9735 results about "Irrigation" patented technology

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

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

The invention relates to the technical field of crop production, in particular to a big data-based crop yield prediction and synergistic management system, which comprises a data acquisition module, a data processing module, a multi-scale crop growth prediction module and a plant stomata dynamic adjustment and transpiration monitoring module, the data acquisition module acquires multi-dimensional agricultural data; the data processing module receives and preprocesses the collected multi-dimensional agricultural data; the multi-scale crop growth prediction module predicts growth trends of crops in different time dimensions; and the plant stomata dynamic adjustment and transpiration monitoring module analyzes and predicts the transpiration rate of the crops, and adjusts the irrigation amount in real time in combination with the prediction result of the growth trend of the crops. According to the invention, the sustainability of water resource utilization is improved, the waste of agricultural resources is reduced, and finally the benefits of agricultural production and the sustainability of the environment are improved.

The invention provides an intelligent management method for natural resource engineering data, and relates to the technical field of data processing, and the method comprises the steps: inputting a time-space coupling model into real-time weather forecast data, simulating water distribution schemes under different rainfall scenes, automatically recognizing a river reach position of which the ecological flow is lower than a design threshold value, and carrying out the calculation of the river reach position. In combination with a historical water replenishing strategy case library, generating a multi-objective optimization scheduling scheme including a water replenishing time sequence, a water replenishing path and a water replenishing amount; three monitoring points are arranged at a downstream section of an ecological protection area, a flood control section and a canal head of an irrigation area in a drainage basin, and ecological flow, flood control section water level and canal water level data are monitored in real time respectively; and dynamically correcting the water supplementing time sequence, the water supplementing path and the water supplementing amount in the multi-objective optimization scheduling scheme to obtain a corrected scheduling scheme. According to the method, refinement and toughness of drainage basin water resource management can be improved.

The invention relates to the technical field of agricultural information management, and particularly discloses an agricultural information management system and method based on a big data platform, and the method comprises the steps: firstly deploying a multi-source data collection module at an edge calculation node, and obtaining and standardizing the soil moisture content, meteorological environment and equipment operation data in real time; secondly, constructing a local dynamic irrigation strategy model, and realizing multi-objective optimization through a reinforcement learning algorithm; establishing a federated learning framework at the cloud, dynamically distributing node weights by adopting an attention mechanism, and realizing model aggregation of privacy protection in combination with secure multi-party computing; an optimal irrigation instruction is generated through a multi-source data fusion engine, and a three-level response exception handling mechanism is established; and finally, a closed-loop feedback system containing short-term incremental learning and long-term architecture optimization is formed. The corresponding management system comprises six functional modules, namely a data acquisition module, a local modeling module, a federated learning module, a real-time decision-making module, an abnormal monitoring module and a closed-loop optimization module.

The invention belongs to the technical field of intelligent agricultural data analysis, and relates to a crop identification and growth process prediction method and system based on multi-modal data. The method comprises the following steps: collecting growth process images and multi-modal environment data of crops in each growth stage; constructing a crop feature data set; constructing a sample data set; training to obtain a crop type identification model; obtaining a crop type identification result; generating a fusion feature vector; performing data enhancement processing; constructing a long-short-term memory network for training to obtain a growth stage prediction model; and predicting the growth stage of the crops. Crop type identification is performed by constructing a deep neural network model, feature fusion is performed on crop growth process image features and multi-modal environment data, crop growth stage prediction is performed by using a long-short-term memory network, fertilization, irrigation and pest control measures are optimized in combination with the multi-modal environment data, and the crop growth stage prediction accuracy is improved. The crop yield and quality are improved.

The invention provides a multi-modal intelligent irrigation decision algorithm based on causal inference, and the algorithm comprises the steps: carrying out the knowledge distillation of a pre-trained image feature extractor based on causal inference, and fully extracting the features of crop time sequence information and image information; according to the method, multi-modal data is considered, and heterogeneous data is deeply fused. Through knowledge distillation of an image feature extractor, the reasoning speed of the model is higher, the prediction accuracy is higher, end-to-end water shortage state prediction can be carried out based on greenhouse environment data, root system environment data and crop images, and a prediction result can be used for guiding crop irrigation and correcting the irrigation frequency and the irrigation amount of crops. And a more intelligent and comprehensive irrigation decision is realized.

