1089 results about "Soil moisture content" patented technology

The invention discloses a flexible load multi-target collaborative scheduling system and method, and relates to the technical field of collaborative optimization of power systems. The method is used for solving the problem of lack of accurate prediction and multi-target coordination of agricultural electricity and water utilization regulation and control. Firstly, based on meteorological data, soil moisture content and crop growth characteristics, an irrigation demand prediction model is constructed, irrigation water demand is predicted, and a water pump load power baseline is generated; then, a dynamic baseline constraint condition is generated in combination with historical behavior data and water pump start-stop logic; establishing a power grid side objective function, a user side objective function and a water affair side objective function, and introducing an underground water and carbon emission punishment mechanism; thirdly, dividing the power distribution network into sub-regions, adopting an alternating direction multiplier method to solve a region regulation and control strategy in parallel, coordinating water resource distribution conflicts through virtual interactive variables, and outputting a global scheduling instruction; and finally, collecting real-time response data and correcting the prediction model on line to realize closed-loop adaptive optimization.

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses a grape disease recognition and early warning method based on the Internet of Things, and relates to the technical field of plant disease recognition, image acquisition equipment and environment sensing nodes are arranged in a vineyard, and leaf images and corresponding temperature and humidity, illumination and soil moisture parameters are obtained; inputting the image into a neural network fusing dilated convolution and a residual attention mechanism, realizing extraction of a disease spot region and a disease spot variation feature, and generating a preliminary recognition result; constructing a multi-factor evolution sample set in combination with the recognition result and the environment state of the time node; constructing a space-time correlation graph model based on a graph neural network, estimating a disease propagation risk path and a diffusion probability, and performing early warning judgment at a gateway end through a multi-factor gating discrimination algorithm; the method disclosed by the invention is high in recognition precision and strong in response timeliness, has adaptive prediction and targeted treatment capabilities, and remarkably improves the intelligence and precision level of grape disease management.

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

The invention relates to the field of agricultural data analysis, in particular to a japonica rice paddy field soil carbon nitrogen ratio dynamic intelligent assessment method, which comprises the following steps: acquiring and preprocessing japonica rice paddy field environment time sequence data to obtain a standardized environment data set; performing nonlinear congestion analysis on the soil moisture content state to obtain a corrected moisture dimension distance component; obtaining a corrected temperature dimension distance component by evaluating the metabolism permissible degree of the soil moisture on the temperature effect; performing double weighted fusion on the moisture and temperature correction distance components to obtain a soil environment state clustering result; through coupling calculation of a soil environment state clustering result and a first-order dynamic decomposition model, a dynamic intelligent evaluation result of the carbon-nitrogen ratio of the japonica rice paddy field soil is obtained, so that the problem that existing clustering cannot accurately distinguish aerobic and anaerobic environment states in a japonica rice paddy field scene and causes deviation of the evaluation result of the carbon-nitrogen ratio of the soil is solved.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The invention relates to the technical field of intelligent control, and discloses a water-saving fertilization variable control method suitable for sandy soil, and the method comprises the steps: obtaining collection data based on a sensor; and establishing and updating an infiltration-storage-leaching loss prediction model of the sandy soil according to the collected data, performing state estimation on soil moisture and nutrient states, and calculating root layer moisture deviation, fertilizer concentration deviation and deep seepage risk. And executing model predictive control in the rolling time domain to obtain a control result. And executing water-saving fertilization operation according to the control result. In the execution process, the soil volume moisture content, the soil water potential, the soil conductivity and shallow layer leakage observation data are fused, the prediction model is corrected, the control result is dynamically adjusted according to the correction result, and when the environment wind speed exceeds a set threshold value, the spray irrigation parameters are corrected. The fine level of control is improved, deep layer leakage can be inhibited under the sandy soil condition, water and fertilizer balance of a root layer is guaranteed, and external disturbance is coped with.

The invention relates to the technical field of alpine sand soil moisture leakage estimation technologies, and particularly discloses an alpine sand soil moisture leakage estimation method which comprises the following steps: S1, multi-source data acquisition: acquiring 0-110cm layered soil moisture content and temperature data by using a cold-resistant soil hydrothermal sensor array, and calculating the moisture content and temperature data of the layered soil; synchronously acquiring Sentinel-2 remote sensing data to extract vegetation coverage and land surface temperature, and recording regional freezing and thawing cycle times and biological crust coverage types; s2, key parameter inversion: calculating a permeability coefficient correction factor kFTC = alpha * N (1 / 2) through a multi-expression programming model based on the freezing and thawing cycle number N. According to the method for estimating the soil moisture leakage amount of the high and cold sand land, the limitation of a traditional model based on steady-state water flow and empirical parameters is broken through by quantifying the dynamic effect of freezing and thawing cycles on the porosity of a 30-80 mm soil layer and the water blocking effect of 23.41%-30.39% when the biological crust coverage degree exceeds 30%; and the influence of the low-temperature freezing and thawing cycle and the biological crust on the leakage mechanism of the high and cold sand land is accurately depicted.

