169 results about "Irrigation management" patented technology

The invention relates to the technical field of soil monitoring, in particular to a farmland irrigation management and control system based on the Internet of Things, and the system comprises a soil monitoring module, a crop water demand analysis module, an irrigation strategy making module, an irrigation execution control module, a water source management module, a fertilizer distribution module, an irrigation effect evaluation module and an adaptability adjustment module. According to the method, the crop water demand is analyzed by combining the decision tree, the water consumption is accurately calculated, the irrigation strategy optimizes the time and frequency based on the linear programming and simulated annealing method, the scheme adaptation degree is improved, the irrigation link adopts an automatic control and dynamic adjustment mechanism, the environment and crop demand changes are adapted in real time, and the irrigation efficiency is improved. Water source allocation ensures efficient distribution of multiple water sources on the basis of dynamic optimization, the type and quantity of fertilizer feeding are accurately controlled according to crop requirements, the irrigation effect is subjected to self-adaptive optimization of an irrigation plan after crop data evaluation, and accurate, flexible and efficient farmland resource management is integrally achieved.

The invention discloses an ecological garden intelligent management method and system, and belongs to the technical field of landscaping management, and the method specifically comprises the steps: monitoring environmental parameters in real time through multiple sensors, and uploading the environmental parameters to a cloud server; at the cloud end, a dynamic data analysis model is constructed by utilizing historical irrigation data, a plant growth model and real-time monitoring parameters, the water demand of plants is predicted, and an irrigation decision is made; an irrigation threshold value is set according to the decision, and an irrigation program is automatically started and executed through remote control equipment; after irrigation, detailed information is recorded and fed back to the data analysis model, the irrigation effect is evaluated by adopting a data analysis method, and an irrigation decision is finely adjusted accordingly; intelligent and precise irrigation management is realized, and the utilization efficiency of water resources and the growth quality of plants are improved.

The invention relates to the technical field of irrigation, and discloses an intelligent energy-saving irrigation management system and method based on environmental monitoring, and the method comprises the steps: obtaining the daily water demand of crops in a farmland through a crop growth monitoring module, setting the water flow of irrigation according to the daily water demand, then dividing the farmland into a plurality of regions, then environment parameters of soil humidity, temperature and illumination intensity of each area are monitored in real time through an environment monitoring module, and then the environment parameters are processed and analyzed through a data processing module to obtain current irrigation indexes of each area; whether the set irrigation water flow meets the irrigation requirements of all the areas or not is judged based on the current irrigation indexes of all the areas, if the areas do not meet the irrigation requirements, water supplementing is conducted on the areas in a sprinkling irrigation mode, then the water supplementing water flow is calculated based on the environment parameters, and it is guaranteed that the water content of all the areas of the farmland is consistent; therefore, the growth vigor of crops in each area is consistent.

The invention discloses a farmland precise irrigation decision-making method based on fusion of a physical process and machine learning, and the method comprises the following steps: S1, data collection: collecting meteorological data, soil data, fertilization and irrigation data and cotton growth data in a cotton growth period, S2, data preprocessing: carrying out the cleaning, standardization and normalization processing of an original data set, and carrying out the calculation of the data set; the method comprises the following steps: S1, preprocessing a crop mechanism model, S3, data enhancement: constructing and calibrating the crop mechanism model by using preprocessed data, S4, selecting key feature variables, and dividing the obtained data set into a training set and a test set by using an original data set and an enhanced data set, S5, model training and optimization, and S6, model verification and evaluation. According to the method, by coupling the crop growth mechanism model and the machine learning algorithm, not only can the growth conditions of crops under different moisture conditions be simulated and predicted, but also the influence of soil salinity and alkalinity on crop growth can be evaluated, so that comprehensive decision support is provided for irrigation management.

