19 results about "Crop coefficient" patented technology

The present application relates to a kind of water culture crop biomass prediction methods based on stage-specific feedback evapotranspiration calculation, comprising the following steps: according to growth day or accumulated temperature, water culture crop growth is divided into three stages;For current growth stage, calculate stress factor and regulate the weight corresponding to each stage stress factor;Multiply basic crop coefficient, canopy coverage, reference evapotranspiration and stress factor and corresponding weight, calculate stage-specific evapotranspiration;Multiply water productivity adjustment value, the ratio of stage-specific evapotranspiration and reference evapotranspiration and environmental stress factor and corresponding weight, predict biomass increment, obtain water culture crop biomass prediction value;Using ensemble Kalman filter, integrated assimilation canopy coverage and biomass prediction value, carry out sustained biomass prediction in the whole growth cycle of water culture crop.Compared with prior art, the present application has the advantages of biomass prediction accuracy.

The application discloses an irrigation system for adjusting soil moisture by means of meteorological data information, and relates to the technical field of agricultural irrigation. The system comprises a historical meteorological data processing module, a crop water requirement model optimization module, a soil moisture balance model optimization module, an irrigation decision model optimization module, a multi-modal fusion model optimization module, an execution control module and an irrigation device. The system calculates the ET0 value by the Penman-Monteith formula, optimizes the soil moisture model in combination with the precipitation intensity duration correction factor, dynamically correlates the crop coefficient and meteorological elements, constructs the irrigation water function based on the meteorological forecast and real-time monitoring data, introduces the disaster early warning mechanism to adjust the irrigation timing, adopts the historical meteorological feature fusion neural network, reduces the prediction error through cross validation, and realizes the accurate matching of soil moisture and crop water requirement. The application integrates multiple source meteorological parameters, optimizes the simulation precision of water cycle, improves the irrigation efficiency, and improves the efficiency of maintenance of the sprinkler equipment by replacing the sprinkler equipment without shutdown.

The invention provides a water and fertilizer control system and method based on artificial intelligence, and the method comprises the steps: extracting a chlorophyll absorption index of a crop in a current growth stage and a vegetation index of a growth environment from a canopy multispectral image of the crop, and carrying out the nutrition diagnosis of the growth environment of the crop based on the chlorophyll absorption index and the vegetation index. Obtaining the nutrition stress state of each nutrient element, and determining the target fertilizer solution concentration of each nutrient element according to each nutrition stress state; determining the crop coefficient of the crop in the current growth stage, and performing moisture balance on the crop growth environment according to the crop coefficient, the moisture distribution characteristic in the soil moisture content data and the rainfall evaporation characteristic in the meteorological information of the crop growth environment to obtain the net water demand of the crop in the current growth stage; and generating a fertilization control instruction for directly driving the water and fertilizer integrated equipment based on each target fertilizer solution concentration and the net water demand. Based on the scheme, collaborative decision-making of water and fertilizer supply and demand in a crop growth environment can be realized.

The invention relates to the technical field of agricultural irrigation, in particular to a drip irrigation water and fertilizer configuration, calculation and selection integrated system and method, and the method comprises the following steps: 1, calculating the water demand of crops; step 2, calculating irrigation water consumption; 3, designing an irrigation period and an irrigation quota; 4, calculating the flow of the water pump; 5, selecting the type of the filter; and step 6, system integration and verification. The system comprises a data acquisition module, a data calculation module, a decision suggestion module and a system integration module. The meteorological data and the crop coefficient Kc are collected in real time, and the digital twinborn model is constructed, so that the irrigation decision can be dynamically adjusted based on the current environmental condition, the possibility of resource waste and system failure is reduced, and the operation stability of the system is improved.

This invention relates to the field of water demand prediction technology, specifically to an intelligent water demand prediction system and method for rural areas. By quantifying the crop growth process into continuous growth state parameters and constructing a dynamic crop coefficient model based on a sine function, it solves the problems of discontinuous water demand calculation and physiological distortion caused by traditional step-type models during growth stage transitions. It corrects root depth and combines measured soil moisture content with preset field capacity to dynamically constrain effective precipitation, overcoming the shortcomings of existing technologies that ignore basic soil moisture conditions and overestimate the contribution of natural precipitation. By constructing a priority score based on water depletion time and growth sensitivity, it achieves rational scheduling in scenarios with limited water resources across multiple plots, ensuring the continuity of crop physiological water demand and improving the precision of irrigation decisions and water resource utilization efficiency.