The invention relates to the field of intelligent irrigation, in particular to an intelligent irrigation method and device based on the Internet of Things, and the method comprises the steps: constructing a soil standard model, obtaining soil data in real time, inputting the soil data into the soil standard model, and outputting a region division result through the soil standard model; based on a regional division result, judging whether the soil data meets an irrigation condition or not; when the soil data meets the irrigation condition, acquiring meteorological data, water pressure data and historical irrigation data; according to the meteorological data, the water pressure data and the historical irrigation data, irrigation parameters needed by the corresponding area of the soil are predicted; and determining corresponding irrigation behaviors in combination with the soil data and the irrigation parameters of the corresponding areas. Soil data is monitored in real time, so that the system can dynamically adjust an irrigation plan, invalid irrigation after rainfall is avoided, a targeted irrigation strategy is made according to the soil humidity level and topographic features, slope water and soil loss is prevented, and it is ensured that the flat ground is prevented from being too wet to cause root system oxygen deficit.

The invention relates to the technical field of agriculture, in particular to a corn stress resistance environment monitoring and management method, which comprises the following steps of S1, collecting environment data of a corn planting area in real time; s2, transmitting the environment data to a data center through a wireless network; s3, preprocessing the environment data transmitted to the data center; s4, establishing an adversity stress assessment model based on the preprocessed environmental data; s5, evaluating the stress resistance of the corn under different environmental stresses; s6, predicting the type and strength of adversity stress which will occur in the future and the influence of the adversity stress on the corn, and forming stress response prediction; and S7, generating a corn growth management decision suggestion. According to the invention, through real-time environment monitoring and accurate adversity stress assessment, in combination with growth characteristics and weather prediction of corn, irrigation, fertilization and pest control strategies are dynamically adjusted, so that accurate management of corn growth is realized, stress resistance is improved, resource utilization is optimized, and yield is increased.

The invention relates to the field of saline-alkali soil water-fertilizer regulation, in particular to a saline-alkali soil soybean water-fertilizer-salt regulation dynamic decision-making method and system. A saline-alkali soil soybean water, fertilizer and salt regulation dynamic decision-making system comprises a region division module, a data monitoring module, an irrigation initial decision-making module, a fertilization initial decision-making module, an irrigation decision-making acquisition module and a fertilization decision-making acquisition module. By integrating an advanced sensor technology and an intelligent decision-making model, key parameters are accurately monitored, and the total amount of irrigation and fertilization required in a specific time window in the future is calculated in combination with crop growth stages and historical water and fertilizer records; meanwhile, the irrigation and fertilization frequency can be dynamically adjusted according to soil and environment data monitored in real time, waste of water resources and fertilizer is avoided, the possible soil salinization problem is solved, soil health can be maintained, and crop growth is promoted.

The invention relates to the technical field of environment regulation, in particular to a corn cultivation environment intelligent regulation method based on multi-sensor control, which comprises the following steps: S1, generating a soil moisture three-dimensional distribution diagram in real time through humidity sensor arrays arranged at different depths of a corn root zone; s2, combining infrared temperature data of the canopy and the three-dimensional distribution diagram of the soil moisture, and calculating a moisture motion mismatch degree through transpiration-permeation conduction collaborative analysis; s3, generating a user-defined irrigation mode based on the water movement mismatch degree, and controlling the irrigation duration, the stop interval and the water flow rate. According to the method, coupling modeling is carried out on the plant physiological water demand behavior and the soil moisture supply characteristic, the limitation that a traditional irrigation system only depends on a soil humidity threshold value or meteorological index judgment is broken through, and the biological adaptability and the aging responsiveness of field moisture regulation and control are improved.

The invention provides an intelligent agricultural environment monitoring system based on the Internet of Things, and relates to the technical field of data processing. The feature extraction and analysis module is used for performing segmented statistical analysis on the soil humidity original data according to the soil humidity original data in combination with historical soil humidity data, historical environment temperature data and preset near-period soil humidity feedback data, and automatically extracting a humidity change rate and an abnormal trend index data set under multiple time windows; the self-learning judgment module is used for dynamically adjusting the current judgment threshold value based on a self-learning algorithm, judging the soil humidity original data in combination with the humidity change rate and the abnormal trend index, and generating a judgment signal for judging whether local irrigation response is triggered or not; the response control module is used for automatically generating an irrigation equipment starting instruction; the irrigation execution module is used for executing irrigation operation according to the irrigation equipment starting instruction; according to the invention, the accuracy of agricultural environment monitoring is improved.

