721 results about "Soil humidity" patented technology

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention relates to the technical field of soil environment monitoring, and discloses a soil remediation construction site environment monitoring method based on edge computing cloud collaboration. The method comprises the following steps: deploying an edge computing node at a restoration construction site, and collecting real-time data of soil humidity, heavy metal concentration, volatile organic compound content and meteorological parameters through a multi-source sensor; inputting the data into a spatio-temporal feature extraction network, and generating a spatio-temporal fusion feature matrix by means of hierarchical convolution and a cross-channel attention mechanism; on the basis of the matrix, the index anomaly probability is calculated by using a depth probability network, and a risk feature sequence with uncertainty measurement is generated; constructing a dynamic risk field model in combination with repair process parameters, and predicting a pollution diffusion path through a space-time propagation algorithm; and in combination with an equipment operation state, an edge side local regulation and control instruction and a cloud global optimization strategy are generated by a collaborative decision engine, and efficient and accurate environment management requirements of a construction site are met.

The invention discloses a unit area irrigation water consumption estimation method and system based on multi-source data fusion, and relates to the field of irrigation water consumption estimation. Multi-source heterogeneous data such as remote sensing images, meteorological data, soil humidity and crop growth information are fused, and observation and model errors are reduced through data assimilation; estimating crop evapotranspiration under an energy balance principle; estimating daily-scale irrigation water consumption by adopting a water balance method; a crop actual irrigation area identification model based on deep learning is introduced, actual irrigation areas of different crop types are effectively distinguished, and the influence of temporal and spatial changes on irrigation efficiency is dynamically reflected. According to the method, high-precision and rapid estimation of the comprehensive unit area irrigation water consumption of a single crop and multiple crops on different regional scales can be realized, the timeliness and accuracy of irrigation water quota evaluation are remarkably improved, and the method has strong adaptability to climate change, soil difference and crop diversity and is suitable for popularization and application. Powerful technical support is provided for precise irrigation and intelligent water conservancy management.

The invention discloses a flood disaster risk intelligent early warning method fusing multi-source information, and relates to the technical field of flood disaster prediction.The method comprises the steps that multi-source data of meteorology, hydrology, terrain, remote sensing, ground monitoring terminals and the like are obtained, and a unified data set is constructed through time synchronization, space resampling, abnormal value elimination and missing data filling; constructing multi-dimensional spatio-temporal features based on factors such as rainfall process, topographic features, surface coverage, drainage capacity, river network structure and soil humidity; inputting the spatial-temporal characteristics into a multi-source information fusion model, and predicting a flood occurrence probability in a future time period; flood risk indexes are generated according to the prediction probability, real-time monitoring and historical samples, and regional risks are divided into different grades; early warning information is automatically generated and issued according to the risk level; and the early warning result is fed back and corrected through field monitoring and patrol data, so that self-adaptive updating of early warning parameters and a model structure is realized.

The invention discloses a garden partition intelligent water-saving irrigation regulation and control system based on the Internet of Things, and belongs to the field of irrigation regulation and control systems. According to the system, a basic water demand model containing static characteristics of plants, soil and the like is established for each subarea through a central processing controller, real-time environment data and weather forecast are fused, and future irrigation water demand and optimal time are calculated in a prospective mode. The core innovation of the method lies in that after irrigation, the system performs feedback iterative optimization on a basic water demand model by comparing the efficiency deviation of the actual change and the predicted change of the soil humidity to form a self-learning closed loop. According to the method, conversion from passive response to active prediction is achieved, the method has the advantages of being accurate, prospective and self-adaptive, the method can continuously adapt to environmental changes, and the intelligent level and the water saving effect of irrigation are remarkably improved.

The invention provides a photovoltaic power generation and air water taking irrigation system, comprising: a photovoltaic power generation module for generating electric energy by using light energy; the gel adsorption module is connected with the photovoltaic power generation module and is used for adsorbing water vapor in air by using gel and driving the gel to desorb and release the water vapor by using waste heat generated by the photovoltaic power generation module; the condensation water collection module is connected with the gel adsorption module and is used for condensing the water vapor to obtain condensed water; the crop irrigation module is connected with the condensation water collection module and is used for irrigating crops by utilizing the collected condensate water; the soil detection module is connected with the crop irrigation module and used for controlling irrigation according to the detected crop root system soil humidity, collaborative production of water, electricity and crops can be achieved under the condition that no external input exists in the island reef environment, and the problems that in the island reef environment, fresh water is in shortage, electricity is insufficient, and self-sufficiency of agricultural production is difficult are solved.