The invention relates to an irrigation amount control method and system for prune cultivation. The method comprises the steps of performing timestamp calibration and dynamic weight distribution processing according to soil data, meteorological data and transpiration data to obtain a multi-modal data set; based on the multi-modal data set, microclimate abnormal event detection and soil humidity correction processing are carried out, and anti-interference soil water content data are obtained; according to the anti-interference soil moisture content data and preset prune growth stage parameters, carrying out root system layered water absorption dynamic modeling treatment to obtain a deep root system moisture demand prediction value; according to the deep root system moisture demand prediction value and the transpiration data, irrigation amount dynamic planning processing is carried out, and the target irrigation amount is obtained. According to the method, through multi-source data fusion, root system water absorption modeling processing and dynamic irrigation planning, the target irrigation amount of prune cultivation can be accurately determined, the accuracy and scientificity of irrigation management are remarkably improved, and powerful guarantee is provided for high-quality growth of prunes and efficient utilization of water resources.

The invention discloses a multi-source information fusion irrigation decision optimization method and platform, and relates to the related field of data processing, and the method comprises the steps: determining a data collection node, and enabling the data collection node to be determined by taking a preset time interval as a reference and taking an interval irrigation factor feature as a compensation amount; triggering source end acquisition equipment, synchronously performing data acquisition on the irrigation area, and determining multi-source information; performing fuzzy set analysis on the irrigation factors, mining a confidence coefficient matrix, and supervising and training an irrigation decision model; transmitting the multi-source information to an irrigation decision model, executing deisomerization and matrix operation decision, and determining an irrigation strategy; an intelligent irrigation decision-making system is carried, an irrigation strategy is responded, and irrigation equipment is managed and controlled. The technical problems that an existing irrigation decision is lack of intelligentization and low in decision precision are solved, and the technical effect of achieving intelligentization and precision of irrigation management is achieved.

The invention discloses an intelligent precise irrigation system based on low-altitude remote sensing data driving. The intelligent precise irrigation system comprises a low-altitude unmanned aerial vehicle, a ground sensor network module, a meteorological data integration module, a space-time self-evolution feature map network module, a space-time adaptive attention network module, a context multi-arm tiger machine algorithm module and an automatic irrigation control module. The low-altitude unmanned aerial vehicle is used for acquiring remote sensing data of crops; the ground sensor network module is used for collecting real-time environment information; the meteorological data integration module is used for multi-modal data integration; the space-time self-evolution characteristic graph network module is used for generating a moisture demand distribution graph; the space-time adaptive attention network is used for optimizing an irrigation strategy; the context dobby machine algorithm module is used for dynamically selecting an optimal irrigation strategy; and the automatic irrigation control module is used for executing the optimal irrigation strategy and implementing differential intelligent irrigation management. According to the invention, precise and intelligent irrigation is realized by using the space-time self-evolution feature map network and the context multi-arm tiger machine algorithm.

The invention belongs to the technical field of garden irrigation, and particularly discloses a garden landscape intelligent irrigation management system which comprises a sensing layer, a transmission layer, a platform layer and an execution layer. The sensing layer is used for collecting garden environment and plant related data; the transmission layer is used for transmitting the data acquired by the sensing layer to the platform layer; the platform layer is used for realizing data visualization, intelligent decision generation and equipment control; the execution layer executes irrigation operation according to an instruction of the platform layer; according to the system, through accurate monitoring and intelligent control, water resource waste and irrigation cost are effectively reduced. Manual inspection and operation workloads are reduced through automatic operation, one administrator can manage thousands of mu of gardens, and the management efficiency is improved. The risk of plant diseases and insect pests can be reduced through on-demand irrigation, and the stability and attractiveness of the garden landscape effect are kept. The environment, irrigation and plant growth data accumulated by the system provides a scientific decision basis for garden maintenance, and is convenient for long-term planning and management strategy optimization.

The present invention discloses a multi-modal system for real-time plant hydration monitoring and irrigation management. The system comprises at least one acoustic sensor configured to emit controlled sound waves through plant tissues and detect corresponding vibrations and resonance frequencies using sensitive microphones or piezoelectric sensors to assess plant hydration. The system includes at least one bioelectrical sensor for monitoring bioelectrical signals. At least one nanotechnology-based sensor is configured to detect molecular changes in water content within plant cells through embedded or externally applied nanosensors. An aerial imaging device mounted on an unmanned aerial vehicle captures data related to canopy temperature, leaf color, and hydration indicators using infrared, multi-spectral, and thermal cameras. A central processing unit receives and analyzes data using predictive models, integrates the data via data fusion algorithm to generate a plant hydration profile, and controls water delivery through an irrigation management unit based on the hydration profile.

