54 results about "Moisture stress" patented technology

The invention discloses an irrigation method and system based on a crop growth model, and relates to the technical field of farmland irrigation, and the method comprises the steps: training a growth model based on a time sequence neural network through employing the obtained historical meteorological data, the crop growth process data of a corresponding time period, and the moisture state of a soil root zone; calculating the actual evapotranspiration of the crops by combining the actual moisture state of the soil and the moisture stress coefficient predicted by the growth model, and determining the irrigation amount according to the water deficit and the root depth by combining the current moisture state of the root zone with the stage target moisture content. And the irrigation frequency adaptive to the current environmental condition is calculated by using the meteorological prediction data and the actual evapotranspiration. According to the invention, by introducing the crop growth model based on time sequence modeling, correlation learning among meteorological conditions, soil moisture dynamic changes and crop moisture stress states is realized, so that irrigation decisions can reflect periodic physiological needs of crops on the basis of real-time data.

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

The invention relates to the technical field of digital rural agricultural treatment, in particular to a digital rural agricultural treatment method and system based on artificial intelligence. Obtaining a water demand characteristic parameter of the type of a crop currently planted in the Badzhou field from a crop database; superposing the spatial distribution data of the soil moisture content and the spatial distribution map layer to generate a Badzhou field moisture state map layer; the accurate water supplementing amount of the Badzhou field is obtained through water demand analysis of the Badzhou field with the water stress; an irrigation path is planned according to the position proximity relation and gradient information of all the Buddha fields in the Buddha field moisture state graph layer, and differential irrigation schemes are generated by combining the precise water supplementing amount corresponding to each Buddha field; sending the differential irrigation scheme to irrigation execution equipment, and supplementing water to each palm field; the problem that precise irrigation is difficult due to scattered distribution and large environmental difference of the palm field in the mountainous area is effectively solved.

The invention relates to the technical field of agricultural drought monitoring and precise irrigation, in particular to a crop water stress diagnosis method and system based on sunlight-induced chlorophyll fluorescence, crop canopy oxygen absorption band spectral data is collected through a hyperspectral sensor, and a sunlight-induced chlorophyll fluorescence intensity SIF value is solved by adopting a fluorescence extraction algorithm. The method comprises the following steps: taking an SIF value of a crop canopy without water stress as a reference canopy SIF value, determining a difference between the SIF value of a to-be-detected crop canopy and the reference canopy SIF value, then generating a relative SIF water deficit index SIFWDI by combining a preset formula, establishing a mapping relation between the SIFWDI and a soil water deficit index by utilizing a regression analysis model, and outputting a diagnosis report. Variable irrigation decision making and early warning are triggered according to the stress level, and model parameter self-adaptive optimization is achieved through a feedback mechanism. The system realizes lossless, real-time and high-precision water stress monitoring and irrigation management.

The invention relates to the technical field of agricultural irrigation control, in particular to an intelligent cotton irrigation control method and system based on stem flow data, and the method comprises the following steps: collecting data, calculating a stem flow rate, constructing a monitoring data set, extracting morning data, calculating a morning optical flow conversion coefficient, deducing a noon theoretical potential stem flow rate, and calculating a noon optical flow conversion coefficient. Comparing the theoretical value with the actual value to calculate the instantaneous deficit depth, calculating the deficit width based on the duration continuously lower than the theoretical value, screening the maximum depression depth, multiplying the maximum depression depth by the deficit width to calculate the pore closing resistance index, and constructing an opening instruction when the index is greater than a threshold value. According to the method, the potential rate is deduced through the morning conversion relation, the deficit depth and width are quantified, the pore closing resistance index is calculated to evaluate water stress, the instruction is triggered based on the physiological response, water demand diagnosis is achieved, timely water supplementing is ensured, the irrigation precision is improved, and the photosynthetic efficiency is guaranteed.