The invention belongs to the technical field of intelligent agriculture, and particularly relates to a farmland multi-source data fusion precise water-saving intelligent irrigation decision-making method, which comprises the following steps: acquiring farmland multi-source monitoring data; the method comprises the following steps: dividing a farmland into rasterized spatial infinitesimal elements, constructing a farmland hydrothermal migration heterogeneous graph model, and outputting a whole-field soil moisture content spatial-temporal distribution prediction result; crop growth period parameters and an improved double-crop coefficient method are combined, and crop water demand and water shortage stress levels of different grid units are quantified; an improved self-adaptive weight particle swarm optimization algorithm is adopted to generate a grid-level differential irrigation decision scheme, and meanwhile, an irrigation early warning threshold is self-calibrated through Bayesian optimization; the method is issued to a field edge control node through the low-power-consumption wide-area Internet of Things, an intelligent irrigation execution mechanism is linked to complete precise irrigation, and model parameters and a decision strategy library are updated. Therefore, the problems of insufficient multi-source data integration efficiency, inaccurate farmland hydrothermal heterogeneity description and the like in the prior art are solved.

The invention belongs to the field of irrigation control methods, particularly relates to a vegetable water consumption digital management system, and aims to solve the problems that underground water overdraft is serious in facility vegetable irrigation in water resource-deficient areas such as the Hebei Province, and water consumption is low-efficiency and waste is caused by extensive traditional management modes. According to the system, a correlation model of a vegetable crop coefficient Kc and an effective accumulated temperature Ta is constructed, and in combination with a re-established crop coefficient calculation formula (Kc = ETc / ET0), a relation model of a greenhouse vegetable evapotranspiration estimated value ETc and the effective accumulated temperature Ta is deduced; the crop daily evapotranspiration is estimated by means of data of a single environment collector, the problem that water supply and demand are unbalanced in traditional irrigation decision-making methods such as experience and time sequence is solved through the model method, and irrigation water is converted from quantitative supply to on-demand supply. And a powerful technical support is provided for pressure mining of regional underground water and sustainable development of the vegetable industry.

The invention discloses an intelligent precise irrigation method for matrix cultivation based on environmental parameter optimization. Firstly, the theoretical evapotranspiration is calculated based on the total irrigation amount, the irrigation discharge amount and the total weight of a crop matrix; then, calculating reference evapotranspiration based on a Penman correction formula, and calculating the reference evapotranspiration in different growth stages by utilizing crop coefficients; finally, by taking the reference evapotranspiration of different growth stages as a contrast, optimizing the theoretical evapotranspiration by using the daily average temperature, the daily average photosynthetically active radiation quantity and the daily average relative humidity to obtain a difference function between the reference evapotranspiration and the theoretical evapotranspiration; compensating the theoretical evapotranspiration by using a difference value between the reference evapotranspiration and the theoretical evapotranspiration to obtain an actual evapotranspiration; when the actual evapotranspiration is larger than the allowable water deficit value of the matrix, irrigation is started, and the irrigation amount is the actual evapotranspiration. According to the method, the quantitative relation among the irrigation time, the irrigation amount and the actual evapotranspiration amount is obtained, intelligent and accurate irrigation is achieved, and the problems that the irrigation accuracy is poor, and irrigation is not timely are solved.

The invention belongs to the technical field of agricultural water conservancy and water-saving irrigation, and provides a winter wheat daily crop coefficient calculation method. According to the method, key meteorological factors influencing the evapotranspiration of typical crops in each growth period of the Huaibei plain are extracted through a geographic detector method, a winter wheat growth period crop coefficient model based on meteorology and underground water burial depth is constructed, and the crop evapotranspiration process under different underground water burial depths is simulated. According to the technical scheme provided by the invention, the crop evapotranspiration simulation precision can be improved, a scientific basis is provided for efficient utilization of regional agricultural water resources and daily evapotranspiration simulation, and meanwhile, the blank of considering the underground water level burial depth influence in an existing crop evapotranspiration empirical model is filled.