The invention discloses a variable irrigation prescription map generation method and device based on the synergistic effect of an unmanned aerial vehicle and satellite remote sensing, equipment, a medium and a product, and relates to the technical field of agricultural information monitoring and agricultural irrigation, and the method comprises the steps: formulating an irrigation management strategy of a sprinkling irrigation cluster based on a crop key water demand period; according to the unmanned aerial vehicle remote sensing information, constructing a soil moisture content inversion model in different growth stages, and according to a conversion equation between the unmanned aerial vehicle remote sensing information and satellite remote sensing information, predicting and inverting the soil moisture content in different growth stages; determining a soil moisture content spatial distribution diagram in the irrigation area of the sprinkling irrigation cluster; based on the irrigation information, according to the soil moisture content inversion model and the soil moisture content spatial distribution diagram, determining a moisture deficit spatial distribution diagram in the control area of the single sprinkling irrigation equipment; and based on an irrigation management strategy, according to the water deficit spatial distribution map, determining a variable irrigation prescription map in a control area of the single sprinkling irrigation equipment, and according to the application, accurate irrigation management can be realized.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention discloses an intelligent irrigation dynamic decision-making method based on reference crop evapotranspiration estimation. The method comprises the following steps: firstly, acquiring historical day-by-day weather data of a farmland area, calculating the evapotranspiration of a reference crop, and preprocessing the evapotranspiration; secondly, weather factors are screened to serve as input features, reference crop evapotranspiration serves as a target variable, and a reference crop evapotranspiration estimation model is constructed and trained; and then, through a reference crop evapotranspiration estimation model, obtaining reference crop evapotranspiration estimation results of the current day and the future n days. And finally, according to an evapotranspiration estimation result in combination with a crop coefficient corresponding to the growth stage of the rice, evapotranspiration of the current day and n days in the future is calculated, an irrigation decision is made, and the water storage capacity of the reservoir is adjusted based on the irrigation water consumption of the current day and the irrigation water consumption of the n days in the future. The invention provides an efficient, economic and sustainable technical scheme for intelligent irrigation management of large-scale farmland, and has important value for promoting agricultural precision and ecological development.

The invention belongs to the technical field of irrigation management and control, and provides a multi-source sensing data-driven irrigation area water demand prediction and accurate water distribution method. The method comprises the steps of obtaining multi-source data of a target irrigation area when a preset irrigation condition is met; performing quantitative evaluation on the first soil water content data and the second soil water content data based on the meteorological data to determine respective confidence degrees, and giving dynamic weights based on the confidence degrees, the temporal and spatial variation characteristics and the actual condition of the irrigation area; performing fusion processing on the first soil water content data and the second soil water content data based on the dynamic weight to obtain third soil water content data; on the basis of the third soil water content data and the crop growth stage data of the target irrigation area, the water demand of the stage is predicted; and generating a control instruction of the intelligent valve of the water delivery canal system based on the water demand. The accuracy of water demand prediction can be remarkably improved, the problems of water resource waste and insufficient supply are reduced, and efficient utilization and scientific management of water resources in an irrigation area are achieved.

The invention provides a garlic planting full-process database service system based on agricultural big data, which relates to the technical field of agricultural planting and comprises a planting planning module, a seeding management module, a growth monitoring module, a topdressing irrigation management module, a pest control module and a harvesting decision module. The planting planning module accurately matches varieties and base fertilizer formulas according to soil and yield data; the seeding management and growth monitoring module dynamically optimizes planting measures in combination with real-time data and historical experience; the topdressing irrigation module, the pest control module and the harvesting decision-making module utilize technologies such as image recognition and spectrum detection to realize a precise and scientific management strategy, effectively improve the resource utilization rate, reduce the influence of environmental fluctuation on garlic growth, reduce the use of chemical agents, realize full-process data driving by integrating multi-source data, and improve the management efficiency. Meanwhile, a complete planting file is formed to support strategy iteration, large-scale and efficient development of garlic planting is facilitated, and then the economic benefits of garlic planting are effectively improved.