The invention relates to the technical field of intelligent irrigation, and discloses an intelligent irrigation system for a fruit tree planting greenhouse. The system comprises a multi-dimensional data acquisition module, a data fusion processing module, an irrigation decision module and an irrigation control execution module which are connected in sequence. The system obtains a root soil image, a leaf surface temperature distribution map and stem micro-deformation data, and performs multi-dimensional fusion calculation to generate a spatial water potential distribution map capable of representing the overall moisture condition of crops. And calculating specific regional water demand and preferential irrigation sites based on the map, and further driving an execution terminal to realize precise irrigation. According to the method, the physiological water demand signal of the crop can be responded, the spatial difference of water stress can be identified, the limitation that the traditional technology depends on single soil information is overcome, the crossing from environment monitoring to crop body sensing is realized, and the irrigation accuracy and the water resource utilization efficiency are improved.

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

The invention relates to the technical field of environment monitoring, in particular to a water-salt ratio dynamic monitoring method and system for farmland, and the method comprises the following steps: collecting multi-point tension and conductivity data, carrying out the ratio judgment, delaying the collection if a threshold value is exceeded, synchronously updating a data set, converting salt, calculating the change of the water-salt ratio, and fitting a trend to obtain change information. Evaluating water stress in combination with crop parameters, judging a water demand period, superposing predicted rainfall to generate an interference result, and calculating a water-salt change acceleration recognition intervention point. According to the method, through the water stress index constructed by fusing the transpiration potential base value of the crop and the water content of the root layer, accurate identification of the water demand period of the crop is realized, comparison with the predicted rainfall time interval is carried out, and trend key points influenced by rainwater disturbance are screened; a continuous acceleration change section is identified through a water-salt ratio acceleration sequence formed by a change rate difference value, and an intervention demand point is marked in time under the condition that a continuous trend is enhanced.

The invention provides a planting method for promoting synthesis of camptothecin and derivatives thereof from nothapodytes pittosporoides. The planting method comprises the following steps: planting a plurality of nothapodytes pittosporoides trees in a planting field; organic fertilizer is applied to the multiple planted nothapodytes pittosporoides trees every first preset time; the planted nothapodytes pittosporoides trees are soaked with cold water for several days, and temperature and water stress is carried out; removing the nothapodytes nothapodytes which do not survive after water stress; introducing microorganisms every second preset time to adjust rhizosphere microorganisms of the nothapodytes pittosporoides trees so as to carry out microorganism stress; watering is stopped in autumn, so that the survival nothapodytes nothapodytes trees are subjected to sunlight stress, and the survival nothapodytes nothapodytes trees enter the sunlight stress period; in the sunlight stress period, synthesis of terpene indole alkaloid of rhizosphere microorganisms is activated, and synthesis of camptothecin and derivatives of camptothecin from nothapodytes pittosporoides is promoted; wherein except for the sunlight stress period, the environment temperature of the planting field is kept at 5 DEG C or above, and the environment humidity is kept at 63%-78%.

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

The present invention discloses a CEEMDAN-based method for screening and monitoring soil moisture stress in farmland, characterised by the steps: preprocessing of remote sensing images, construction of NDVI long time series, CEEMDAN decomposition, calculation of statistical descriptors, screening of soil moisture stress sequences, ground data measurement, construction of soil moisture stress characteristic curves, fitting of soil moisture stress response characteristic curves and predicting the content of soil moisture stress. The invention adopts CEEMDAN decomposition, which solves the problems of noise residue and low reconstruction accuracy in the previous methods, and the high reconstruction accuracy of decomposed component data is more conducive to capturing the transient effects of soil moisture stress, and realizes the screening and extraction of soil moisture stress by combining with the ground measured data. The inverse model of soil moisture content is fitted by combining the effects of multiple indicators, and the CEEMDAN algorithm with remote sensing technology tools to achieve accurate monitoring of soil moisture in a large area of farmland.