The invention relates to the technical field of precision agriculture monitoring, and discloses a precision agriculture monitoring system based on adaptive wireless communication. The system comprises a multi-source environment acquisition module, a communication protocol adaptation module and a crop state evaluation module. The multi-source environment acquisition module acquires soil humidity data, illumination intensity data and meteorological parameter data through wireless sensor nodes deployed in a farmland area, and performs timestamp synchronization on the data to ensure the consistency of the data in time dimension. And the communication protocol adaptation module dynamically selects a wireless transmission frequency band according to the synchronous data volume, allocates communication time slots based on the data priority, and generates adaptive transmission channel parameters so as to cope with the communication requirements in the data volume fluctuation and complex environment. The crop state evaluation module receives the transmission channel parameters, analyzes the real-time environment data flow, and divides the current crop growth state into a growth standard state, a growth lag state or a growth fluctuation state. The system provides support for fine management of agricultural production.

The invention discloses a desert region water resource recycling method based on ecological restoration coupling, and the method comprises the steps: building a moisture-vegetation coupled spatial dynamic model through obtaining soil humidity distribution, vegetation coverage and regional microclimate data, and recognizing a suspected water resource retention region; the water resource circulation potential is evaluated in combination with microclimate fluctuation characteristics and earth surface characteristics, and an actual water resource optimal utilization area is screened; according to the water resource circulation potential, the vegetation planting density, variety and irrigation mode are dynamically adjusted, the ecological restoration gain coefficient is calculated, and collaborative optimization of the water resource utilization efficiency and the ecological restoration effect is achieved. Water resource management and ecological restoration are organically coupled, accurate utilization and efficient circulation of water resources are achieved, and the restoration capacity and sustainability of an ecological system in the desert area are remarkably improved.

The invention provides a runoff forecasting method and device based on soil humidity calibration, and relates to the technical field of forecasting. The method comprises the steps of generating weather forced data of a WRF mode based on an atmosphere reanalysis data set under the condition that a target area has no runoff observation data; acquiring an observed value of surface soil humidity of a target area in the external meteorological data set; inputting the meteorological forced data into a distributed hydrological model WRF-Hydro model coupled with the WRF mode, and performing simulation to obtain a simulation value of the surface soil humidity of the target area; calibrating a key parameter set of the hydrological model according to the difference between the observed value and the simulated value of the soil humidity and a historical flood event of the target area to obtain a general parameter set of the target area; and using the general parameter set to drive a WRF-Hydro model to carry out runoff forecasting on the target area. Based on the scheme, runoff forecasting can be accurately carried out on the area without runoff observation data.

The invention relates to a multi-source data driven farmland irrigation decision-making method and device, equipment and a medium. The method comprises the following steps: acquiring multi-source heterogeneous data; carrying out fusion processing on the multi-source heterogeneous data, and constructing a unified farmland information database; performing spatial analysis on the data in the farmland information database; based on the analysis result, a preliminary irrigation prescription scheme is generated in combination with soil variation information and a crop water demand rule; on the basis of the preliminary irrigation prescription scheme, dividing the farmland into different irrigation areas, and calculating to obtain differential irrigation parameters of each irrigation area; integrating the differentiated irrigation parameters to obtain a differentiated irrigation strategy corresponding to each irrigation area; and dynamically optimizing a farmland irrigation decision based on each irrigation strategy. By adopting the method, multi-source data such as soil humidity, meteorological conditions and crop growth states can be effectively integrated, and a decision-making method capable of analyzing and generating an optimal irrigation scheme in real time is constructed.

The invention relates to the technical field of GNSS-R remote sensing, in particular to a satellite-borne GNSS-R soil humidity fusion inversion method and system based on machine learning, and the method comprises the steps: obtaining multi-source data composed of satellite-borne GNSS-R internal feature value data and SMAP external auxiliary data; the method comprises the following steps: performing preprocessing based on acquired multi-source data, taking a matched data set as input to construct a machine learning inversion model, performing track point soil humidity inversion by using an optimized inversion model, performing grid point fixed weight fusion according to a track point soil humidity inversion value, calculating the weight of each GNSS system by using an improved self-adaptive CRITIC weight method, and calculating the weight of each GNSS system according to the weight of each GNSS system. And result verification and application are carried out based on the soil humidity inversion value of the grid point, weight-fixed fusion is carried out on the characteristic value of each GNSS system track point in each grid, abnormal value interference is effectively resisted, and the output continuous grid data can be directly adapted to agricultural irrigation zoning, hydrological model input, climate research and other business scenes.