An automated method of classifying irrigation status of fields within a geographical region includes identifying a plurality of field areas delineated by field boundaries within the geographical region. Seasonal normalised difference vegetation index (NDVI) time series data are processed to generate a plurality of aggregate field NDVI feature values for each identified field area. Irrigation status of each identified field area is classified by executing a decision tree classifier that is configured to determine a classification as either ‘irrigated’ or ‘non-irrigated’ based upon the corresponding plurality of aggregate field NDVI feature values.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

The invention provides a farmland root zone soil humidity prediction method, system and device and a storage medium, and belongs to the technical field of soil humidity prediction, and the method comprises the steps: obtaining multi-source remote sensing data of a farmland; downscaling the spatial resolution of the multi-source remote sensing data to obtain a kernel normalized difference vegetation index kNDVI of a first spatial resolution, a surface soil humidity SSM of a second spatial resolution and meteorological data ERA5 of a third spatial resolution; a root zone soil humidity RZSMt at the t moment is given, the kNDVI, the SSM and the ERA5 at the same moment and the SSM at the t + 1 moment are jointly input into a soil humidity prediction model, and the root zone soil humidity RZSMt + 1 at the t + 1 moment is simulated; and taking the current model output as the model input of the next step, and repeating the step to complete the prediction of the soil humidity of the root zone every day. According to the soil humidity prediction model provided by the invention, the problems of sensitivity and complexity in remote sensing inversion can be effectively solved, and relatively high-precision irrigation area scale root zone soil humidity prediction is realized.

The invention relates to the technical field of saline-alkali soil improvement, in particular to an intelligent irrigation and soil conditioning system for saline-alkali soil improvement, which comprises a climate prediction module, a soil detection module, an electrochemical salt migration prediction module, a data analysis module, an irrigation execution module and a data feedback module, the climate prediction module predicts future climate conditions; the soil detection module monitors soil parameters of the saline-alkali land in real time; the electrochemical salt migration prediction module is used for predicting the migration behavior of salt in soil; the data analysis module formulates irrigation and soil conditioning strategies; the irrigation execution module controls operation of irrigation equipment; and the data feedback module feeds back the irrigation execution result and the actual soil improvement effect, and continuously optimizes irrigation and soil conditioning strategies. According to the method, efficient utilization of resources is achieved, and the accuracy and scientificity of saline-alkali soil improvement are ensured.

The invention relates to the technical field of resource management, in particular to a seawall ecological protection-oriented vegetation water demand prediction method and system, and the method comprises the steps: collecting the environment monitoring data of a seawall region; establishing a salinity stress factor model based on the soil data, calculating a comprehensive salinity stress coefficient of the monitoring point, and performing multi-factor dynamic correction on the vegetation water demand to obtain a vegetation basic water demand; establishing a multi-dimensional time sequence model of a tidal cycle and a soil moisture supplement relationship considering geologic structure differences, and obtaining resource scheduling weights under different tidal stages and environmental conditions; according to the vegetation data, the underground data and the environmental data of the different seawall regions, based on a multi-region collaborative water resource allocation network, obtaining a deployment optimization scheme based on regional characteristic parameters; and generating a vegetation water demand prediction result and a graded resource scheduling scheme by integrating the vegetation basic water demand, the resource scheduling weight and the allocation optimization scheme, and performing irrigation.

The invention discloses an optimization method for spatial distribution of garden plants, which comprises the following steps: generating a high-precision 3D model of a garden terrain by adopting a plant spatial adaptive layout algorithm and utilizing a laser radar or unmanned aerial vehicle technology, generating a weight matrix according to soil types, illumination intensity and drainage characteristics, and applying a dynamic weight Voronoi algorithm in the terrain model to optimize the spatial distribution of the garden plants. The method comprises the following steps: generating a planting range of each plant, establishing a root distribution model by using an underground imaging technology, simulating distribution dynamic of moisture and nutrients among root systems in combination with underground hydrological data, collecting sensor data in real time by using a wireless network, designing a dynamic irrigation scheme, and optimizing the planting density and irrigation range of the plants. Data such as terrain and climate are integrated into a virtual garden, the virtual garden is ensured to be consistent with an actual state through interaction of a sensor and a digital twinborn model, plant growth, ecological benefit changes and disaster risks are predicted by using the digital model, and a user is allowed to modify and optimize the garden layout in real time through a visual interface.

