1022 results about "Soil humidity" patented technology

The invention relates to the technical field of intelligent agriculture and ecological internet of things, in particular to an ecological irrigation decision dynamic optimization method and related equipment. Comprising the following steps: acquiring multi-source environment data, acquiring a soil profile humidity gradient in real time through a soil humidity sensor array, acquiring future rainfall probability distribution, a temperature change rate and a wind speed predicted value in combination with a weather forecast interface, and synchronously accessing a geographic information system to acquire terrain elevation and crop distribution data. The three technical bottlenecks of model dimension collapse, parameter estimation instability and optimization response lag in a traditional irrigation decision-making system are systematically solved by constructing a space-time coupling analysis framework and a closed-loop optimization mechanism of multi-source heterogeneous data.

The invention relates to the technical field of slope stability data identification, and particularly provides a multi-sensor fusion slope stability intelligent analysis system and an edge calculation method.The system comprises an environment sensing subsystem, a data processing subsystem and an edge calculation subsystem, the environment sensing subsystem monitors the change of the soil moisture content through a soil humidity sensor, and micro movement data of a slope are obtained through a displacement sensor; using a pressure sensor to sense the change of the ground pressure; meanwhile, pre-screening out abnormal data to form a fused data set; the data analysis subsystem is used for converting abnormal data of the fusion data set into a quantitative index of slope stability, and deducing a potential sliding risk of the slope by analyzing the relevance of the soil water content, the tiny movement data or the ground pressure; and the decision support subsystem provides specific reinforcement countermeasures according to the analysis result of the potential sliding risk. According to the method, the potential sliding risk of the side slope is comprehensively deduced by analyzing the relevance of the soil water content, the tiny movement data and the ground pressure.

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

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

The invention relates to the field of intelligent agriculture, and discloses an intelligent drip irrigation control method, system and device and a storage medium, and the method comprises the following steps: data collection: obtaining soil humidity data, environmental meteorological data and crop growth state data of a target area through a plurality of sensors, and transmitting the data to a data processing module; the system comprises a data acquisition module used for acquiring soil humidity data, environmental meteorological data and crop growth state data and transmitting the acquired data to a data processing and storage module; the device comprises a machine case shell, and an intelligent control chip, a wireless communication module, an electromagnetic valve and a water flow meter are installed in the machine case shell. By integrating a sensor network, intelligent analysis and a real-time feedback mechanism, the water demand of crops is accurately calculated, irrigation is automatically adjusted, and water resource utilization is optimized; a personalized irrigation plan is made by combining a deep neural network and time sequence analysis, and the water requirements of crops in different growth stages are ensured.

The invention discloses an agricultural irrigation area water resource intelligent management system and method, and relates to the field of intelligent management, and the method comprises the steps: building a correlation model of a crop growth stage and a basic irrigation threshold value through real-time collection of original weather station data, future 24-hour weather forecast, crop types, planting dates and other multi-dimensional data; and in combination with soil humidity sensor data and a channel pump station state, the basic irrigation starting threshold is dynamically adjusted by using an FAO formula or a deep learning algorithm to form a dynamic irrigation decision threshold. And then multi-source data is input into an irrigation decision module, an irrigation starting and stopping instruction is generated by judging the relation between the irrigation state and the soil humidity threshold value, and closed-loop management is achieved. Thus, the limitation of traditional irrigation static threshold management is broken through, and through meteorological data dynamic adaptation and multi-source data fusion decision making, on the premise of guaranteeing crop growth water demand, the water resource allocation efficiency is optimized, and the intelligent management level of an agricultural irrigation area is improved.

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 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 relates to the technical field of agriculture, and discloses an intelligent fertilization technical measure and method for improving the fertilizer utilization rate, and the intelligent fertilization technical measure comprises a soil sensor module which is used for monitoring various parameters of soil in real time, including but not limited to soil nutrient content, soil humidity, soil acidity and alkalinity, soil temperature and soil texture; the soil sensor module can collect monitored parameter data with high precision and high frequency, and transmit the data to the central control unit through the wireless communication module; the fertilizer storage and conveying module is internally provided with a plurality of independent fertilizer storage bins, and each storage bin can store different types of fertilizers. According to the present invention, the type, the use amount and the application time of the fertilizer can be precisely controlled according to the growth requirements of different crops and the soil state through the precise fertilization mode so as to ensure the efficient absorption of the fertilizer by the crops, such that the fertilizer utilization rate is significantly improved, and compared with the traditional fertilization mode, the fertilizer waste can be reduced, and the agricultural production cost can be reduced.

