farmer production behaviors are collected and analyzed in real time so as to generate a digital farmer portrait. According to the farmer portrait generation method and system based on the GIS pattern spots, decision-making precision can be accurate to a window period of 3 days, meanwhile, early warning of plant diseases and insect pests can be achieved 14 days in advance, the fuel cost of agricultural machinery is reduced, meanwhile, environmental pollution is reduced, supervision efficiency is improved, and great convenience is provided for modern agricultural development.

The invention provides a water-carbon collaborative scheduling low-carbon ecological cycle agriculture system and method, relates to the field of ecological cycle agriculture, and aims to determine the range of a target agricultural system and generate a digital agricultural ecological model according to the range of the target agricultural system; corresponding ecological parameters are collected in the range of the target agricultural system, and the digital agricultural ecological model is adjusted in real time according to the ecological parameters; dividing the target agricultural system into a plurality of subsystems, and analyzing carbon sink parameters in each subsystem and the target agricultural system to obtain a carbon sequestration index of the current target agricultural system; a carbon sink target is set, a digital agricultural expression model is adopted for simulation, and a circulation strategy needed for achieving the carbon sink target is analyzed; the invention discloses an agricultural system for realizing water-carbon cooperative scheduling according to a circulation strategy. According to the water-carbon cooperative scheduling low-carbon ecological cycle agriculture system and method, cooperation of maximization of agricultural carbon sink and efficient utilization of water resources is achieved.

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 relates to the technical field of agricultural planting, in particular to digital agricultural light supplementing equipment for agricultural planting, which comprises a mounting plate main body, a light supplementing assembly, an angle adjusting assembly and a limiting connecting assembly, the light supplementing assembly is mounted below the mounting plate main body, and the angle adjusting assembly is mounted at the bottom end of the mounting plate main body. A limiting connecting assembly is mounted at the top end of the mounting plate main body; the light supplementing assembly comprises a lifting disc, a first motor, a winding wheel, a connecting rope and a first connecting plate. The angle adjusting assembly is adopted, the light supplementing structures can be adjusted, so that the light supplementing range is adjusted, the number of the light supplementing structures does not need to be increased, the agricultural planting cost is reduced, and the light supplementing effect is improved; and the angle adjusting assembly is adopted, the illumination angle can be adjusted, light supplementing treatment can be conducted on the shadow of the plant, manual auxiliary light supplementing is not needed, the light supplementing burden is reduced, and the light supplementing effect is improved.

The invention relates to the technical field of agricultural management, in particular to a digital agricultural automatic control management system, which comprises the following steps of: acquiring a plant production condition picture in a region by using a satellite, segmenting the picture, dividing the picture into different regions, numbering the regions, and storing the numbered regions into a database; utilizing a GPS positioning technology to match farmland data sensors into the divided areas; obtaining a plant growth image training set, establishing a plant growth prediction model of an ST-LSTM architecture, and inputting the plant growth images into the prediction model to obtain a trained model; the method comprises the steps that a satellite picture is divided for three times, the divided picture is input into a trained model, a plant growth state prediction result is obtained, a plant growth model is compared according to the plant growth state prediction result, the plant growth state is judged, a corresponding area is matched according to the result, and nutrients are supplemented in time according to data. Pictures are finely divided, so that the soil condition of each planting area is conveniently obtained, and a corresponding cultivation scheme is provided.

The application provides a water-carbon coordinated scheduling low-carbon ecological circulation agricultural system and method, relates to the field of ecological circulation agriculture, and specifically aims at the range of a target agricultural system, generates a digital agricultural ecological model according to the range of the target agricultural system; collects corresponding ecological parameters in the range of the target agricultural system, and adjusts the digital agricultural ecological model in real time according to the ecological parameters; divides the target agricultural system into multiple subsystems, analyzes carbon sink parameters in each subsystem and the target agricultural system, and obtains a carbon fixation index of the current target agricultural system; sets a carbon sink target, simulates by using the digital agricultural ecological model, analyzes a circulation strategy required for realizing the carbon sink target, and realizes the water-carbon coordinated scheduling agricultural system according to the circulation strategy. The water-carbon coordinated scheduling low-carbon ecological circulation agricultural system and method can realize the cooperation of the maximization of agricultural carbon sink and the efficient use of water resources.