The invention provides a saline-alkali soil underground water regulation and control method and device, electronic equipment and a storage medium, and relates to the technical field of land governance. Inputting the first historical monitoring information into a pre-constructed prediction model to obtain a predicted value of soil salinity in a future time period; the predicted value of the soil salinity is input into the analysis model, and the target water level of underground water in the future time period is obtained; wherein the target water level of the underground water is used for restraining the target value of the soil salinity to be smaller than a preset salinity threshold value and the target value of the soil humidity to be larger than a preset humidity threshold value; generating a water level adjusting strategy based on the target water level of the underground water, and controlling the opening or closing of the water pump based on the water level adjusting strategy. According to the method, the soil salinity in the future time period is predicted by acquiring the saline-alkali soil monitoring information, so that the target water level of the underground water is obtained, the water level regulation strategy is generated, and the perspectiveness, dynamic nature and stability of underground water level regulation are ensured.

The invention discloses a sapling planting growth evaluation method based on image recognition parameter acquisition, relates to the technical field of sapling planting growth evaluation, and aims to solve the problem that the sapling survival rate is reduced due to inaccurate sapling growth cycle evaluation. According to the method, visual information is converted into quantitative data through single plant marking and feature binding, the objectivity and accuracy of growth state evaluation are greatly improved, a high-quality data foundation is laid for subsequent analysis, the leaf withering and yellowing risk is pre-judged through correlation of temperature fluctuation and soil humidity, a prospective management basis is provided for planters, and the method is suitable for large-scale popularization and application. According to the method, the timeliness and scientificity of growth regulation and control are remarkably improved, the withered and yellow risk of the leaves is pre-judged through correlation of temperature fluctuation and soil humidity, a prospective management basis is provided for planters, and the timeliness and scientificity of growth regulation and control are remarkably improved.

The invention relates to the technical field of agricultural environment intelligent monitoring, and discloses a cultivated land crop adaptability evaluation and planting suggestion system, which comprises a plurality of distributed microenvironment decision-making units, each unit acquires soil humidity and illumination data in real time through an environment sensor, and slope information acquired by a tilt angle sensor is combined to determine the adaptability of cultivated land crops. The local processor calculates entropy values representing crop stress risks based on a dynamic weight adjustment algorithm, and entropy value interaction and collaborative decision-making among units are realized through a low-power-consumption communication network. Therefore, the system can autonomously identify the local hydrological stress risk, forms a cooperative response network based on the physical law between units, and achieves the precise perception and intelligent decision-making of a complex farmland environment.

The invention discloses a multi-source information interaction enhanced soil humidity downscaling method and system, and belongs to the field of remote sensing image processing and computer vision. Comprising the following steps: constructing a single-multi-element interactive attention sub-module, a multi-source information interactive attention module, a residual space channel attention sub-module, a multi-scale feature fusion module, an adaptive double-domain joint loss function and a joint supervision type and degeneration type training mechanism, and obtaining a training convergence soil humidity downscaling network. And carrying out downscaling processing on the to-be-processed high-resolution auxiliary geoscience parameter data through training a convergence network to obtain a high-resolution soil humidity downscaling product. According to the method, the internal relevance between earth surface parameters is deeply mined, the multi-source information interaction attention module is constructed, the mapping relation between geoscience parameters is mined through information interaction between geoscience parameter characteristics, efficient fusion of multi-source data is achieved, and effective extraction and accurate calibration of multi-scale characteristics are achieved.

The invention provides a photovoltaic system and power distribution network state cooperative monitoring method based on the Internet of Things, and the method comprises the steps: deploying a multi-point collection device in a key region of a photovoltaic power station through a sensor network, and obtaining the soil humidity change and soil conductivity at regular time; denoising and standardizing the acquired soil humidity change and soil conductivity data to generate a standardized environmental parameter matrix; according to the dynamic change trend of grounding grid corrosion, identifying the mapping relation between the soil environment parameters and the grounding resistance value, and combining the real-time environment parameters and the corrosion trend to predict the resistance change; and comparing the predicted resistance change with the current grounding resistance value in real time, if the current grounding resistance value is detected to exceed a safety threshold range, triggering an abnormal early warning signal, and determining a potential area with high electric shock risk according to the resistance deviation degree and the corrosion rate.