The invention discloses a winter wheat multi-target irrigation dynamic optimization method based on a crop model and artificial intelligence, and belongs to the field of agricultural intelligent irrigation optimization and decision, the method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; carrying out coupling processing on the AquaCrop model and the PyFAO56 model based on the standardized input data to obtain final output of a coupling model; based on the final output of the coupling model, optimizing decision variables by adopting an NSGA-2 multi-objective optimization algorithm to obtain a Pareto optimal solution set; and sorting the candidate schemes in the Pareto optimal solution set by combining a fusion strategy of an entropy weight method and a random forest algorithm to obtain a final irrigation strategy. According to the invention, through multi-objective optimization, objective weighting and machine learning intelligent evaluation, a novel precise irrigation scheme considering both yield and water efficiency is provided.

A system for risk-based management of irrigation may perform an initial assimilation for a first layer of soil, wherein the initial assimilation for the first layer of the soil is based on water input data and an initial water withdrawal estimate. The system may perform a deterministic-based estimate to generate deterministic-based estimate values for one or more additional layers of the soil. The system may perform an observation-based estimate to generate observation-based estimate values for the one or more additional layers of the soil. The system may generate soil moisture content modelling values based on the observation-based estimate values and the deterministic-based estimate values, the soil moisture content modelling values generated by applying a reconciliation algorithm to reconcile conflict between the deterministic-based estimate values and the observation-based estimate values.

The invention is suitable for the technical field of electric digital data processing, and provides a dry farming rice irrigation method, device, equipment and system and a storage medium, the method comprises the following steps: obtaining the daily average temperature and historical soil moisture content of target dry farming rice from the beginning of sowing to the current moment; determining the growth stage of the target dry farming rice based on the daily average temperature; based on the historical soil moisture content, the field water holding rate is obtained; determining an irrigation upper limit value and an irrigation lower limit value of the target dry farming rice at the current stage based on the field water holding capacity and the growth stage of the target dry farming rice; and performing irrigation management on the target dry farming rice based on the irrigation upper limit value and the irrigation lower limit value. The automation degree and the water and fertilizer utilization rate of dry farming rice field management can be effectively improved.

The invention discloses a soil humidity sensing system and method based on millimeter wave radar, and relates to the technical field of agricultural Internet of Things and soil monitoring. In order to solve the technical defect that a soil humidity sensing system suitable for various agricultural environments is urgently needed in the prior art, the technical scheme provided by the invention comprises the following steps: acquiring millimeter wave radar echo signal intensity in a training stage; performing frequency domain transformation on the echo signal intensity, and constructing a two-dimensional signal intensity matrix with a frequency dimension and a virtual antenna dimension; constructing a neural network model based on the signal intensity matrix, and training the model according to the corresponding real soil humidity value; acquiring millimeter wave radar echo signal intensity in a test stage, constructing a corresponding signal intensity matrix, and inputting the signal intensity matrix into the trained neural network model for soil humidity prediction; and outputting the predicted soil humidity value as a sensing result. The system can be applied to non-contact real-time monitoring and precise irrigation management of soil humidity in smart agriculture.

A system and method for irrigation, and managing irrigation of, a property or landscape. The system may include an irrigation management server (“IMS”) with the information for an irrigation system. The system may access the irrigation system that has been input into the IMS as well as access a third party map that may display the irrigation system over the map. The system may provide for a user's GPS location to be accessed through and identify the location of the user with respect to the map and irrigation overlay. The system display may provide information regarding the landscape to a user. The system may allow for manipulation of the irrigation system while overlaid on the map by the creation and manipulation of zones within the irrigation system as well as the creation and manipulation of other commands, including watering times and watering duration.

The invention belongs to the technical field of equipment planning, and discloses an automatic planning method for intelligent agricultural Internet of Things equipment, which comprises the following steps: dividing a target region into grid units, collecting soil moisture parameters and nutrient parameters of each grid, and establishing a regional digital twinborn model; obtaining a target crop of each grid, obtaining growth information from the crop knowledge base, and executing time sequence simulation to generate a water and fertilizer demand table of each grid; executing a water-fertilizer balance strategy based on the water-fertilizer demand table, and generating a water-fertilizer supply plan of each grid; analyzing spatial distribution characteristics of supply plans, and clustering adjacent grids with similar plans into irrigation management partitions; and generating a pipe network topological structure and an equipment planning scheme based on the partition layout. According to the method, a traditional uniformly distributed area is scientifically divided into management subareas with consistent internal requirements, so that unified management of the same subarea is realized, and the problem of supply and demand mismatching caused by neglect of spatial heterogeneity in a traditional method is solved.

