88 results about "Irrigation management" patented technology

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 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 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 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 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.

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 present application relates to the field of garden irrigation, and specifically discloses a garden intelligent monitoring irrigation system for environmental remediation, comprising: an environmental parameter and plant health index monitoring module for real-time monitoring of environmental parameters of each plant in the garden, including soil humidity, light intensity and temperature, and plant health indexes of each plant, including growth conditions, leaf color, and calculating a comprehensive index F of each plant at any time t i (t); the present application realizes efficient and accurate irrigation management through the multiple modules integrated by the system, significantly improves the pertinence and effectiveness of irrigation, the real-time monitoring function of the environmental parameter and plant health index monitoring module enables the system to accurately capture the growth needs of plants and environmental changes, providing a scientific basis for the development of irrigation strategies, the personalized irrigation strategy not only meets the growth needs of plants, but also avoids unnecessary irrigation waste, improving water resource utilization efficiency.

The invention provides a comprehensive benefit evaluation method and system for saline-alkali soil improvement based on drainage basin water-salt balance, and belongs to the technical field of agricultural ecological management. The method comprises the following steps: collecting and preprocessing geographical partition data, soil hydrological data, crop salt tolerance data and irrigation management data; constructing a salt migration-yield coupling model, namely establishing a salt migration dynamic equation, substituting the preprocessed data into the salt migration dynamic equation, and calculating the downstream salt concentration; a yield response model is established, the downstream salinity concentration and the preprocessed data are substituted into the yield response model, and the upstream treatment area increased yield and the downstream reduced yield are calculated; and calculating the net benefit value of saline-alkali soil improvement according to the upstream treatment area yield increase and the downstream yield reduction. According to the method, the salinity increment, exceeding the salt tolerance threshold value, of the soil of the treatment area is considered, overall systematic evaluation is carried out on upstream and downstream yield changes, and therefore reliable guidance is provided for selection and optimization of irrigation measures.

The invention provides a cold region greenhouse irrigation method and system, and belongs to the technical field of environment control. The method comprises the steps that soil temperature data and humidity data of different depths in all partitions of a greenhouse are acquired in real time; carrying out preprocessing and spatial-temporal feature enhancement on the collected data, inputting the data into a soil temperature-humidity dynamic coupling monitoring model, and calculating to obtain a root retting risk index of the current soil environment; if the root retting risk index exceeds a dynamic risk threshold, generating an irrigation strategy for inhibiting the root retting risk; if the root retting risk index does not exceed a dynamic risk threshold, generating a conventional irrigation strategy based on a multi-objective optimization algorithm; according to the generated irrigation strategy, driving the irrigation execution mechanism of the corresponding subarea to perform independent action, and realizing subarea accurate irrigation; the problem of root retting caused by low temperature and high humidity in a cold region greenhouse environment is solved, and precision, intelligence and high efficiency of irrigation management are achieved.

The invention relates to the technical field of data processing, in particular to an efficient monitoring and intelligent irrigation management platform for radar rainfall data. Obtaining a first abnormal point location and a non-initial abnormal point location according to a data volume feature in an intensity data cluster of the echo intensity and a data difference feature of the intensity data cluster; obtaining a point location connected domain according to the intensity data cluster of the non-initial abnormal point location; obtaining a spatial noise feature value according to the position distribution feature between the point location connected domains and the data difference feature in the point location connected domains; obtaining a time noise characteristic value according to the historical echo intensity of the non-initial abnormal point location; obtaining a second abnormal point location according to the spatial noise characteristic value and the time noise characteristic value; correcting the corresponding echo intensity according to the position features of the first abnormal point location and the second abnormal point location; and the rainfall is determined according to the corrected echo intensity, and the irrigation rule is adjusted, so that the accuracy of rainfall judgment and irrigation regulation is improved.

This invention discloses a smart irrigation management system based on soil and crop moisture status identification, belonging to the field of crop moisture status monitoring technology. The system includes: acquiring leaf electrophysiological signal sequences and multi-depth water potential parameters of the rhizosphere microdomain of the target crop; determining the onset time of water stress and its corresponding first characteristic frequency based on the leaf electrophysiological signal sequences; determining the active water-uptake layer and its corresponding second characteristic water potential based on the multi-depth water potential parameters; performing a coupled comparison based on the first characteristic frequency and the second characteristic water potential, and determining whether irrigation triggering conditions are met based on the comparison results. If the irrigation triggering conditions are met, a differentiated irrigation quota is determined for different irrigation areas. This invention determines irrigation triggering conditions by comparing the consistency and degree of deviation between the first and second deviation directions, avoiding premature or late irrigation caused by misjudgment based on a single indicator.