The invention relates to the technical field of agricultural intellectualization, and discloses a fruit tree growth monitoring system based on multi-sensor fusion, which comprises a multi-mode sensor array, an integrated soil humidity sensor, a three-dimensional ultrasonic ranging sensor, a hyperspectral imaging sensor and a microenvironment meteorological station, and realizes multi-parameter synchronous acquisition; a nonlinear data calibration model adopts a layered architecture, and cross interference between sensors is eliminated; a dynamic weight distribution algorithm is built in the edge computing node, multi-source heterogeneous data are fused in real time, a fruit tree growth situation map is generated, and a low-power-consumption processor is adopted to support lightweight AI model deployment; the self-adaptive power supply module adopts a photovoltaic-temperature difference composite power generation structure to realize cable-free deployment; the wireless ad hoc network communication unit constructs an inter-tree relay network based on a LoRa protocol, supports low-power-consumption remote data return, and integrates a relay node health degree evaluation mechanism. The real-time performance of fruit tree growth monitoring, the data reliability and the situation analysis accuracy are improved.

The invention discloses a Xinanjiang model correction method based on CLDAS soil humidity assimilation. Soil moisture constants such as field capacity FC and wilting point WP are calculated; corresponding and mapping of a CLDAS observation layer and a model three-layer soil structure (WU, WL and WD) are realized through layered interpolation. For the problem of missing measurement of the CLDAS, a reconstruction model based on space-time Transform is introduced for filling, pixel-level uncertainty is output, and an EnKF observation covariance matrix is constructed based on the pixel-level uncertainty. And then dynamically updating the three-layer soil water content state of the model by using ensemble Kalman filtering, thereby reducing the influence of soil humidity initial estimation and simulation deviation on flood simulation. The flood simulation and real-time forecasting precision can be effectively improved, the method has the advantages of being stable in data source, clear in physical significance and high in applicability, and a new technical approach is provided for state initialization and digital hydrology research of a distributed hydrological model.

The invention discloses a soil humidity inversion method and device, a medium and equipment, and relates to the technical field of microwave remote sensing inversion. According to the method, the total backscattering coefficient, the normalized vegetation index, the leaf area index and the average leaf inclination angle of the electromagnetic waves of the target area pixel by pixel are obtained in combination with microwave remote sensing data and atmospheric apparent reflectance data of the target area; according to the normalized vegetation index, the leaf area index and the average leaf inclination angle, spatial clustering is carried out on vegetation, spatial heterogeneity of crop types is captured, and then based on the constraint that the surface roughness parameters and the vegetation parameters of the same vegetation type are the same and the soil humidity in the same surface space window is the same, the spatial heterogeneity of crop types is obtained. Through two-stage iteration inversion of the intra-class scale and the ground surface space window scale, the time dynamic state is adapted, the parameter calibration function of the in-situ soil humidity data is replaced, and the model parameters and the soil humidity can be determined without the in-situ soil humidity data. According to the invention, the accuracy and reliability of the soil humidity inversion method are improved.

The invention relates to the technical field of agricultural intelligent prediction, and discloses a fertilization decision-making system fusing soil and rice growth vigor information data acquisition and analysis, which is characterized in that a microcontroller controls an irrigation valve to apply an irrigation pulse with a preset duration, and a soil humidity sensor is used to monitor a time interval when moisture falls back from a peak value to a baseline; according to the method, irrigation behaviors are reconstructed into an active diagnostic tool, traditional chemical index detection is replaced by physical process dynamic monitoring, lag adjustment is converted into a pre-prediction mode, meanwhile, acoustic verification and a differential correction mechanism are combined, and the crop transpiration rate is predicted. And an anti-environmental interference accurate decision-making system is constructed on the basis of low-cost hardware.

The invention relates to the technical field of rotary cultivators, in particular to a rotary cultivator with a residual film recycling function. Comprising a front wheel; first rotating shafts are fixedly connected to one sides of the two front wheels, second rotating shafts are fixedly connected to one sides of the two rear wheels, a recycling machine shell is jointly and rotationally connected between the two first rotating shafts and the two second rotating shafts, and a tillage machine is arranged on one side of the recycling machine shell. Through cooperation of a water pump, a second threaded rod, a spraying bin and a spray head, broken films on a screen mesh plate can be dissolved, soil blocks can be decomposed, the problem that a large amount of time is spent on sorting residual films and straw by workers in the follow-up process is solved, a first motor, an air pump, a second motor and the water pump are controlled through a controller, and the working efficiency is improved. Complex residual film recovery and soil treatment processes are integrated into a highly automatic and intelligent operation system, the soil humidity, the residual film density and the impurity content are monitored in real time through a sensor, the suction force of an air pump and the water pressure of a water pump are intelligently adjusted, and soil moisture loss and energy consumption are reduced to the maximum extent while it is ensured that residual films are efficiently recovered.