The invention provides a crop whole-growth-period drought monitoring and early warning method based on an adaptive water shortage index. The method comprises the following specific steps: establishing a regression relationship between a crop coefficient and a leaf area index; calculating the daily potential evapotranspiration; establishing an effective rainfall experience calculation formula, and calculating day-by-day effective rainfall according to the rainfall observed by the meteorological station; constructing adaptive day-by-day-scale crop water shortage indexes considering different crop types and drought-enduring characteristics in different growth stages; setting a crop drought grade division standard based on the adaptive water shortage index, carrying out crop drought identification and grade research and judgment, and achieving the monitoring and early warning of the whole process of crop drought occurrence, development and ending. According to the method, multi-source data such as remote sensing data, meteorological data and crop characteristic data are fused, the problem of water shortage sensitivity difference of different crop types and different growth stages is considered, the adaptive day-by-day scale crop water shortage index is constructed, and the whole drought process of the crop growth stage can be monitored.

The invention relates to the technical field of agricultural intelligent irrigation, in particular to an irrigation decision optimization system and method based on complementation of soil moisture content and meteorological data. Comprising a soil moisture content monitoring unit used for collecting layered soil moisture content and temperature in real time; the meteorological data acquisition unit is used for acquiring local actually measured meteorological parameters and integrating third-party remote sensing rainfall and evapotranspiration prediction data; the data fusion and processing unit is used for modeling the multi-dimensional data by using a machine learning model and extracting a field evapotranspiration feature vector; the decision optimization unit is used for analyzing the deviation degree of the actually measured moisture reduction rate and a theoretical predicted value based on a machine learning algorithm, dynamically correcting the crop coefficient in the current growth period and generating a precise irrigation instruction in combination with a water balance model; and the execution control unit is used for realizing closed-loop irrigation management and checking the infiltration effect through the intelligent ecological gateway. According to the invention, through deep fusion of the actually measured soil data and the weather forecast data, the defect of crop coefficient staticization in a traditional irrigation decision is corrected.

The invention discloses a water resource allocation scheduling method. The method comprises the following steps: mapping a sub-basin-plaque water demand grid of a distributed water demand model into a water demand boundary vector consistent with a pipe network node-segmental arc topology through a double-topology isomorphic mapper; a double-rate Runge-Kutta asynchronous coupler is embedded in a time axis, the macroscopic water demand trend and the microcosmic hydraulic fluctuation are alternately integrated, and an asynchronous flow driving sequence synchronous with the valve beat is generated; the sequence is input into a pipe network hydraulic solution kernel to calculate pressure, water age and water quality, and real-time calibration is carried out; and reversely correcting the crop coefficient and the soil water content through the adjoint sensitivity matrix, carrying out closed-loop iteration until error convergence, and finally outputting a valve regulation and control instruction to realize water demand-hydraulic heterogeneous closed-loop scheduling. According to the invention, efficient, accurate and closed-loop linkage between distributed day-by-day water demand and centralized real-time hydraulic power can be realized.

The invention discloses an urban green land intelligent monitoring system and method based on the Internet of Things, and relates to the field of green land monitoring, and the method comprises the steps: dividing a plant growth cycle through setting time windows, collecting growth data of each window to generate a record table, and carrying out the comparison and extraction of difference parameters; on the basis of the difference parameters, screening positive correlation data as evaluation indexes, calculating growth evaluation scores after standardizing the evaluation indexes, determining a difference threshold by combining historical data, and dividing growth stages according to the difference threshold; acquiring data through a weather station, calculating evapotranspiration, calculating water demand by combining crop coefficients, and drawing a time-varying curve of the water demand; after curve data are preprocessed, a polynomial algorithm is used for curve fitting, and a water demand prediction model is constructed; in combination with real-time weather and growth stages, the irrigation amount is determined according to the water demand predicted by the model, the result is evaluated after irrigation, the model is corrected if the result is unqualified, and urban green land plant growth monitoring and precise irrigation management are achieved.

This invention discloses a method and system for generating water-saving agricultural irrigation decisions based on multi-source data fusion, relating to the field of intelligent agricultural irrigation technology. The method includes: acquiring soil moisture data, meteorological data, and multi-source remote sensing data for a target field to form a multi-dimensional data matrix; using the physical process of soil moisture transport as a constraint, fusing the soil profile water content of each field grid according to the physical semantics and spatial representativeness of each data source to obtain the fused soil state and its uncertainty; dynamically determining the crop coefficient and its uncertainty based on meteorological data, the fused soil state, and crop remote sensing data; substituting the fused soil state, crop coefficient, and meteorological elements into a crop water requirement estimation model to obtain the crop water requirement and its uncertainty for each field grid; and generating irrigation decision instructions based on the crop water requirement. This invention, through multi-source data fusion and full-link uncertainty propagation, enables refined and quantitative characterization of the reliability of agricultural irrigation decisions.