The invention discloses a poplar degenerated forest stand accurate restoration method, and relates to the technical field of poplar degenerated forest stand restoration, and the method comprises the steps: obtaining a multi-temporal satellite remote sensing image and an unmanned plane multi-spectral image of a target area; in combination with soil moisture content, rhizosphere respiration rate and micrometeorological data collected by ground Internet of Things sensing nodes arranged in a forest stand, geographic coordinate registration and time sequence alignment are performed on the data to form a multi-source observation data set; the method comprises the following steps: extracting vegetation indexes, short-wave infrared reflectivity, soil moisture and rhizosphere physiological parameters based on a multi-source observation data set, constructing a degradation diagnosis index fusing spectral change, moisture stress and root activity, calculating the degradation degree of each pixel, and generating a degradation level spatial distribution diagram; the area with the degradation degree reaching a set threshold value is determined as a to-be-repaired area; and selecting representative poplar individuals in the to-be-repaired area, measuring the photosynthetic ability of the individuals, and calculating the individual competition intensity in combination with the three-dimensional point cloud data.

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention relates to the technical field of forestry environment monitoring, and discloses a forestry environment monitoring method and system based on big data analysis. The method comprises the following steps: acquiring multi-dimensional environmental parameters such as soil moisture content, vegetation coverage and meteorological factors through a distributed sensor network, and generating a real-time weight coefficient through a dynamic weight distribution engine; and completing anomaly detection by using the space-time correlation analysis model, and triggering a self-adaptive sampling strategy to perform high-density acquisition on an abnormal region. Real-time data and satellite remote sensing data are integrated through a multi-source data fusion algorithm, a forestry environment state matrix is generated, and a reference threshold is dynamically updated in combination with an incremental learning mechanism. And generating a regulation and control instruction set for soil improvement, vegetation maintenance and disaster early warning according to the updated threshold value, and executing and collecting feedback data by the edge computing node. And comparing feedback data with an expected index through a bidirectional verification mechanism, generating system optimization parameters, and returning the system optimization parameters to a dynamic weight distribution engine, thereby realizing accurate monitoring and efficient regulation and control of a forestry environment.

The utility model discloses a soil moisture tensiometer, which belongs to the technical field of soil moisture tensiometers, and comprises a glass tube, a fixing component is arranged on the peripheral side of the bottom of the glass tube, a plug is arranged at the top end of a pressure monitoring device, a mounting sleeve is arranged below the plug, and the mounting sleeve is fixedly connected to the outer side of the top of the glass tube. A water supplementing and exhausting assembly is arranged on one side of the mounting sleeve. According to the water purifier, a worker manually presses a circular plate to drive a fixing rod and a piston to move, purified water stored in a fixing barrel is conveyed into a connecting block, and when the pressure of the connecting block is larger than the elastic force of a spring, water flow of the connecting block can drive a ball head block, a side block and a tooth block to move; at the moment, the liquid is conveyed into the glass tube through the stepped through hole and the one-way water inlet so as to perform water replenishing treatment on the interior of the glass tube, so that the mounting and dismounting work of the plug is reduced, the influence on the sealing performance of the plug after a long time is prevented, and the influence on the monitoring precision of a product is further prevented.

The invention relates to the technical field of environment monitoring, in particular to an intelligent digital rural environment monitoring system and method based on big data, and the system comprises a layered water potential collection module, a gradient reverse correction module, a path inversion modeling module and a multi-dimensional diffusion modeling module, and the layered water potential collection module calculates and generates rural soil environment monitoring data; the rural soil environment monitoring data comprises layered soil water potential distribution, soil water content gradient and a soil conductivity characteristic value, and the soil water potential abnormal flow data comprises a vertical water potential difference value, an abnormal water flow direction vector and a water flow intensity grade; the moisture permeation path distribution diagram comprises a permeation path distribution structure, a soil water potential accumulation interval and a water flow propagation direction identifier, and the diffusion boundary space model comprises a pollutant diffusion boundary range, a moisture transverse migration region and multi-dimensional diffusion rate distribution. According to the scheme, the abnormal distribution range, the duration time and the development trend are quantitatively described, and the research and judgment accuracy of the abnormal penetration and diffusion process is improved.