The invention provides an alkali-reducing, salt-inhibiting and high-yield planting method for newly-reclaimed severe saline-alkali soil, and belongs to the technical field of plant planting. The method comprises the following steps: laying corn straws at the bottoms of furrows or mixing the corn straws with lower-layer soil at the bottoms of the furrows by adopting a ridge planting and furrow application mode; then applying a modifier in the furrows in a layered manner, and covering with surface soil when reaching the earth surface; ditching and planting alkali-reducing and salt-inhibiting plants at the top of the ridge to which the chemical modifier is applied; the alkali-reducing and salt-inhibiting plant is sesbania and / or oat; the chemical modifier comprises the following raw materials: ardealite and an organic fertilizer; the mass ratio of the ardealite to the organic fertilizer is (10-20): (10-20). According to the method, current-year safe planting and efficient growth of the sesbania and the oats in the newly-reclaimed severe saline-alkali soil can be achieved, and the problem that planting and growth inhibition are affected by saline-alkali stress and water stress in the planting process of the sesbania and the oats in the saline-alkali soil is effectively solved.

The present application provides a sand ecological-hydrological process critical threshold identification system and application thereof, comprising a multi-parameter sample collection device, a soil physicochemical index determination module, a vegetation water stress response monitoring module, a critical threshold calculation unit and a steady state transition early warning module; by determining soil aggregate stability index data and soil water characteristic curve data, monitoring vegetation transpiration conductance data and leaf water potential data, using multi-level lag response analysis and dynamic response surface construction method, calculating critical water content threshold value and steady state transition critical point data, accurate identification and dynamic early warning of sand ecological system steady state transition are realized.

The invention provides an automatic farmland irrigation method based on image acquisition. The method comprises the following steps: integrating acquired multispectral image data, soil moisture content data, meteorological data and topographic data into a multi-source original signal; performing hierarchical and time-sharing preprocessing and feature extraction on the multispectral image data to obtain a crop water stress feature set and a terrain adaptation feature set; performing timestamp synchronous calibration and validity verification on the multi-source original signal to obtain standardized multi-source data; performing fusion calculation on the standardized multi-source data, the crop water stress feature set and the terrain adaptation feature set through a dynamic weight distribution strategy to obtain a crop water demand coefficient; and according to the crop water demand coefficient and the terrain adaptive feature set, determining a subarea accurate irrigation parameter so as to carry out farmland irrigation. According to the method, through synchronous collection of multi-source data, layered and time-sharing preprocessing, feature extraction and dynamic weight fusion, partition precise irrigation parameters are calculated, and the irrigation water-saving and efficiency-increasing level, the irrigation precision and the irrigation uniformity are improved.

The present application relates to the field of ecology, water resources management and remote sensing technology, and provides an integrated estimation method and device for ecological water demand and water supplement. The method comprises: performing deviation correction or fusion processing on precipitation, evapotranspiration and soil moisture content data respectively; determining a reference evapotranspiration based on the obtained consistent driving field and constructing a baseline vegetation correction coefficient, performing residual correction on the correction coefficient under the condition of no water stress, and determining the ecological water demand according to the corrected vegetation correction coefficient and the reference evapotranspiration; inputting the consistent driving field, the ecological water demand and the remote sensing vegetation observation information into multiple discrimination gates, performing logical operation on the discrimination results, and dividing each pixel into a multi-level water supplement priority label; determining the total loss term based on the consistent driving field, the ecological water demand and the water supplement priority label, and determining the ecological water supplement. The problem that the ecological water supplement accounting in the prior art is difficult to meet the water conservation constraint and is prone to systematic bias is solved.

The invention provides bacillus zhangzhouensis and application thereof, and belongs to the technical field of biological agriculture, the bacillus zhangzhouensis is preserved in the China General Microbiological Culture Collection Center (CGMCC), the preservation number is CGMCC No.34248, the preservation number is CGMCC No.34248, and the preservation number is CGMCC No.34248. The bacterial strain and the biological agent containing the bacterial strain have a good effect of improving plant stress resistance (water stress resistance), can maintain basic physiological activities of crop plants under the condition of soil water shortage or waterlogging, can further promote growth of the crop plants, and achieve yield improvement and crop quality improvement. And the bacillus zhangzhou is collected from the natural environment, is safe, environment-friendly and pollution-free, and has a wide application prospect.