The invention discloses a farmland environment multi-parameter intelligent monitoring system and method based on Internet of Things, particularly relates to the technical field of agricultural Internet of Things monitoring, and is used for solving the problem of stress resistance evaluation distortion caused by neglecting crop physiological recovery hysteresis in the prior art. Collecting soil water content data in real time through an Internet of Things sensor, and extracting a soil water content change rate when a preset threshold value is reached; secondly, constructing a space-time gradient matrix based on different soil layer water deficit rates, and generating a stress cumulative intensity index through principal component analysis and information entropy fusion; then analyzing a coupling relationship between the soil moisture change rate and the stress accumulation effect in the historical stress event on physiological recovery delay to generate a recovery delay coefficient; the physiological state lag degree is evaluated by combining the stomatal conductance change rate ratio of the stress period and the recovery period and the recovery delay coefficient; and finally, dynamically prolonging the irrigation execution time according to the lag degree until the irrigation execution time is lower than a set tolerance value, thereby remarkably improving the reliability of crop stress resistance test data.

The invention relates to the technical field of image processing, in particular to an irrigation prescription map inversion method based on unmanned aerial vehicle spectrum data. The method comprises the following steps: carrying out unmanned aerial vehicle data acquisition on a farmland area to obtain a multispectral image, DEM data and meteorological interpolation data; performing micro-landform parameter calculation according to the multispectral image, DEM data and unmanned aerial vehicle GPS data, and constructing a terrain-spectrum coupling model to obtain a terrain relief degree map and the terrain-spectrum coupling model; performing vegetation multi-dimensional state analysis by using a terrain-spectrum coupling model to obtain a vegetation index map, a canopy temperature map and a water stress preliminary judgment map; performing net radiation calculation by using the meteorological interpolation data to obtain a net radiation diagram; according to the net radiation diagram, the vegetation index diagram and the topographic relief degree diagram, soil heat flux estimation is conducted, and a soil heat flux diagram is obtained. According to the method, collaborative inversion is carried out by eliminating the terrain effect, and the precision and reliability of irrigation prescription map inversion are improved.

To provide a new type of intelligent rice seeding machine with a water-saving membrane and drought resistance that solves the problem of high production costs. [Solution] The system includes a mobile body 1 and an implement system, which includes a main mounting frame 2, a drip irrigation belt laying mechanism 3, an implement frame lifting mechanism 4, a soil cover removing mechanism 5, a furrow forming mechanism 6, a seeding mechanism 7, a synchronous irrigation mechanism 8, and a laser film cutting mechanism 9. The implement frame lifting mechanism adjusts the lifting of various mechanisms. The soil cover removing mechanism can be used for soil cover removing and transportation. The furrow forming mechanism can dig the soil in the seeding zone and open it into a furrow shape, which enables concentrated positioning and side fertilization. The seeding mechanism moves following the integrated machine. The sowing holes in the hole sowing wheel can perform automatic sowing, but the synchronous irrigation mechanism irrigates a uniform, fixed amount of water only in each seeding hole.

The invention relates to the technical field of self-adaptive control, in particular to a tobacco field variable fertilization control system and method, and the system comprises a sensing data collection module which collects soil humidity data through a soil humidity sensor, monitors the growth data of crops at the same time, and carries out the synthesis to obtain real-time soil and growth data; and performing time sequence analysis on the real-time soil and growth data, identifying a data trend and an abnormal point, and generating a trend analysis result. According to the method, the soil humidity and the crop growth data are collected, and the time sequence analysis is combined, so that the trend and the abnormality are quickly identified, and the prediction accuracy is improved. The convolutional neural network is used for carrying out recognition and probability evaluation on diseases and insect pests, so that disease and insect pest management is more accurate, and response is quicker. Through the technologies, the system can dynamically adjust irrigation parameters based on real-time data, optimize water distribution, and ensure that an irrigation strategy is perfectly matched with crop requirements and potential risks.