The invention relates to the technical field of flood forecasting, in particular to a flood forecasting method, device and equipment based on field monitoring and real-time modelling, and the method comprises the steps: collecting a topographic data file and an earth surface data text of a target area to construct a sub-meter DEM digital elevation model; radar real-time base data, radar and ground rainfall historical observation data, brightness temperature data and geographical coordinate information of a target area are obtained to solve actual radar real-time base data, rain short-term forecast data and soil moisture data, and then the data are utilized to calculate a preset hydrological model to obtain a mountain torrent gully basin distributed hydrological model result; and establishing a hydrological-hydrodynamic coupling model according to the sub-meter DEM digital elevation model and the torrential flood gully basin distributed hydrological model so as to forecast the current flood state and the future flood state. Therefore, the problems that an existing mountainous area hydrological model cannot completely reflect the current state of a drainage basin, and the simulation error of a hydrodynamic model is large are solved.

The invention relates to an organic matter near infrared spectrum inversion method and device for removing moisture based on radiation. The method comprises the steps that after a soil sample is pretreated, multiple moisture content gradients are regulated and controlled, the natural moisture permeation state is simulated, visible light-near infrared spectrum data and the organic matter content are collected, and a soil spectrum library is constructed; quantifying the interference weight of moisture on the spectrum by using a physical radiation transmission model to obtain a preliminary correction spectrum; moisture and organic matter features are extracted through a deep learning multi-branch architecture, a moisture characterization index is constructed, cross-domain feature alignment is realized in combination with a domain adversarial network, and an accurate correction spectrum is obtained; all the modules are integrated to construct a soil moisture elimination reconstruction spectrum model, after layered verification and iterative optimization, a to-be-detected soil spectrum is input into the model, organic matter content near infrared spectrum inversion is completed, and a result is output. According to the method, moisture interference is removed through combination of physical radiation and deep learning, the inversion precision is improved, and the method is suitable for rapid detection of organic matters in various moisture-containing soil.

A regional soil moisture monitoring method based on airborne spectral reconstruction optical satellite remote sensing, the method comprising: acquiring satellite remote sensing data of a region to be monitored, and collecting unmanned aerial vehicle remote sensing data of a local region in the region to be monitored; on the basis of the unmanned aerial vehicle remote sensing data of the local region, performing spectral reconstruction on the satellite remote sensing data of the region to be monitored, so as to obtain reconstructed satellite spectral data of the satellite remote sensing data; and inputting the reconstructed satellite spectral data into a pre-trained soil moisture inversion model, so as to obtain the soil moisture content of the region to be monitored. Therefore, soil moisture is accurately and quickly monitored.

The invention discloses a soil water content prediction method, a soil water content prediction device, soil water content prediction equipment and a computer readable storage medium based on a spectral analysis technology. The method comprises the following steps: acquiring target spectral data of a target soil area and standard spectral data of the target soil area after drying; performing optimal coupling spectrum calculation based on the target spectrum data and the standard spectrum data to obtain optimal coupling spectrum data; carrying out wavelength screening processing on the optimal coupling spectrum data by adopting a wavelength optimization algorithm to obtain model input data; wherein the wavelength optimization algorithm is a hybrid algorithm of a competitive adaptive reweighted sampling algorithm and a continuous projection algorithm; reasoning model input data based on the fusion prediction model to obtain a soil water content estimation value; wherein the fusion prediction model is a prediction model obtained by training and fusing an initial partial least square regression model and an initial convolutional neural network model based on spectrum training data.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention relates to a drip irrigation pipe network zoning irrigation control method and system based on multi-objective optimization, and belongs to the technical field of artificial intelligence. The method comprises the following steps of collecting multi-source heterogeneous data and sampling point space coordinates, after abnormal value elimination and normalization processing, splicing features and normalized coordinates to form a joint feature vector, and obtaining irrigation partitions with continuous space and similar features through K-means clustering; constructing a multi-objective optimization model taking the zoning irrigation duration and the inlet target flow as decision variables; solving by adopting a hybrid strategy multi-objective evolutionary algorithm of chaotic mapping initialization and adaptive hybrid variation to obtain a Pareto optimal solution set; and finally, combining an entropy weight method and decision maker preference, determining an optimal irrigation parameter through a superior and inferior solution distance method, and introducing a real-time soil moisture monitoring feedback correction parameter. Partitioned precise irrigation of the drip irrigation pipe network can be achieved, multiple optimization objectives are balanced, the irrigation effect and the resource utilization efficiency are improved, and system energy consumption is reduced.