This invention relates to the field of plant canopy transpiration estimation, specifically a method for estimating canopy transpiration in shallow-rooted plants. The invention collects readily available surface environmental factors such as total radiation, vapor pressure deficit, soil moisture content at 20cm depth, canopy leaf area index, and wind speed. It then uses the envelope method to extract the corresponding standardized canopy transpiration rate values ​​and fits control functions one by one. Finally, it constructs a canopy transpiration estimation model by multiplying the output values ​​of each control function, the leaf area index, and the canopy transpiration rate per unit leaf area. This effectively avoids the shortcomings of traditional Penman-Monteith-type models, which require a large number of difficult-to-obtain parameters such as crop coefficients and canopy impedance, significantly lowering the barrier to model use. Furthermore, considering the root distribution characteristics of shallow-rooted plants, it uses soil moisture content at a depth of 20cm to improve the accuracy of canopy transpiration estimation.

The application discloses a kind of desert photovoltaic power station area actual evapotranspiration estimation method, steps include:1) collection relevant data;2) calculate daily scale reference evapotranspiration;3) calculate monthly scale reference evapotranspiration ET o ;4) calculate monthly scale soil water storage change ΔS;5) calculate monthly scale precipitation P;6) calculate monthly scale actual evapotranspiration ET;7) calculate crop coefficient K c ;8) calculate daily scale actual evapotranspiration ET, namely, this application method, only need meteorological observation equipment and soil observation equipment to collect basic data, can accurately obtain the dynamic characteristics of daily actual evapotranspiration on long time sequence;It can also monitor meteorological and soil element data simultaneously, facilitate overall water balance analysis, to clarify the water cycle process in the background of climate warming in northwest arid region, to provide strong support for further exploration of arid region water cycle research.

The invention relates to the technical field of drip irrigation control, and discloses a drip irrigation system for towel gourd cultivation, which comprises an atmosphere demand simulation unit, a soil humidity sensing unit, a drip irrigation execution unit and a main controller, the main controller takes a feedforward demand signal generated by the atmosphere demand simulation unit and a dynamically updated crop coefficient as a main basis for irrigation, and performs safety correction by using a feedback correction signal VWC generated by the soil humidity sensing unit; the main controller further utilizes the historical irrigation amount, the soil humidity net variation VWC and the time integral value of the feedforward demand signal to continuously update the crop coefficient through water balance calculation.

The invention relates to the technical field of irrigation, in particular to a high-time-sequence plot-level crop coefficient estimation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining remote sensing image data in a target region, carrying out the preprocessing, and generating a high-time-sequence NDVI data set and a high-spatial-resolution NDVI data set; on the basis of the high-time-sequence NDVI data set and the phenological characteristics of the crops, the types and distribution of the crops in the target area are recognized, and sample points corresponding to the crops are selected; according to the method, an NDVI data set of crop key growth nodes is constructed, a high-time-sequence plot-level NDVI sequence is reconstructed by using a double logistic model, a segmented crop coefficient and vegetation growth state index relation model matched with crops and regions is established in combination with regression analysis, and finally day-by-day, continuous and dynamic estimation of the crop coefficients on the plot scale is realized. Reliable technology and data support is provided for regional precise irrigation, agricultural water resource efficient management and drought monitoring.

The invention discloses an irrigation water demand calculation method based on unmanned aerial vehicle growth monitoring results. The method specifically comprises the steps that an unmanned aerial vehicle obtains multispectral images including red light, green light, near-infrared light, red edges and the like and transmits the multispectral images to a processing terminal; carrying out splicing, radiometric calibration (DN-to-reflectivity) and geometric / ortho-rectification on the images to generate a reflectivity ortho-image; indexes such as NDVI, NDRE, OSAVI and selectable MSI are calculated pixel by pixel, LAI is inverted, a crop coefficient Kc is obtained through Kc = f (LAI), and a moisture stress coefficient beta is obtained according to beta = g (MSI); inputting reference evapotranspiration ETo to calculate ETc = ETo * Kc, generating irrigation water demand IR in combination with the selectable effective rainfall Peff, and outputting a meter-scale prescription map; and further converting the IR into a target water application amount / flow qtar, generating a valve opening or nozzle duty ratio instruction according to an irrigation equipment calibration curve, and setting an amplitude limit and a change rate limit to realize differentiated precise water supply. The implementation difficulty is reduced, the calculation efficiency is improved, precise irrigation is realized, and the decision reliability is enhanced.