The invention provides a ginger planting method for preventing and treating ginger bacterial wilt, and belongs to the technical field of ginger planting.According to the ginger planting method, through crop rotation, a ginger bacterial wilt pathogenic bacterium host chain is effectively cut off, ralstonia solanacearum is inhibited, harmful pathogenic bacteria in soil are removed through sun exposure, a biochar-probiotic mixture is applied, and meanwhile the harmful pathogenic bacteria in the soil are inhibited; biochar is utilized to increase porosity and water-holding capacity, relieve the problem of soil hardening, increase the pH value of soil, increase the content of alkaline cations, effectively inhibit growth of pathogenic bacteria of ginger blast, and also adsorb and retain nutrients such as ammonium and potassium through static electricity, and the pore structure of the biochar provides a shelter for probiotic groups, so that the probiotic groups are planted in the soil, and the soil quality is improved. The living space of the ginger blast pathogenic bacteria is inhibited; seed gingers are soaked in bacterium liquid containing bacteriophages, ginger blast pathogenic bacteria are effectively inhibited, drip irrigation is adopted for later-period management to maintain soil humidity, reproduction and spreading of the ginger blast pathogenic bacteria are effectively prevented and controlled, charcoal base fertilizer is additionally applied, and finally ginger blast is effectively prevented and controlled.

The invention relates to the technical field of intelligent irrigation, in particular to an intelligent watering control method and system for agricultural production, and the method comprises the steps: data collection, global data reconstruction, soil moisture content prediction, watering strategy optimization, sensor layout optimization, control execution and model updating. In the prior art, a pure mathematical method is generally adopted to calculate global humidity from sparse point data, and multi-dimensional environment information cannot be effectively fused, so that the reconstruction precision is limited and the physical credibility is insufficient; a physical information enhanced confrontation generation network is adopted to carry out global perception, sparse measurement values, geographic coordinates and real-time environment data are fused into conditional tensors to be input into a generator, and training is guided by forcing sensor data consistency in a loss function and introducing moisture diffusion physical constraints; the method can generate a high-resolution and physically reasonable global soil humidity distribution diagram, and significantly improves the reconstruction precision and reliability from sparse points to planar information in a complex farmland environment.

The invention relates to the technical field of water and soil loss dynamic monitoring and early warning methods in water conservancy projects, and discloses a water and soil loss dynamic monitoring and early warning method, which comprises the following steps: synchronously acquiring multi-dimensional data of terrain, rainfall, soil humidity and vegetation coverage through unmanned aerial vehicle remote sensing and a ground sensor network; registering and correcting the multi-source heterogeneous data based on a space-time fusion algorithm; the improved SWAT model is combined with a machine learning algorithm to dynamically simulate a water and soil loss process; and triggering graded early warning according to a preset risk threshold and generating prevention and control suggestions. The system comprises a data acquisition module, a data fusion processing module, a water and soil loss simulation prediction module and an early warning response module. According to the scheme, the real-time performance, the spatial resolution and the early warning accuracy of water and soil loss monitoring are remarkably improved, and efficient decision support is provided for ecological protection of water conservancy projects.

The invention relates to the technical field of agricultural irrigation, in particular to a precision agricultural drip irrigation system based on intelligent sensing and a control method thereof, and the system comprises an intelligent sensing module which is used for collecting meteorological data, soil humidity data and soil temperature data of different irrigation areas; the model prediction module comprises a moisture diffusion model and a rainfall prediction model which are respectively used for performing soil moisture diffusion prediction and rainfall prediction on different irrigation areas by utilizing the soil humidity data, the environment temperature data and the meteorological data, and outputting prediction results; the drip irrigation control module is used for controlling operation of a drip irrigation pump; the drip irrigation control module is further used for controlling electromagnetic valves in drip irrigation pipes in different irrigation areas to be opened and closed by utilizing an intelligent control mechanism according to the prediction result of the model prediction module so as to adjust the irrigation volumes in the different irrigation areas; the soil moisture diffusion rule can be predicted, repeated irrigation and irrigation blind areas are reduced, and waste of water resources is reduced.