The invention relates to the technical field of construction resource scheduling, in particular to a water conservancy construction equipment scheduling method and system, and the method comprises the following steps: obtaining on-site point cloud and soil data to construct an environmental resistance field; calculating a dynamic work efficiency matrix by combining the equipment power parameters and the resistance field; generating an initial time sequence chain based on the work efficiency matrix, and calculating an overlapping volume of a three-dimensional space envelope body of the equipment; if there is overlap, the job time is adjusted according to the priority to generate a conflict-free scheduling instruction set. According to the method, the rated work efficiency parameters of different machine types in a specific operation area are corrected in real time by constructing the equipment passing resistance model fusing the terrain gradient and the soil moisture content, the problem that static scheduling cannot adapt to environmental changes is solved, and meanwhile space-time conflict detection logic based on the three-dimensional operation envelope surface is introduced; and when it is recognized that the multi-machine moving tracks are overlapped, avoiding or peak shifting instructions with priorities are automatically generated, and conflict-free collaborative operation of the equipment cluster in a complex water conservancy scene is achieved.

Systems and methods for measuring soil moisture are provided. The system may include a neutron sensor configured to be disposed at a sampling location and including one or more neutron detectors configured to detect fast neutrons and thermal neutrons at the sampling location, and a computing device including at least one data processor and a memory storing instructions, the instructions are configured to cause the processor to perform operations including receiving a first electrical signal characterizing a fast neutron count at a sampling location and a second electrical signal characterizing a thermal neutron count at the sampling location from the neutron sensor, determining a ratio of fast neutrons to thermal neutrons present at the sampling location, a soil moisture measurement is determined based on the ratio, and the soil moisture measurement is provided.

The invention provides a slope displacement monitoring system and method based on multi-parameter progressive analysis, and belongs to the technical field of slope monitoring. The system comprises a central processing module, a first-stage monitoring module, a second-stage monitoring module, a third-stage monitoring module and a risk elimination module. The first-level monitoring module collects slope surface displacement data and identifies a potential sliding area. The secondary monitoring module collects stress data of the deep anchor cable and judges the depth of a sliding surface; the third-level monitoring module collects vegetation root system soil fixation force, reinforced material deformation and soil moisture content data and evaluates the displacement inhibition efficiency of ecological slope protection. The system triggers detection according to a progressive sequence of'surface displacement-deep stress-ecological effect ', abnormity of a previous stage is a necessary condition for starting a next stage, the central processing module judges a risk level according to a detection result, and the risk elimination module outputs a corresponding risk elimination strategy. According to the method, closed-loop management of early discovery, fine positioning and strong treatment is realized, and the accuracy and risk prevention and control capability of slope monitoring are improved.

The invention relates to the technical field of big data processing, in particular to a hydrological information forecast data collection automatic processing method, which comprises the steps of multi-source data automatic acquisition and access, data quality intelligent diagnosis processing and abnormity early warning, multi-index comprehensive drought evaluation model construction and drought trend intelligent prediction. GIS spatial analysis and visual display are carried out, and drought evolution is dynamically reflected; and user-defined configuration and adaptive learning are carried out. The equipment state can be displayed, and related personnel can be prompted to maintain the observation station in time; existing hydrological data of the hydrological bureau are fully applied, rainfall, river flow, soil moisture content and other single items are normatively determined to determine the drought level, and meanwhile, according to multiple indexes, comprehensive drought judgment is performed according to the proportion, and based on the GIS technology, an interpolation method and a proximity association method are utilized to determine a Xinjiang whole administrative division plane drought map; and according to the analysis data, automatically sorting out soil moisture content brief report materials.