The invention discloses a photovoltaic vegetation light-supplementing sprinkling irrigation control system for a stony desertification mountain land, and relates to the technical field of agricultural Internet of Things and ecological environment regulation and control. According to the system, vegetation units under a photovoltaic array are finely divided, and an underground root system imaging technology and multi-dimensional root system structure analysis are combined; precise measurement of key parameters such as the horizontal extension length of the sea-buckthorn vegetation root system, the rhizosphere connectivity, the complexity of a fine root net structure, the capillary water effect and the like is achieved, and the transpiration potential and the water supply condition of the vegetation are comprehensively reflected. The first priority regulation and control area and the second priority regulation and control area are scientifically identified by constructing the transpiration cooperation index and combining the local air humidity, the soil temperature and the photovoltaic shielding intensity which are collected in real time, it is ensured that light supplementing spray irrigation regulation and control are accurately focused on the area with the high transpiration capacity and obvious water stress, and the water resource utilization efficiency and the light supplementing effect are greatly improved. Meanwhile, by dynamically matching the sprinkling irrigation time period and the photovoltaic shielding intensity, collaborative optimization of sprinkling irrigation and light supplement is achieved.

The invention provides a sand ecological-hydrological process critical threshold recognition system and application thereof. The sand ecological-hydrological process critical threshold recognition system comprises a multi-parameter sample collection device, a soil physicochemical index measurement module, a vegetation water stress response monitoring module, a critical threshold calculation unit and a steady-state transition early warning module. The method comprises the following steps: measuring soil aggregate stability index data and soil moisture characteristic curve data, monitoring vegetation transpiration conductance data and leaf water potential data, and calculating a critical moisture content threshold value and steady-state transition critical point data by adopting a multi-level lag response analysis and dynamic response curved surface construction method; and accurate identification and dynamic early warning of steady-state transition of the sand ecosystem are realized.

A data gathering module receives georeferenced data from a UAV over a target area. A processing module receives the captured georeferenced sensor data and analyzes the data to create detailed landscape condition maps using techniques such as machine learning and vegetation indices (NDVI, NDRE). In some embodiments the module automatically identifies and segments different landscape features such as turf, plants, weeds and hardscapes. The data processing module pinpoints specific problem areas or polygonal zones that require remediation for issues such as water stress, nutrient deficiency, weed infestation or pest damage and generates a specific remediation recommendation. An action module generates a set of targeted commands to accomplish the remediation recommendation, and a verification module compares the pre- and post-mediated areas to generate a verification report.

The invention relates to a plant spacing setting method, system and device of a rice transplanter and a medium, and the method comprises the steps: obtaining meteorological prediction data, a water demand interval of a growth cycle of a to-be-planted crop in a preset time period, soil humidity, terrain elevation data and historical environment data of each sampling region, and a historical crop remote sensing index of the crop under the historical environment data; calculating an environmental influence coefficient of each sampling area; determining a moisture convergence path network of the target planting area; determining a soil humidity change sequence of each sampling area in a preset time period according to the meteorological prediction data and the moisture convergence path network; performing numerical value superposition on the soil humidity change values in the soil humidity change sequence and the corresponding soil humidity to obtain a soil humidity sequence; and calculating a water stress risk value of the growth cycle, and determining the planting distance of each sampling area. The rationality of the planting density is improved by setting the plant spacing of the rice transplanter.

The invention discloses a method for regulating and controlling leaf color change of Chinese tallow through water stress. The method comprises the following steps: A, selecting Chinese tallow container seedlings which grow robustly and are free from plant diseases and insect pests; b, performing water stress treatment 20-50 days before the natural color changing period of the Chinese tallow in the container; c, water stress treatment comprises drought treatment in which watering is stopped, semi-submerging treatment in which the container is placed in a water storage device and the water level of the water storage device does not exceed half of the height of the container, and full submerging treatment in which the upper edge of the container is located below the water level of the water storage device; and D, compared with the sapium sebiferum subjected to normal moisture management in the container, the appreciation time of the sapium sebiferum can be advanced by 16-20 days through the drought treatment, the appreciation time of the sapium sebiferum can be advanced by 10-15 days through the half-flooding treatment, and the appreciation time of the sapium sebiferum can be advanced by 21-30 days through the full-flooding treatment. The invention provides three gradient schemes of drought, half flooding and full flooding, the treatment intensity can be flexibly selected according to different landscape requirements and time nodes, and the effect is stable and controllable.