The invention belongs to the technical field of agricultural intelligent control and irrigation management, particularly relates to a self-adaptive irrigation decision-making system oriented to crop water demand response, and aims to solve the problems that a traditional irrigation system is lagged in response, extensive in water distribution and difficult to match actual physiological water demand of crops. The system collects crop physiological and environmental parameters through a multi-source sensor, inverts a water stress index in real time in combination with an edge computing node, dynamically calls a basic water demand curve based on a crop growth stage, and generates an instantaneous water demand deviation. A double-level decision-making mechanism is adopted, wherein the first layer triggers emergency irrigation according to a dynamic warning threshold value; and the second layer realizes irrigation plan feed-forward adjustment through trend analysis. The system also integrates a closed-loop feedback verification and federated learning optimization mechanism, and continuously improves the decision precision. By fusing physiological feedback and self-adaptive modeling, the conversion from watering on day to water supply on demand is realized, the water utilization efficiency is remarkably improved, and the stress risk is reduced.

The invention relates to the technical field of management systems, in particular to an intelligent spray irrigation management system under forestry plant maintenance, which comprises a soil layering model construction unit for collecting the types of planted saplings and topographic data of a planting area, extracting soil layering structure data of the planting area and a three-dimensional topographic model, and constructing a three-dimensional model; performing fusion processing to obtain a soil layering model; determining the distribution positions of planted saplings on the soil layering model, marking the position corresponding to each sapling on the soil layering model as a planting point position, constructing a sapling root growth model based on the type of the sapling and the soil layering structure of the position, and forming a sapling soil layering model together with the soil layering model; and the irrigation network construction unit is used for setting an irrigation point position for each sapling root growth model based on the sapling soil layering model, connecting a plurality of drip irrigation point positions to form a pipeline trend straight line, erecting branch pipes along the pipeline trend straight line, erecting a main pipe and connecting a plurality of branch pipes to form an irrigation network.

The invention relates to the technical field of agriculture, and discloses a fresh corn efficient harvesting and storage method based on ecological planting, which comprises the following steps: ecological planting management, harvesting opportunity determination, efficient harvesting operation and storage condition control. According to the ecological planting management, scientific fertilization and irrigation management are implemented, nutrient and water supply is dynamically adjusted according to the growth stage of the fresh corn, a balanced nutrition environment is provided for growth of the fresh corn, and therefore balanced development of quality indexes of the fresh corn is promoted; the harvesting opportunity is determined based on quality index monitoring and a harvesting prediction model, accurate judgment of the mature state of the fresh corn is achieved, it is guaranteed that harvesting is conducted at the optimal harvesting time, a foundation is laid for obtaining high-quality fresh corn, efficient harvesting operation reduces mechanical damage to the fresh corn by optimizing harvesting parameters and the harvesting environment, and the yield of the fresh corn is improved. And immediately transferring to a shading environment, and slowing down the physiological deterioration process after harvesting.

The present application relates to the technical field of agricultural drought monitoring and precision irrigation, and particularly relates to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, which collects crop canopy oxygen absorption band spectral data through a hyperspectral sensor, and calculates sunlight-induced chlorophyll fluorescence intensity SIF values by using a fluorescence extraction algorithm. The SIF value of a non-water-stressed crop canopy is taken as a reference canopy SIF value, the difference between the SIF value of a to-be-measured crop canopy and the reference canopy SIF value is determined, then a relative SIF water deficit index SIFWDI is generated by combining a preset formula, a regression analysis model is used to establish a mapping relationship between the SIFWDI and a soil water deficit index to output a diagnosis report, a variable irrigation decision and early warning are triggered according to a stress level, and model parameter adaptive optimization is realized through a feedback mechanism. The system realizes non-destructive, real-time and high-precision water stress monitoring and irrigation management.

The application relates to the irrigation technical field and discloses a smart energy-saving irrigation management system and method based on environment monitoring, which acquires the daily water requirement of crops in farmland through a crop growth monitoring module, sets the water flow of irrigation according to the daily water requirement, divides the farmland into several regions, then monitors the soil humidity, temperature and light intensity environmental parameters of each region in real time through an environment monitoring module, then processes and analyzes the environmental parameters through a data processing module to obtain the current irrigation indexes of each region, judges whether the set water flow of irrigation meets the irrigation requirements of each region based on the current irrigation indexes of each region, if some region does not meet the requirements, the region is watered through a sprinkling irrigation mode, then the water flow of the water supplement is calculated based on the environmental parameters, the water content of each region of the farmland is ensured to be consistent, and therefore the growth of crops in each region is ensured to be consistent.