The invention discloses an intelligent agricultural irrigation management method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. The method comprises the following steps: acquiring regional environment monitoring data, regional condition information and historical irrigation demand corresponding to an irrigation management region; determining an initial irrigation time point and an initial irrigation demand quantity corresponding to the irrigation management area based on the area condition information; obtaining an environment influence coefficient corresponding to the irrigation management area based on the area environment monitoring data; based on the environmental influence coefficient, correcting the initial irrigation time to obtain a corrected irrigation time point corresponding to the irrigation management area; based on the environmental influence coefficient, the historical irrigation demand quantity and the initial irrigation demand quantity, obtaining a corrected irrigation demand quantity corresponding to the irrigation management area; and agricultural irrigation equipment in the irrigation management area is controlled to carry out irrigation operation according to the corrected irrigation demand quantity at the corrected irrigation time point. According to the technical scheme, the irrigation effect and the irrigation accuracy can be effectively improved.

The invention relates to construction and application of a wheat biomass estimation model fusing phenological information and hyperspectral data, and aims to solve the technical problem that wheat biomass estimation prediction precision and interpretability are difficult to balance. According to the method, Boruta-SHAP feature selection is combined with an attention-based neural network to estimate the wheat biomass, and the wheat biomass comprises phenological indexes (GDD, GD and ZS) with physiological significance and spectral features which are optimally selected by using a hierarchical attention mechanism. The model can effectively capture the crop development time and spectral dynamics. SHAP analysis shows that the contribution of phenological information to the model performance is 31.5%, key spectral characteristics comprise NIR (22.9%) and SWIT1 (12.3%), and the estimation precision is further improved. The robust and explainable framework model has a potential wide application prospect in the aspects of wheat yield prediction, fertilization optimization and irrigation management by providing an extensible biomass estimation method.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

Systems, user interfaces, and methods related to irrigation control systems are provided herein. In some embodiments, an irrigation control system includes an irrigation management application which, when executed by an electronic device, causes a user interface to be displayed to a user. The user interface includes features that permit a user to do one or more of configure, monitor, program, control, adjust various components and / or operations of the irrigation management system.

The invention provides a fruit tree growth state monitoring method and device based on a flexible sensor, is applied to a fruit tree growth state detection system, and relates to the technical field of agriculture. The system comprises a stem diameter detection device and a terminal device. The stem diameter detection device includes: at least one flexible sensor; the flexible sensor wraps a target position of the fruit tree; the target position comprises a trunk of the fruit tree, side branches of the fruit tree and fruits of the fruit tree; the method comprises the steps that the stem diameter detection equipment acquires diameter data of a target position; the terminal equipment judges the growth state of the fruit tree based on the diameter data; wherein the growth state at least comprises one of the following states: a normal state, a drought state, a fruit cracking early warning state and a nutrient deficiency early warning state; the diameters of trunks and fruits of the fruit trees are obtained through the flexible sensors, the growth state of the fruit trees is judged based on diameter changes, the equipment and labor cost is reduced, irrigation management is optimized, the crop yield and quality are improved, environmental stress is monitored, and agricultural digital transformation is promoted.

The invention discloses an irrigation pipe network intelligent scheduling method and system based on a hydraulic model, and the method can simulate the hydraulic behaviors of a pipe network in different operation scenes through the construction of a hydraulic irrigation simulation model, thereby achieving the precise simulation and prediction of the hydraulic behaviors in the irrigation pipe network. On the basis, an irrigation pipe network intelligent scheduling optimization objective function is established by taking optimal water supply quality as an objective according to the requirements of crop types and growth cycles of a target area for water. The minimum absolute error between the actual irrigation amount and the theoretical water demand is considered, key conditions such as the water supply amount constraint, the head valve working pressure constraint and the pipeline pressure constraint are also fused, and the scientificity and rationality of irrigation scheduling are ensured. Therefore, the irrigation efficiency can be remarkably improved, water resources are saved, healthy growth of crops is promoted, and effective technical support is provided for solving the problems of water resource shortage, poor irrigation management and the like in modern agriculture.