The application discloses a vanilla planting soil modifier and an application method thereof, and belongs to the technical field of soil improvement. The modifier is a core-shell structure particle, the inner core comprises porous biochar and quaternary ammonium salt modified organic bentonite, and the shell layer is a dry-state zinc alginate cross-linked film coated on the surface of the inner core. After being applied to soil, the shell layer is swelled into a hydrogel to seal the inner core pores; when contacting phenolic acid secreted by the vanilla root system, the zinc ions in the shell layer are competitively chelated to cause the degradation of the shell layer, release the zinc element and expose the inner core to adsorb the phenolic acid. The application method comprises implementing moisture stress treatment in the rapid accumulation period of vanilla essential oil to induce the root system to secrete a large amount of phenolic acid to trigger the degradation of the shell layer. The application eliminates the rhizosphere allelopathic toxin, breaks the hydrophobic crust, realizes the targeted release of the zinc element and improves the extraction rate of the essential oil.

The invention relates to the technical field of agricultural monitoring, and particularly discloses a real-time monitoring system for the transpiration amount of forage grass crops. The system comprises a multi-source data sensing module, a stress response correction module, a dynamic self-adaptive calibration module and a transpiration fusion calculation module, and identifies a water stress state and generates a stress correction factor by collecting crop physiology, microclimate and soil data in real time; the initial transpiration estimated value is dynamically calibrated in combination with a meteorological sudden change compensation mechanism, and finally a high-precision real-time transpiration monitoring value is calculated and output through neural network fusion. According to the system, the adaptability to crop moisture conditions and environmental changes is improved, and reliable data support is provided for precise irrigation.

A rice transplanter spacing setting method, system, device and medium, wherein the method comprises: obtaining meteorological prediction data, water requirement interval of a growth cycle of a crop to be planted in a preset time period, soil moisture of each sampling area, topographic elevation data, historical environmental data and historical crop remote sensing index of the crop under the historical environmental data; calculating an environmental influence coefficient of each sampling area; determining a water collection path network of a target planting area; determining a soil moisture change sequence of each sampling area in the preset time period according to the meteorological prediction data and the water collection path network; superimposing the soil moisture change value in the soil moisture change sequence and the corresponding soil moisture to obtain a soil moisture sequence; calculating a water stress risk value of the growth cycle, and determining a planting spacing of each sampling area. The application improves the rationality of planting density by setting the spacing of the rice transplanter.

The invention relates to the technical field of intelligent agricultural irrigation, and discloses a self-adaptive intelligent monitoring and decision-making method for an agricultural water spray sensor, and the method comprises the steps: S1, carrying out the real-time collection and preprocessing of multi-source data; s2, obtaining the basic water demand of crops; s3, correcting in combination with environmental stress to obtain an actual water demand; s4, constructing a water spray decision model and dynamically optimizing the water spray decision model; s5, executing the final water spraying instruction and uploading feedback data; through the step S3 and the step S4, the limitation of traditional single judgment depending on soil humidity is broken through through collected multi-source data, the actual water demand is corrected, so that the real water demand of crops is quantified, meanwhile, a soil water stress coefficient and a meteorological correction factor are introduced, and the accuracy of the water demand is improved. The dynamic influence of environmental factors on water demand of crops is quantified, a water spray decision model is optimized, and a decision-execution-optimization closed-loop feedback mechanism is formed.