The invention discloses an iron-nitrogen compound application method and system based on big data analysis, and belongs to the field of digital agriculture, the method comprises the following steps: collecting multi-source heterogeneous data of a target farmland, sending the collected multi-source heterogeneous data to a cloud, and constructing a multi-source heterogeneous database in the cloud; data in the multi-source heterogeneous database is called through an iron-nitrogen dynamic application model deployed in the cloud to obtain an iron-nitrogen composite application strategy, an optimal decision scheme is issued to a geographic information system, the geographic information system divides a target farmland into a plurality of grid management units, and the grid management units are used for managing the target farmland. And constructing fertilizer application data based on the information of the optimal decision scheme, and issuing the fertilizer application data to a control system of the automatic agricultural machine, or pushing the fertilizer application data to a user through a user terminal as a basis for manual operation. The invention has beneficial effects.

The utility model provides a digital agricultural intelligent spraying system based on a photoelectric tracking technology, relates to the technical field of irrigation and spraying equipment and control, and mainly solves the problems that an existing agricultural spraying system is poor in spraying irrigation effect and not accurate enough in spraying decision, the environment is harmed due to excessive use of pesticides, the spraying precision is poor, and the waste condition is serious. The system comprises an intelligent controller, an irrigation facility and a pesticide spraying facility. By arranging a humidity sensor, a temperature sensor, a corresponding intelligent controller and a corresponding irrigation facility, precise irrigation can be achieved; and a visual data analysis module, a corresponding region positioning module and a pesticide spraying facility are arranged, so that precise pesticide application is realized, blind large-area spraying is avoided, the pesticide can be utilized most reasonably, the usage amount of the pesticide is remarkably reduced, the cost of purchasing the pesticide is reduced, and the yield and quality of crops are improved.

The invention discloses a digital agricultural Internet of Things terminal hot standby management method and system based on the entropy reduction principle, and the method comprises the steps: receiving life signals transmitted by a first control unit and a second control unit, selecting one of the first control unit and the second control unit as a host when the two life signals are valid, and transmitting the selected one to the host; a corresponding interface of the control module is connected with an interface of an agricultural Internet of Things continuous terminal object, and the other one of the first control unit and the second control unit is used as a standby machine; and life signals sent by the host and the standby machine are detected in real time, when the life signals sent by the host are invalid and the life signals sent by the standby machine are valid, the standby machine is used as a new host, and the corresponding interface of the control module of the standby machine is connected with the interface of the continuous terminal object of the agricultural Internet of Things. The overall reliability of the system can be improved.

The invention discloses a monitoring system for standardized management of farmland, which relates to the technical field of farmland management, and constructs a farmland multi-source information model by acquiring multi-dimensional monitoring parameters including meteorology, soil, water quality and crop images, and compares the farmland multi-source information model with a preset standardized management rule base to obtain a standardized management result. Generating a difference value to identify an abnormal monitoring point and an abnormal type thereof; associating the abnormal information with a geographic layer, generating a visual management map, synchronizing the visual management map to a remote terminal, and supporting graphical display and control operation; meanwhile, the model is dynamically updated based on historical monitoring data and a current difference value, the abnormal response capability and the self-adaption level are improved, and intelligentization, standardization and closed-loop optimization of farmland management are achieved; the method is suitable for multi-crop and multi-plot digital agricultural scenes.