The invention relates to a hilly area intelligent irrigation decision-making system based on ET precise irrigation management, and the system comprises an ET-based intelligent irrigation forecasting system: simplifying a calculation model through ET0, and constructing a hilly area simplified ET0 prediction model; the farmland soil moisture content monitoring and early warning system is used for monitoring soil moisture in real time and realizing high-speed transmission and accurate analysis of soil moisture content and weather forecast data according to soil drought early warning indexes; a crop growth environment information real-time online acquisition and analysis system and a long-distance low-energy-consumption lossless information transmission technology are integrated; farmland irrigation control is managed in real time by using ultra-long-distance wireless transmission based on a spread spectrum technology and a pulse type electromagnetic valve low-energy-consumption wireless communication remote control technology based on a narrow-band internet of things. Quantitative and efficient water distribution of crop irrigation is achieved, and a precise irrigation regulation and control technology integration and efficient management mode meeting multi-crop, multi-climate and multi-growth-period requirements is met.

The invention relates to the technical field of monitoring and adjusting, and particularly discloses a water-saving irrigation system, method and equipment based on concealed pipe drainage. The system comprises a farmland irrigation water quality reference evaluation value comparison module, a farmland irrigation water quality compensation evaluation value comparison module, a farmland irrigation water quality real-time monitoring module, a farmland irrigation water quality compensation scheme comparison module and a farmland water-saving adjustment module, the problems that the prior art bureau is limited to directly monitor the water quality discharged from each water seepage underground pipe water outlet to a water well, and the influence of the use state of each water seepage underground pipe on water quality monitoring and water saving is neglected are solved; the influence of the amount of water, which is monitored by a flow meter and flows out of a water outlet of each water seepage concealed pipe to a water well, on the adjustment of unqualified farmland irrigation water quality and water saving is not considered, so that better irrigation management service can be provided for the farmland, and the unqualified water quality can be adjusted in a targeted manner; and the water-saving regulation and management level of farmland irrigation water quality is improved.

The invention provides a greenhouse crop habitat optimization method, system and device and a storage medium, and belongs to the technical field of intelligent agriculture, and the method comprises the steps: continuously obtaining greenhouse internal environment multi-element data, greenhouse external meteorological data, crop root zone current soil moisture data and device state data; predicting greenhouse internal environment multi-element data and soil moisture content of different soil layers in a future time period based on the data; predicting photosynthetic physiological habitat conditions of crops in the greenhouse in the future time period based on the multi-element data of the internal environment of the greenhouse in the future time period and soil moisture contents of different soil layers in the crop root zone; and taking the predicted habitat condition as an optimization target, performing optimization decision on current irrigation and environment multi-element regulation and control, generating a control scheme, further generating a regulation and control signal, and controlling greenhouse terminal equipment to operate according to the regulation and control signal. According to the invention, the accuracy, real-time performance, collaboration and intelligent level of greenhouse environment regulation and irrigation management are obviously improved, and the crop yield, quality and resource utilization efficiency are further improved.

The invention discloses an intelligent irrigation method based on deep learning, and the method comprises the steps: 1) data collection and arrangement: employing collected related meteorological data to calculate the water demand of crops; 2) establishing a data set, performing judgment by using the sorted meteorological data and the crop water demand and grey relational analysis, constructing a lightweight crop water demand model, constructing a four-classification data set based on irrigation decision, and dividing the data set into a training set and a test set; 3) by taking the LSTM model as a baseline, performing optimization to construct an LSTM-Attention model and construct a dynamic learning rate; 4) inputting the test set into the trained model for testing to obtain related performance indexes of the model; and 5) constructing a system platform, and developing an intelligent irrigation management platform at a web port. According to the invention, a crop water demand model can be trained by using a network data set, so that the irrigation amount of a target farmland can be formulated more accurately and scientifically, and control and assistance of irrigation management of the farmland can be realized in the system platform.