The application discloses a kind of intelligent irrigation methods and systems of traditional Chinese medicinal material seedlings based on reinforcement learning, comprising: obtaining the state perception parameter of current seedling cultivation environment, including soil moisture stress degree, seedling growth stage index and canopy wilting representation value;Based on state perception parameter and cumulative water stress degree, obtain the effective component accumulation estimate value of current seedling;State perception parameter and effective component accumulation estimate value are input into pre-trained irrigation decision model, and the irrigation action decision including irrigation duration and irrigation intensity is obtained;After executing irrigation, obtain updated state perception parameter and updated cumulative water stress degree, and calculate updated effective component accumulation value;Reward value is calculated based on the change of state before and after updating, and the irrigation decision model is updated online using reward value.The present application solves the technical problem that the prior art cannot dynamically balance seedling survival guarantee and effective component improvement.

本申请公开了一种基于低空多模态数据的水稻水氮亏缺分离诊断方法及装置，涉及智慧农业监测与农业遥感技术领域，该方法：获取待测区水稻生长参数、冠层多模态数据和环境参数；预处理冠层多模态数据，得到植被指数与作物水分胁迫指数；用C‑Vine Copula函数约束多维生理参数联合分布，耦合ORYZA V3与SCOPE模型构建仿真基准数据集；计算水分胁迫后验概率；超阈值则以水分胁迫为协变量剔除其对光谱干扰，再算氮素胁迫后验概率，未超阈值则直接基于植被指数计算；依据两类后验概率判定胁迫等级；再反演土壤含水率与氮营养指数，结合生育期适宜指标计算出水分、氮素亏缺量。可实现精准、少田间破坏性采样的水氮亏缺分离诊断。

The invention provides a vegetation net primary productivity estimation and prediction method and system based on a CASA model and XGBoost, and relates to the technical field of vegetation net primary productivity estimation. The method comprises the following steps: firstly, preprocessing related data such as remote sensing, climate and land cover and inputting the data into a CASA model to obtain NPP data in a historical period; collecting and processing environment driving data, and training an XGBoost model in combination with historical NPP data to obtain a prediction model; and finally, inputting environment driving data under different climate scenes in the future and outputting a prediction result. According to the method, the moisture stress coefficient is simplified through the earth surface humidity index, the average value of the maximum light energy utilization rate of a Chinese ecosystem research network is adopted, the long-time-sequence and large-scale NPP estimation and prediction precision is effectively improved, and reliable support can be provided for carbon income and expenditure evaluation and ecological protection decision making.

The invention discloses an intelligent decision-making system for precise irrigation of a corn field, and belongs to the technical field of precise agriculture. The system is based on an information geometry theory, corn canopy temperature data is acquired through an unmanned aerial vehicle thermal infrared acquisition module, soil moisture and plant physiological parameters acquired by a ground monitoring module are combined, and after data preprocessing, an intelligent decision-making system is established; the multi-dimensional phenotypic features are mapped to Riemannian manifold by the phenotypic manifold construction module, then key phenotypic features are extracted by the information dimension reduction module, seamless assessment of the moisture state from a single plant to a field is realized by systematically and innovatively utilizing the multi-scale moisture stress assessment module, and the irrigation decision module generates a precise irrigation scheme according to the characteristics of the corn growth period. The execution control module converts the information into an equipment control instruction and executes irrigation operation, the effect evaluation module evaluates the irrigation effect and optimizes system parameters, the visual display module visually displays various information, the water stress evaluation accuracy is remarkably improved to 92-97%, and the spatial resolution is improved by 5-10 times.

The application discloses a kind of evapotranspiration calculation methods considering soil moisture and carbon dioxide influence, specifically: collection flux station observed meteorological information and vegetation water carbon flux information;Carbon dioxide stress function to leaf stomatal conductance and soil moisture stress function to stomatal conductance are constructed;Based on the stress function constructed, canopy conductance parameterization model is constructed, and canopy resistance is obtained;Through the canopy resistance of parameterization land surface process and aerodynamic resistance, the water and heat flux transport model of soil-vegetation-atmosphere continuum is constructed, and latent heat flux is calculated;Through the latent heat flux observed by flux station, the parameters in water and heat flux transport model are optimized;The optimized parameters are substituted into water and heat flux transport model for calculation, and latent heat flux and canopy conductance value are obtained.The application improves the accuracy of evapotranspiration estimation under the condition of rapid environmental change, and provides a reliable tool for water resource management, ecological assessment and climate research.