The invention relates to the technical field of ecological monitoring and digital agriculture, in particular to a grassland ecological restoration digital evaluation system and method. The method comprises the following steps: collecting a remote sensing vegetation index sequence and ground quadrat biomass data, and aligning the remote sensing vegetation index sequence and the ground quadrat biomass data according to a unified time index to obtain a synchronous observation sequence; constructing an adaptive calibration relationship between the vegetation index and the ground quadrat biomass based on the synchronous observation sequence; therefore, by constructing a full-link digital process from multi-source data fusion, adaptive calibration, multi-scale dynamic feature extraction to quantitative score generation, the problems that traditional grassland ecological restoration assessment depends on manual interpretation, the static model deviation is large, and the process is non-traceable are solved; the objectivity, the accuracy, the timeliness and the decision support capability of the evaluation result are improved.

A soil nutrient detector for digital agriculture, comprising a workbench (1), characterized in that a water tank (24) is mounted on the top of the workbench (1), a water supply line (26) is connected to a side surface of the water tank (24), a water pump (25) is mounted on the outer surface of the water supply line (26), wherein an end surface of the water supply line (26) is fixedly connected to an annular sprinkler line (27); a passage channel (28) is formed below the annular sprinkler line (27), a mounting bracket (29) is attached to the inner wall surface of which a connecting rod (30) is attached to the outer surface of the mounting bracket (29); a scraper blade (31) is fixedly connected to the outer surface of the connecting rod (30); a cleaning brush (32) is attached to the inner surface of the scraper blade (31).

The invention provides a variety region suitability intelligent evaluation method and system, and relates to the technical field of digital agriculture. The method comprises the following steps: synchronously collecting registration information of an environment sensor, an imaging device and a variety registration database; performing space-time registration and cleaning on the acquired data to obtain a cleaned data set; extracting normalization, time window statistics and domain derivation features from the data, and constructing a unified feature data set; inputting the data into a pre-trained cross-residual attention network model, and outputting adaptability evaluation of each variety in the target area; sorting the evaluation results into a suitability catalogue and pushing the suitability catalogue to a user side; and collecting feedback of actually measured yield, growth period and the like, and iteratively updating model parameters. According to the invention, through multi-source data fusion, dynamic optimization and closed-loop learning, efficient, scientific and real-time selection of regional varieties is realized.

The application discloses an intelligent digital aquaculture management system and method, and particularly relates to the technical field of digital agriculture management, and is used for solving the problem that the long-term coordination of local dynamic adjustment and overall production plan is insufficient in the existing breeding management, which causes the production process to gradually deviate from the expected target; the breeding production plan, production state data and management operation data are acquired and continuously monitored; the response logic between the management operation and the production state is analyzed to determine the deviation trend; when the deviation occurs, the historical and current causal relationship networks are constructed and compared to identify the change of the key causal relationship; meanwhile, the uniformity change mode of the individual size in the breeding population is analyzed; the specific management adjustment instruction is generated and executed according to the above analysis results; finally, the breeding production plan is dynamically updated according to the instruction execution effect; the closed-loop adaptive management of the breeding process is realized, and the predictability and stability of the production result are ensured.

This invention discloses a method and system for posture estimation and cutting point localization of rhizome medicinal materials, belonging to the field of digital agriculture technology. The method includes: acquiring training data; constructing a YOLO model; training recognition and localization models for each part; inputting the RGB image of the rhizome medicinal material to be identified into the model; preprocessing the image using a multi-frame optimization strategy, obtaining the contour of the largest connected region after denoising, and calculating the centroid; using a geometric constraint strategy, recovering the root-stem transition zone detection box based on the spatial position of the root and stem, obtaining its largest connected region contour, and calculating the centroid; based on the root-stem transition zone and the optimized stem and root detection boxes and centroids, using a root-stem opposite-direction clipping algorithm and weighted least squares fitting iteration to obtain the posture angle of the rhizome medicinal material; finally, locating the cutting point of the rhizome medicinal material based on the posture angle and the structural geometric features of the root-stem transition zone. This invention offers the advantages of accurate, efficient, and rapid posture estimation and cutting point localization of rhizome medicinal materials, enabling automated root-stem separation and processing of rhizome medicinal materials.