The invention relates to the technical field of soil moisture content monitoring, and discloses a method and a system for accurately monitoring soil moisture content through remote sensing inversion of an unmanned aerial vehicle. Acquiring original spectral data of different soil types and environmental conditions through a spectral sensor to obtain an initial multispectral image data set; dividing soil types by using a support vector machine classification method according to pixel reflection characteristics to form a soil type grouping set; calculating a multispectral band reflectivity variance for each group, and screening candidate bands to construct a set; processing the candidate wave band set through a random forest regression model, adjusting the weight in combination with an environment variable, and performing iterative screening to form an optimized wave band combination sequence; extracting spectral features of the optimized wave band, and carrying out weighted fusion on the soil variation region to output a processed image; and calculating the soil moisture content by using a linear inversion model based on the pixel reflectivity of the processed image, and outputting a distribution diagram when the deviation reaches the standard. The method is suitable for different soil and environments, the wave band is accurately selected, the precision is improved, and high-quality data is provided for agricultural and environmental governance.

The invention discloses an SMAP soil hydrodynamic downscaling method based on a TOPMODEL theory, and the method comprises the steps: enabling original SMAP data to be matched with DEM data in spatial attributes through standardization processing, and obtaining standardized SMAP data; on the basis of DEM data, a confluence area and a gradient are calculated, and then a terrain index is calculated according to a TOPMODEL model; constructing a time sequence through the standardized SMAP data, and executing space-time stabilization processing to obtain a basic soil moisture field; based on the terrain index and the basic soil moisture field, preliminary soil moisture data is obtained through calculation by means of a downscaling model, and target soil water grating data with high resolution are obtained by optimizing the quality of the preliminary soil moisture data; according to the downscaling method, the structure is simple and clear, batch operation is facilitated, and low-resolution SMAP soil water data can be upgraded into high-resolution soil water data with the same resolution as DEM data in complex terrain areas such as mountainous areas, hills and river valleys.

In a first step, a pretreatment for measuring a moisture characteristic of soil is performed. The pretreatment can be removal of moisture from the soil of interest. For example, removing water that is a cause of a soil mass can be the pretreatment. Next, in a second step, the moisture characteristic of the soil subjected to the pretreatment is measured. The moisture characteristic is a water content of the soil. In addition, the moisture characteristic is a matrix potential value at a specified soil water content. Next, in a third step, the corrosion of the metal member buried in the soil is estimated from the measured moisture characteristic.

The invention provides a crop moisture stress diagnosis method and system based on a fuzzy discrimination theory and multi-source information fusion, and the method comprises the steps: collecting multi-source monitoring data which comprises but is not limited to soil moisture, crop physiology, remote sensing images and meteorological parameters; preprocessing the data, and calculating at least one moisture related index; constructing a membership function of the indexes based on a fuzzy set theory; fusing the indexes by adopting a fuzzy comprehensive discrimination method to obtain a comprehensive moisture stress index; and outputting a mild, moderate or severe crop water stress level according to the index. The system comprises a data acquisition, data processing, fuzzy discrimination and result display modular structure according to the method. By fusing multi-source monitoring data and adopting a fuzzy set theory and a fuzzy comprehensive discrimination method, accurate diagnosis and graded output of crop water stress are realized, the utilization efficiency of water resources is improved, and the method has remarkable water-saving and efficiency-increasing values.

Systems and methods for measuring the content of an element in a soil comprises: a neutron source for irradiating the soil with neutrons; a detector assembly configured to detect an INS gamma spectrum of the soil; an instrument for measuring a moisture content of the soil; and a processor in communication with the detector assembly, the processor configured to: apply a moisture calibration coefficient to calculate a net peak area of a characteristic peak of the element in the INS gamma spectrum, the moisture calibration coefficient calculated to account for the moderation of fast neutrons by hydrogen atoms of water present in the irradiated soil at the location under analysis; and generate a concentration of the element in the soil. The instrument for measuring a moisture content of the soil may comprise the detector assembly. Systems and methods incorporating a temperature-controlled housing for the gamma detector assembly are also provided.

The invention belongs to the technical field of agricultural cultivation, and particularly relates to a film-mulching shallow-burying drip irrigation planting method. The film mulching, shallow burying and drip irrigation planting method comprises the following steps: a preparation stage before sowing; sowing the seeds; performing irrigation; the soil moisture content of the crop planting area is monitored in real time; meanwhile, the evapotranspiration amount of the crop planting area is monitored in real time; when the monitored soil moisture content is lower than 64%-66% of the field water holding rate before sowing and the evapotranspiration reaches the irrigation requirement standard, an irrigation system is started for drip irrigation. According to the film-mulching shallow-burying drip irrigation planting method, through accurate control over water and fertilizer integration, the water resource utilization rate and the crop yield are remarkably increased, meanwhile, environmental friendliness is achieved, and the labor cost is effectively reduced.