The invention discloses a flood detention area emergency evacuation early warning method based on environmental perception and intelligent analysis, and relates to the technical field of flood control and disaster mitigation, and the method comprises the steps: collecting the original data of a flood detention area site, and forming a monitoring array; a monitoring array is divided into a plurality of monitoring units according to geographic grids, a rainfall permeation model is established according to rainfall data and soil humidity data, a surface runoff field is constructed in combination with terrain elevation data, and the confluence direction of the runoff field is calibrated through water level data; based on a rainfall penetration model and a surface runoff field, generating a flood detention area inundation evolution sequence, segmenting into an inundation grade map according to a time step length, planning a flood detention area emergency evacuation path, matching with a road network in a geographic information system, generating an optimal evacuation path, and issuing graded early warning information to an early warning area. And sending a withdrawal instruction to residents in the flood detention area through the emergency broadcasting system. According to the invention, by establishing a partition early warning mechanism and a differential early warning strategy, accurate delivery and efficient transmission of early warning information are realized.

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 remote monitoring and management rice seedling raising irrigation plant protection system, computer equipment and a medium, and relates to the technical field of rice seedling raising irrigation, the remote monitoring and management rice seedling raising irrigation plant protection system comprises a sensing acquisition module, an edge processing module, a data fusion module, a prediction modeling module, an execution control module, a user interaction module and an energy safety component; the sensing acquisition module is used for acquiring soil humidity, temperature, pH value, air temperature and humidity, illumination intensity, wind speed and direction, rainfall and crop image information through multiple types of sensors deployed in a rice seedling raising field, and outputting original environment data; the edge processing module is connected with the sensing acquisition module and is used for preprocessing the original environment data, generating preprocessed data and uploading the preprocessed data to a cloud server through an NB-IoT protocol; and the data fusion module is deployed in the cloud server and is used for receiving the preprocessed data from the edge processing module.

The invention discloses an accurate analysis method based on smart agriculture multi-modal data, and relates to the technical field of smart agriculture, comprising the following steps: collecting and preprocessing soil humidity and meteorological data, extracting features to construct an environment sequence table, establishing a prediction and anomaly detection model, and outputting a soil humidity prediction value and an environment anomaly probability. And constructing a decision model to generate a regulation index, dynamically adjusting an irrigation strategy, visualizing farmland data distribution and trend in combination with a thermodynamic diagram and a broken line diagram, synchronously recording an operation log, and realizing a multi-modal data driven precise agricultural decision closed loop. Through multi-modal data fusion and a dynamic decision-making mechanism, the problems of traditional agricultural data island and disaster response lag are effectively solved, the regulation and control index is optimized, the water resource utilization rate is improved, the invalid irrigation risk is reduced, the farmland state is fed back in real time, precise partition management is assisted, the disaster response efficiency is improved, and abnormal losses are reduced. And agricultural intelligent upgrading is promoted.

The invention belongs to the technical field of agriculture, particularly relates to a cranberry water demand dynamic sensing system and a method thereof, and aims to solve the problems that an existing traditional irrigation method is often based on a fixed time table or simple soil humidity measurement, the actual water demand condition of cranberries cannot be timely and accurately reflected, and insufficient irrigation or excessive irrigation is easily caused. In order to solve the problems that in the prior art, water resources are wasted, and the growth and yield of cranberries are possibly influenced, the invention provides the following scheme: the system comprises a monitoring module, a control module and a display module, wherein the monitoring module comprises stem flow rate, leaf water potential, soil water content at different depths and positions, air temperature and humidity, illumination intensity and wind speed data; according to the cranberry water demand monitoring system, accurate monitoring, real-time evaluation and scientific prediction of the cranberry water demand condition can be achieved, then a reasonable irrigation decision and environment regulation and control are guided, and the irrigation efficiency and the water resource utilization efficiency are improved.

The invention discloses a farmland soil humidity intelligent monitoring system based on the Internet of Things, and relates to the field of resource optimization decision crossing, a soil humidity monitoring module executes a soil humidity adjustment plan, monitors farmland soil humidity time sequence data in real time, and obtains a farmland soil humidity monitoring result through time sequence analysis and K-means clustering; obtaining an association rule between the soil humidity time sequence data and the crop yield; and the humidity monitoring report generation module is used for optimizing the soil humidity adjustment plan by tracking an optimal soil humidity threshold value in real time by utilizing a dynamic programming hybrid optimization algorithm according to the optimal soil humidity range, and generating a farmland soil humidity monitoring report. Through an optimization model coupled by a crop moisture production function and a multi-objective genetic algorithm, and in combination with Monte Carlo global sensitivity analysis, an optimal soil humidity range is identified, so that accurate quantitative modeling of a humidity-yield nonlinear relationship is realized, the defect of a static soil humidity threshold is avoided, and the sensitivity of moisture stress early warning is improved.