The application belongs to the technical field of digital agriculture, and particularly relates to a blockchain traceability data management platform for digital agriculture, wherein the system comprises an agricultural product supply chain informatization module, a trust value quantification module, a two-dimensional code generation module, a local two-dimensional anti-fraud verification module and a traceability query module. The scheme constructs a distributed blockchain ledger network suitable for the agricultural product supply chain, allocates unique on-chain identity and permission to each link participant, generates a basic two-dimensional code bearing core data through a two-dimensional code generation library, triggers a smart contract to calculate a trust value in real time and uploads the trust value to the chain during traceability query, calculates a local hash value by extracting an on-chain identity embedded in a two-dimensional code to be detected, and compares the local hash value with a preset authorization list to complete legality verification, realizes real-time two-dimensional anti-fraud verification of the two-dimensional code without network dependence, can accurately trace and immediately intercept a counterfeit behavior, and provides a full-process anti-counterfeiting and traceable trust guarantee for agricultural product traceability.

The invention relates to a low-energy-consumption digital agricultural factory design optimization method and system adopting the Internet of Things, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining the type of a planted plant and the growth stage of the plant; analyzing the types of the planted plants and the growth stages of the plants so as to determine the normal plant state; obtaining a planted plant picture; analyzing the types of the planted plants, the growth stages of the plants, the pictures of the planted plants and the states of the normal plants so as to determine the light intensity and the light color required by the plants; and according to the light intensity required by the plant and the light color required by the plant, controlling a preset double-cavity composite dimming greenhouse to adjust the light. The dimming system has the effect of reducing the energy consumption of the dimming system.

The invention discloses an intelligent digital aquaculture management system and method, particularly relates to the technical field of digital agricultural management, and is used for solving the problem that a production process gradually deviates from an expected target due to insufficient long-term collaboration of local dynamic adjustment and an overall production plan in existing aquaculture management. A breeding production plan, production state data and management operation data are acquired and continuously monitored; analyzing response logic between the management operation and the production state to determine a deviation trend; when deviation occurs, a historical causal relationship network and a current causal relationship network are constructed and compared to identify the change of the key causal relationship; meanwhile, the uniformity change mode of the size of individuals in the breeding group is analyzed; generating and executing a targeted management adjustment instruction according to the analysis result; and finally, dynamically updating the breeding production plan according to the instruction execution effect. Closed-loop self-adaptive management of the breeding process is realized, and predictability and stability of production results are guaranteed.

The utility model relates to the technical field of agriculture and animal husbandry waste fermentation, in particular to a digital agriculture and animal husbandry waste fermentation device which comprises a fermentation box, the device further comprises lower supporting plates, an upper right side plate, an upper left side plate, a [-shaped supporting frame, an X-shaped supporting frame, a mounting plate, an air cylinder, a mounting frame, a motor and a hook. The two lower supporting plates are symmetrically arranged at the front ends of the left side and the right side of the fermentation box. The air cylinder drives the mounting frame to move towards the classified fermentation boxes, the mounting frame drives the hook to move towards the classified fermentation boxes until the hook is attached to the front sides of the classified fermentation boxes, then the motor drives the hook to rotate until the hook is buckled with the grip, and then the air cylinder drives the mounting frame to move towards the mounting plate. Meanwhile, the classified fermentation boxes are pulled out of the fermentation box through buckling of the hooks and the grips until the classified fermentation boxes are located at the top of the X-shaped supporting frame, finally, the right-angled U-shaped supporting frame is driven by the lead screw to drive the classified fermentation boxes to move downwards to the ground, and the function of taking goods at a fixed point is achieved.