The application discloses a multifunctional intelligent water-saving irrigation system and particularly relates to the fields of agricultural irrigation and intelligent control, and comprises a data acquisition module, a data processing module, a central processing module, a judgment module, a control module, an irrigation execution module and a remote monitoring and control module. The soil parameters are acquired by the data acquisition module, are cleaned by the data processing module, are transmitted to the central processing module for analysis and calculation, and irrigation values are obtained. The judgment module generates instructions according to the irrigation values and threshold values, and the irrigation operation is executed by the control module. In addition, the remote monitoring and control module enables the user to remotely check the system running state and environmental parameters, and realizes precise and efficient water-saving irrigation management.

The invention discloses a crop canopy interception-growth collaborative simulation method and system based on dynamic feedback, and the method comprises the steps: obtaining a leaf area index and canopy coverage of a crop module in an agricultural production system simulator APSIM, and combining actual measurement rainfall data and meteorological data to drive a revised Gash canopy interception model; and calculating rainfall interception loss and effective rainfall, and feeding back the effective rainfall to a soil moisture module of the agricultural production system simulator in real time, thereby realizing dynamic influence simulation of moisture input on crop growth. The system comprises a canopy interception calculation unit and a crop growth simulation unit, and bidirectional transmission and dynamic coupling are realized through a data interface. The method effectively improves the simulation precision of the crop growth process, especially the moisture process, effectively optimizes the soil moisture balance prediction, significantly enhances the reliability of the crop growth process and yield simulation, and is suitable for the application scenarios of agricultural water resource optimization configuration, water-saving irrigation management, crop precise production decision support and the like.

The invention discloses an intelligent agricultural irrigation system based on Internet of Things, which comprises an information collection module, an irrigation analysis module and an irrigation management module, and is characterized in that the information collection module is used for collecting and obtaining related information of an irrigation area, and the irrigation analysis module is used for analyzing and calculating the appropriate irrigation water amount of the current irrigation area; the irrigation management module is used for controlling the real-time irrigation condition of an irrigation area, the information collection module is electrically connected with the irrigation analysis module, and the irrigation analysis module is electrically connected with the irrigation management module. Timely water supply is ensured; and water waste is avoided. Therefore, the problem that the yield of crops is reduced due to the fact that water supply is difficult to guarantee evenly in a traditional irrigation mode is solved, the utilization efficiency of agricultural water resources is improved, intelligent and efficient agricultural irrigation management is achieved, and the system has the advantages of being intelligent and refined.

The present application relates to the field of water resource management, and particularly relates to a zero-carbon water resource comprehensive management system for agricultural irrigation areas, comprising: an irrigation area basic data calculation module which calculates soil latent heat flux and crop basic evapotranspiration of the irrigation area respectively according to detected air temperature, air pressure, solar radiation and wind speed; an irrigation area data prediction module which generates predicted water storage and utilization amount of the irrigation area through an irrigation area prediction model based on a ConvLSTM architecture; a water demand fusion prediction module which generates comprehensive predicted water demand of the irrigation area through a fusion mapping model based on a gating mechanism and multi-scale convolution; and a zero-carbon water resource management module which calculates predicted net irrigation amount of the irrigation area based on the comprehensive predicted water demand to perform irrigation management on photovoltaic water pumps and photovoltaic valves of the irrigation area. The present application improves the precision of water resource management of the irrigation area, optimizes the scheduling energy consumption of the zero-carbon photovoltaic water pumps and photovoltaic valves, and realizes the optimization of the zero-carbon transformation of the agricultural irrigation area from the head of irrigation energy consumption.

The utility model relates to the technical field of farm irrigation, and discloses a rural development irrigation water accurate meter which comprises a water pipe, a pipeline valve is fixedly installed at the right end of the water pipe, a filter box is fixedly installed in the middle of the water pipe, a meter is fixedly installed at the top of the water pipe, and a faucet is fixedly installed at the left end of the water pipe. An impurity filtering mechanism is arranged in the filtering box, the impurity filtering mechanism is used for filtering impurities in water, an impurity collecting mechanism is arranged at the bottom of the filtering box, and the impurity collecting mechanism is used for collecting the impurities filtered by the impurity filtering mechanism. According to the utility model, irrigation water is filtered by the filter element in the filter tank and then flows to the meter and the faucet, so that impurities in the irrigation water are accurately filtered, the meter is effectively prevented from being damaged by the impurities, stable operation and accurate metering of a metering system are guaranteed, and scientific irrigation management is facilitated.