The invention belongs to the cross technical field of hydrology and meteorology, data analysis and machine learning, and particularly discloses a soil humidity space-time multi-step prediction model construction method and a storage medium, and the method comprises the following steps: constructing a data set; a construction graph convolution module is used for constructing a connection matrix based on time sequence correlation of soil humidity data in a training data set, performing k-hop expansion and normalization processing on the connection matrix to obtain a k-hop normalized connection matrix, and extracting wide-area spatial features and remote heterogeneous dependence of soil humidity based on the construction graph convolution module; and a prediction model is constructed, the prediction model comprises GConvLSTM units and ConvLSTM units, the GConvLSTM units and the ConvLSTM units are crossed or stacked in sequence, a layer memory flow architecture is formed, and an initial KGCCLSTM prediction model is obtained. According to the invention, high-precision multi-step prediction of the soil humidity in a complex scene can be realized.

The invention discloses a multi-mode sensing environment control system and a disaster early warning method based on a greenhouse, and the system comprises a multi-mode sensing module which is used for collecting various environment parameters in the greenhouse, the multi-mode sensing module at least comprises a temperature sensor, a humidity sensor, an illumination sensor, a gas concentration sensor, a soil humidity sensor and image acquisition equipment; the method comprises the following steps: S1, acquiring environmental parameter data and crop growth images in a greenhouse in real time by a multi-modal sensing module; according to the invention, a multi-mode sensing technology is used, environment and crop information is comprehensively collected, and the evaluation accuracy is improved; a plurality of intelligent algorithm models are adopted for data processing, so that a basis is provided for environment control and disaster early warning; multiple modes are provided for disaster early warning, and timely information transmission is ensured; crop growth abnormity is monitored in real time through image analysis; the disaster early warning threshold value can be dynamically adjusted, and the early warning accuracy and timeliness are improved.

The invention relates to the technical field of intelligent agricultural intelligent irrigation control, and discloses an intelligent irrigation analysis control system based on a crop growth period. The system comprises a crop image sequence, soil humidity profile data and a regional meteorological sequence. And the data analysis module is used for performing multi-stage growth state interpretation on the image sequence, generating a crop structure state profile in each growth period, performing vertical moisture exchange identification on the soil humidity data, and generating a root zone moisture dynamic map. And the habitat mapping and stress evaluation module synchronously executes habitat adaptability mapping and moisture stress risk mapping. And the irrigation decision module corrects the habitat mapping result according to the stress evaluation result and generates an irrigation decision profile. And the control instruction generation module generates a partition valve control instruction in combination with the decision profile and the alternate irrigation rule. According to the invention, deep fusion analysis of the crop growth state, the root zone moisture and the meteorological environment is realized, and the irrigation decision accuracy and the water resource utilization efficiency are improved.

The invention discloses an intelligent adjusting device for strawberry garden irrigation, and relates to the technical field of agricultural irrigation, the device comprises a data sensing module, a main control module, a man-machine interaction module and an execution module; the data sensing module is used for collecting soil moisture content, planting environment data and strawberry growth stage data in real time; the main control module is used for receiving the sensing data, generating a dynamic irrigation decision according to an irrigation analysis model, and sending an irrigation decision control signal to the execution module; the man-machine interaction module is used for acquiring strawberry planting parameter information input by a user, querying soil and environment monitoring data and irrigation state information, and performing remote interaction; and the execution module is used for controlling the electromagnetic valve array to independently irrigate according to the strawberry planting ridges according to the irrigation decision control signal. Water resource waste of a traditional irrigation mode is avoided, accurate irrigation is conducted according to the soil humidity and the strawberry growth stage, the water resource utilization rate is increased, and automation of the irrigation process is achieved.

The invention discloses an Internet of Things big data agricultural informatization monitoring and management system and an implementation method thereof, through monitoring, analyzing and researching environmental factors such as illumination, soil humidity and temperature in related environments of crops, and in cooperation with related peripheral electronic elements, an implementation system with operability is formed; therefore, the plant growth environment is regulated and controlled, and finally the purpose of regulating and controlling the plant growth environment is achieved. Meanwhile, people can regulate and control the temperature, the illumination intensity and the soil humidity in the plant growth environment in real time through the network. The monitoring system comprises greenhouse field data acquisition, an environment sensor can acquire parameters such as temperature, humidity, illumination intensity, soil moisture and the like inside and outside the greenhouse in real time, the parameters are presented to a management user in a visual mode, and various sound-light alarm information is provided according to requirements of planted crops. The real-time monitoring effect is achieved in the field of agricultural ecological environment monitoring, real-time monitoring of the agricultural ecological environment is achieved, and high efficiency and high quality of the farm production process are guaranteed.