This application relates to the field of computer information processing, and provides a method for constructing a geographic information system (GIS) application system for digital agriculture, and a computer program product. The method includes: integrating a GIS function with a digital agriculture application demand, and dividing a GIS application for digital agriculture into a plurality of components based on an integration relationship; developing the components separately and constructing a digital agricultural GIS application component library; and selecting a component from the digital agricultural GIS application component library for assembling, and building a navigation module and various functional modules for the GIS application system for digital agriculture to complete construction of the GIS application system for digital agriculture. This application can adapt to various different digital agriculture application scenarios and personalized user demands.

The invention provides a GIS-based digital agricultural comprehensive monitoring management system and method, and relates to the technical field of digital agricultural management, and the method comprises the steps: collecting soil moisture content data, meteorological data, crop growth data and agricultural machinery operation plan data of each detection agricultural node in a target agricultural region, and carrying out the data preprocessing; on the basis of a GIS technology and according to the preprocessed data, constructing a layered space graph layer of each detection agricultural node in the target agricultural area; constructing a regional integrated visual model according to the layered space layer; performing layer overlay analysis on the layered space layers in the area integrated visual model to obtain a comprehensive analysis index of the target agricultural area; based on a deep learning algorithm, according to the comprehensive analysis index of the historical acquisition period, obtaining a predicted planting state of the corresponding target agricultural region; and formulating a precise management scheme according to the predicted planting state.

The invention relates to the technical field of information technology and digital agriculture, in particular to an egg market information generation method and system, and the method comprises the steps: receiving to-be-pushed market information, and determining a push time window based on the content attribute of the market information; determining the pushing time of the target user according to the active time of the target user and the pushing time window; generating a keyword according to the content of the market information to be pushed, and generating a personalized keyword sequence based on the keyword and the portrait attribute of the target user; determining an adaptive syntactic template based on the personalized keyword sequence; generating a natural language summary sentence based on the market information, the personalized keyword sequence and the adaptive syntactic template; and within the pushing time, pushing market information containing the thumbnail display natural language summarized sentence to the target user. The method has the advantages that industrial knowledge, user portraits and intelligent algorithms are deeply fused, and pushing accuracy and user experience are improved.

The invention relates to the technical field of agricultural machinery equipment, in particular to an integrated positioning method for digital agricultural machinery navigation, which comprises the following steps: step 1, data acquisition; step 2, data processing and analysis; 3, establishing a model; and step 4, planning an advancing route and monitoring a planting environment. According to the invention, the visual positioning technology, the GPS positioning technology and the laser ranging technology are combined, so that the positioning precision is ensured, the positioning error is reduced, the acquisition equipment can monitor the obstacle condition in the advancing route in real time in the advancing process, and accurate obstacle avoidance information is provided for the acquisition equipment.

The invention discloses an unmanned aerial vehicle DSM-based paddy field positioning model construction method, positioning method and system, and relates to the technical field of agricultural monitoring camera geographic calibration based on unmanned aerial vehicle images. The problems that in the prior art, in high-precision geographical calibration of a fixed PTZ camera of a paddy field, manual control points are depended, the efficiency is low, and an existing unmanned aerial vehicle technology is not applicable, so that a feasible scheme is lacked are solved. The method comprises the following steps: acquiring digital surface model DSM data of a paddy field area through a sensor carried by an unmanned aerial vehicle; three-dimensional morphological characteristics of the paddy field are extracted based on DSM data, and an analysis model special for paddy field identification and boundary positioning is constructed; the invention also provides a construction method of the model, a positioning method based on the model and a system for realizing the positioning method. The method is suitable for precision agriculture and intelligent farm management, and especially suitable for the fields of agricultural machinery automatic operation, farmland informatization, digital agricultural construction and the like.