The present invention provides a system, a method, and an apparatus for providing an irrigation scheduling module including a graphical user interface to provide irrigation scheduling data for a given field location. According to a preferred embodiment, the irrigation scheduling module is configured to calculate and display an irrigation recommendation for a given set of forecast data. According to another preferred embodiment, the irrigation recommendation includes a representative form in the shape of a circle that changes from a complete circle to an increasing percentage of the complete circle based on the field moisture status.

The invention discloses a planting method for preventing and treating yellowing and regreening of tree leaves under the condition of reclaimed water irrigation in drought saline-alkali soil, which can prevent the yellowing of transplanted nursery stock leaves through the measures of backfilling planting soil in planting holes, raising soil ball height (shallow planting), heavy pruning and the like. The survival rate of transplanted trees under the condition of high-salt reclaimed water irrigation in arid and saline-alkali areas is increased, the survival rate can reach 90% or above, and the method is suitable for reclaimed water irrigation management.

This application relates to a method for planting sugarcane in sandy yellow soil, comprising: preparing the selected sandy yellow soil and applying base fertilizer; selecting sugarcane varieties suitable for the sandy yellow soil; treating and planting the selected sugarcane varieties; dividing the selected planting area into basic unit zones to facilitate field management; conducting field management of the sugarcane planted in the sandy yellow soil, including integrated water and fertilizer irrigation management; harvesting the mature sugarcane; the sugarcane is harvested twice a year, with the second harvested sugarcane being cultivated from the sugarcane roots after the first harvest. The technical solution of this application effectively solves the problem of poor sugarcane yield in existing technologies for planting sugarcane in sandy yellow soil.

The invention discloses a method for interplanting sweet potatoes in a mulberry field, relates to the field of mulberry interplanting, and solves the problems of insufficient illumination of sweet potatoes, nutrient competition between mulberries and sweet potatoes and water and soil loss of ridges in a wide-narrow row mulberry field. The method comprises the steps that mulberries are planted in wide and narrow rows, an arc-shaped ridge face ridge is formed in the middle of the wide rows, and drainage ditches are formed in the two sides of the ridge; guiyuan No.10 sweet potato seedlings are planted on the ridges, and Guimulberry superior 12 mulberry seedlings are planted in narrow rows; sweet potatoes are planted 10-15 days after mulberries sprout in spring, the first-time mulberry harvesting height of the mulberries is 1.5-1.7 m, and the symbiotic period is 140-160 days; in the drought season, irrigation management is implemented, and the irrigation amount is 20-30 cubic meters per mu every time. The method can effectively improve the land utilization rate of the mulberry field and the comprehensive benefits of mulberry and sweet potatoes.

The invention relates to the technical field of agricultural management, in particular to an intelligent irrigation management and control system which comprises the following steps: acquiring crop sample data, preprocessing the crop sample data, and extracting crop growth trend data from the processed crop sample data; constructing a self-attention mechanism model comprising three layers of Transform architectures, and training the self-attention mechanism model of the Transform architectures by using the crop growth trend data to obtain a basic model for detecting the crop growth condition; the method comprises the following steps: collecting sample data of a target area and an irrigation pipe network distribution map of the target area, segmenting the target area by using an irrigation pipe network, obtaining sample data of small areas, and preprocessing the sample data of the small areas. The plant growth condition of each region is accurately predicted, irrigation and fertilization are conveniently and specifically performed on crops, and the farmland fertilization accuracy in the region is improved.

The application 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-making. The method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; performing coupling processing on an AquaCrop model and a PyFAO56 model based on the standardized input data to obtain final output of the coupling model; based on the final output of the coupling model, adopting an NSGA-2 multi-target optimization algorithm to optimize decision variables to obtain a Pareto optimal solution set; and combining a fusion strategy of an entropy weight method and a random forest algorithm to sort candidate schemes in the Pareto optimal solution set to obtain a final irrigation strategy. The application provides a new type of precise irrigation scheme by giving consideration to yield and water efficiency through multi-target optimization, objective weighting and machine learning intelligent evaluation.

The utility model discloses an agricultural internet of things data acquisition control system, including main control cabinet, solar generating mechanism, monitoring mechanism and spraying irrigation mechanism. This agricultural internet of things data acquisition control system utilizes each bottom monitoring branch to monitor the area respectively, monitors carbon dioxide concentration, temperature and soil humidity etc. data in field, helps remote control center real -time monitoring the data in each area, and remote control center judges whether need irrigation according to the plant humidity demand in the area and the data in the corresponding area, when need irrigation, then through main control unit control corresponding irrigation branch watering, thereby according to the plant difference of each area, carries out corresponding irrigation management and data acquisition to each area, realizes the fine management, adapts the field that has different crops to plant, utilizes water storage branch to collect rainwater to reach the purpose of comprehensive utilization rainwater resources and water saving.