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 discloses a preparation method of an intelligent response type soil conditioner based on a nano composite material, and belongs to the technical field of soil improvement. The modifier is prepared from 10 to 15 parts of modified chitosan, 15 to 25 parts of nano silicon dioxide, 20 to 30 parts of biochar-loaded polyglutamic acid, 8 to 15 parts of humic acid-sodium alginate microcapsules, 3 to 5 parts of trace element slow-release particles, 1 to 2 parts of a pH response type cross-linking agent and 15 to 25 parts of a functional agent. The soil conditioner researched and developed by the invention can flexibly adapt to complex and changeable soil environments, can sensitively sense dynamic changes of key environmental indexes such as soil humidity, pH value and temperature, automatically and accurately regulates and controls the release rate and the action effect of the soil conditioner, realizes multiple functions such as water retention, acid regulation, fertility increase and pollution resistance in a one-stop manner, and has a wide application prospect. High efficiency, long-term effect and ecological environment friendliness of soil improvement are comprehensively achieved, a new road is opened up for global soil improvement, and agricultural yield increase, ecological restoration and urban green development are assisted.

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 discloses a rice field irrigation control method, and belongs to the field of rice field irrigation, and the method comprises the steps: obtaining soil humidity, water level and meteorological data from a rice field through a sensor network, storing the data in a pre-established distributed database, and obtaining a multi-source data set; according to the data set in the unified format, key features of soil humidity, water level and meteorological data are extracted by adopting a principal component analysis method, and a feature vector set is obtained; if the water consumption of the predicted irrigation scheme exceeds a preset threshold value, the scheme is optimized and adjusted through a linear programming algorithm, and an optimized irrigation scheme is obtained; according to the optimized irrigation scheme, the water consumption in the irrigation process is monitored in real time through flow meter equipment, and accurate measurement data are obtained; and the accurate measurement data is distributed through a quota management module, a regional water resource allocation scheme is generated by adopting a dynamic quota algorithm, and a quota management result is obtained.

The invention provides a time-space attention-driven soil humidity prediction method in a domestic supercomputing environment, and the method comprises the steps: constructing a time-space feature dynamic fusion soil humidity prediction network model based on attention guidance; the attention-guided spatio-temporal feature dynamic fusion soil humidity prediction network model comprises a convolutional long-short term memory network basic framework and a spatio-temporal attention mechanism module. The introduction of the ConvLSTM network effectively integrates the time sequence and space information of the soil humidity, the potential space-time dependence in the data is fully utilized, the space-time attention mechanism enables the model to adaptively pay attention to important time periods and space regions by dynamically adjusting the weight between time steps, and the accuracy of the model is improved. The limitation of fixed feature selection in a traditional method is avoided, so that the prediction precision is improved. The model can better capture space-time dynamic feature information, further identifies the relationship between the influence factor and the soil humidity, and especially shows unique advantages in complex space-time feature processing and modeling of long-time sequence 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 provides a multi-source remote sensing combined machine learning and deep learning soil humidity prediction method, which comprises a machine learning part and a deep learning part, and is characterized in that the machine learning part comprises the steps of collecting remote sensing data and site data, carrying out random forest model training based on the collected remote sensing data and site data, and carrying out deep learning; the deep learning part comprises the steps of training a deep model through the generated preliminary soil humidity product, constructing a soil humidity prediction model based on the random forest model and the deep model, and performing soil humidity prediction by adopting the soil humidity prediction model. And generating a final soil humidity product. According to the method, the defects of the traditional technology under the sparse data condition can be effectively overcome, the precision of soil humidity prediction is improved through an intelligent learning algorithm, and long-time sequence data can be processed, so that the application of the technology in a large-scale region is more feasible.

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.

A soil moisture and fertility sensor system is presented that includes a plurality of probes and a monitoring system. The plurality of probes are configured to make nutrient, moisture, and / or other measurements of the soil at a plurality of depths. The monitoring system is communicatively connected to the plurality of probes and configured to receive data related to the measurements of the soil from the plurality of probes. In one or more arrangements, the monitoring system is configured to track soil status based on the data to facilitate review of and management of soil nutrient and / or moisture levels.

The invention discloses an intelligent accounting platform for agricultural water consumption, and relates to the technical field of agricultural Internet of Things. The agricultural water consumption intelligent accounting platform comprises a multi-sensor data acquisition module which is used for acquiring multi-dimensional data of an agricultural field environment in real time through multiple sensors, comprises at least one flowmeter, at least one soil humidity sensor and at least one meteorological sensor, and ensures that the acquired data is comprehensive and accurate; interference of external factors on data is eliminated, data reliability is ensured, normal operation and immediate response of the system can be ensured by edge calculation under the condition that a network is unstable, efficient closed loop from data acquisition to decision execution is realized, high efficiency and accuracy of agricultural irrigation in a complex environment are ensured, and the method is suitable for popularization and application. The problems that an irrigation plan cannot be matched with the actual water demand of crops and multi-dimensional changes of a field environment cannot be reflected in real time due to the fact that single flowmeter equipment cannot provide finer data are solved.