The present invention discloses a method, system, electronic device and medium for determining the weight of strawberry fruits, relating to the technical field of digital agriculture. The method includes: obtaining the overall images of a plurality of target strawberries; applying a single-stage object detection model to segment each target strawberry in the overall images to obtain multiple images of single target strawberries; and inputting the multiple images of single target strawberries into a strawberry mass estimation model to obtain the fresh weight of each single target strawberry. The present invention can improve the accuracy of estimating the weight of strawberry fruits.

This invention provides a method, apparatus, medium, and device for discovering time-series causal relationships in digital agricultural information, belonging to the field of agricultural information technology. The apparatus includes: an acquisition module for acquiring a dataset comprising multiple observable time series of digital agricultural information; a decomposition module for expanding the dataset into multiple window representations and decomposing the time-series causal mechanism of the dataset into a summation form with mechanism-invariant and time-invariant modules; a first calculation module for outputting causal numerical transformation relationships through the mechanism-invariant modules; a second calculation module for outputting a window causal graph through the time-invariant modules; and a training module for acquiring the time-series causal relationships of different variables in the digital agricultural information based on the causal numerical transformation relationships and the window causal graph. This invention overcomes the problems of diverse noise and small data volume in digital agricultural information time-series data, and has promising application prospects in agricultural mechanism research.

The invention discloses a multi-modal data-driven agricultural product producing area comprehensive environment management method and system, and belongs to the field of digital agriculture, and the method comprises the steps: collecting multi-modal data of an agricultural producing area environment, obtaining a multi-modal environment data set, and transmitting the multi-modal environment data set to a cloud; preprocessing the collected multi-modal environment data set to obtain a unified spatio-temporal data set; taking the unified spatio-temporal data set as input, and mining deep information of the data set by using a crop growth narrative and predictive analysis algorithm deployed at a cloud end to obtain an accurate quantitative index; and decomposing the accurate quantitative index to obtain a control instruction, and issuing and executing the control instruction. According to the method, a data island among multi-source data is broken, and a complete information space is constructed for an agricultural environment.

The invention discloses a bee-keeping intelligent optimization method and system with adaptive bee species and nectar sources, and aims to solve the problems of nectar source information lag, blindness in bee species selection, low efficiency in transition decision, extensive bee colony management and the like in traditional bee-keeping. According to the method, a honey source space-time database is constructed through fusion of remote sensing and ground survey, and the honey secretion peak period is predicted in combination with meteorological and phenological models; establishing a bee species characteristic knowledge base covering dimensions such as collection radius, flowering phase adaptability, nectar source preference and the like; a bee species-nectar source adaptation degree evaluation model is constructed based on machine learning, and multi-target intelligent recommendation is achieved; the integrated Internet of Things sensor monitors the swarm state in real time, and dynamically optimizes a transition path and a time window; and services such as a nectar source map, yield prediction and abnormity early warning are provided through a user visual platform. The system forms a'sensing-analysis-decision-execution-feedback 'closed loop, so that bee colony loss is reduced, invalid transition is reduced, and ecological sustainable bee keeping is promoted. The method is suitable for smart bee industry, digital agriculture and rural revitalizing application scenes.

This invention provides a grassland carbon sink management decision support system based on a multi-factor weighted regression model, belonging to the field of digital agriculture and ecological information decision technology. The system comprises: a multi-source data integration and processing module that standardizes multi-source data; a multi-factor weighted regression model engine module that employs the multi-factor weighted regression model algorithm; a carbon sink accounting and dynamic assessment module that translates the output of the MWRM model engine module into business language; a scenario simulation and decision support module that provides carbon sink hotspot identification and protection priority delineation, grazing pressure optimization analysis, and pre-assessment of management measure benefits, capable of directly generating quantitatively assessable grazing ban plans, rotational grazing schemes, and engineering layout suggestions based on spatial heterogeneity; and a visualization and interactive output module that provides a graphical interface on a web or desktop to present the results of all the above modules. This invention establishes an automated and intelligent decision support system integrating high-precision accounting, scenario simulation, and spatial planning.