98 results about "Moisture stress" patented technology

The invention provides a high-standard farmland water and fertilizer integrated intelligent regulation and control method and system, and the method comprises the steps: obtaining terrain point cloud data and crop canopy water stress data of a high-standard farmland, generating an elevation gradient distribution feature set, and generating an elevation model according to the set; analyzing the crop canopy moisture stress data, and calculating a moisture stress index; performing spatial position matching on the terrain difference parameters and the micro-terrain difference parameters to generate a terrain compensation parameter set; establishing a time sequence optimization correlation model in the 5G edge computing node, and optimizing a pulse irrigation time sequence factor and a flow distribution weight by using the time sequence optimization correlation model according to the terrain compensation parameter set and the historical irrigation record so as to generate a multi-target irrigation control instruction; performing high-standard farmland water and fertilizer integrated intelligent regulation and control according to the multi-target irrigation control instruction and the water and fertilizer migration rate parameter corresponding to the microtopography difference parameter; water and fertilizer are accurately distributed according to needs, and the irrigation uniformity, the resource utilization efficiency and the crop yield are improved.

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

The invention relates to the technical field of water-saving irrigation, in particular to a drought risk management-based corn planting water-saving irrigation system, which comprises a crop stress state monitoring module for acquiring a corn canopy temperature and an environment temperature and calculating a temperature difference value between the canopy and the environment. According to the method, the actual stress state of the crop is quantified by acquiring the difference value between the corn canopy temperature and the environment temperature in real time, and the crop water stress index which is dynamically calculated is combined, so that the water stress identification precision and the response time efficiency are improved, and the problems of irrigation decision lag and blindness are avoided; further, the real evapotranspiration demand of crops is accurately judged by dynamically monitoring the change rate of latent heat flux, misjudgment possibly caused by single monitoring of temperature difference is avoided, and the irrigation demand judgment accuracy is ensured; meanwhile, the prices, the flows and the available amounts of different water sources are comprehensively optimized, the taking proportion of multiple water sources is configured with the lowest total irrigation cost as the standard, water source economical efficiency optimization becomes possible, and irrigation operation cost and resource consumption are effectively reduced.

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 belongs to the technical field of seedling raising equipment, and relates to a rice three-dimensional seedling raising factory irrigation method and device. Performing pretreatment; extracting main environment characteristics; obtaining prediction data; obtaining an irrigation strategy; irrigation is performed after the irrigation strategy is adjusted. The method comprises the following steps: firstly, collecting environmental data and historical irrigation data in a three-dimensional rice seedling raising factory, then preprocessing the environmental data to obtain preprocessed environmental data, carrying out dimension reduction processing on the preprocessed environmental data, extracting main environmental characteristics, predicting the change trend of soil humidity and temperature according to the main environmental characteristics to obtain prediction data, and finally obtaining the prediction data. According to the method, prediction data and historical irrigation data are combined and optimized by using a particle swarm optimization algorithm to obtain an irrigation strategy, and finally irrigation is performed after the irrigation strategy is adjusted by using a depth Q network according to continuously updated environment data, so that the water demand of seedlings can be accurately adjusted in a complex environment, and water stress on the seedlings is avoided.

The invention discloses an agricultural drought monitoring method for a humid and semi-humid area. The method comprises the following steps: acquiring a day-by-day soil moisture content historical time sequence of a drought generation year of a research site; according to the day-by-day soil moisture content historical time sequence of the research site, estimating the field moisture capacity and the wilting moisture content of various soil textures, and calculating the soil effective moisture content of the various soil textures; according to the crop irrigation water amount and the actual monitoring value of the soil water content in the key growth period of the dry crops, obtaining the soil water content when the dry crops are subjected to water stress; calculating agricultural drought monitoring indexes of the dry crops in the humid and semi-humid areas according to the soil water content when the dry crops are subjected to water stress and the soil effective water content of the corresponding soil texture, and determining agricultural drought severity grade division according to the STAD value for representing the agricultural drought grade of the research area so as to realize drought monitoring. According to the method, the problem that other indexes fail in drought monitoring of a wet or semi-wet area can be solved, and the method has relatively high universality.

The invention provides a crop evapotranspiration prediction method and device, equipment, a storage medium and a program product. The method comprises the steps of determining reference evapotranspiration data based on evapotranspiration input data; determining the relationship between the soil water potential and the soil moisture stress coefficient based on the evapotranspiration measured data and the reference evapotranspiration data; and based on the reference evapotranspiration data and the relationship, predicting the crop evapotranspiration of the to-be-predicted region to obtain a crop evapotranspiration prediction result. According to the method, reference evapotranspiration data is determined through evapotranspiration input data obtained from a to-be-predicted area, evapotranspiration measured data obtained from the to-be-predicted area are used for jointly determining the relation between the soil water potential and the soil moisture stress coefficient, and finally the crop evapotranspiration amount of the to-be-predicted area is predicted based on the reference evapotranspiration data and the determined relation. And obtaining a crop evapotranspiration prediction result. According to the method, the abiotic stress condition of the soil is effectively judged through the soil water potential, and the accuracy of crop evapotranspiration prediction is improved.

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

The invention provides a garden plant irrigation control method and system, and relates to the technical field of garden plant irrigation control, and the method comprises the steps: periodically collecting a canopy image of a target plant in a garden scene through a visual perception device, carrying out the preprocessing and feature extraction of the image, and obtaining the plant visual features of the target plant; then, according to the visual features and water shortage judgment standards preset for different plant types, whether the target plant is in an acute water stress state or not is judged. Once it is confirmed that the plant is in an acute water stress state, the system immediately triggers emergency irrigation of the area where the target plant is located, and the problem that in the prior art, an automatic irrigation system excessively depends on environmental parameter measurement and weather prediction when facing sudden extreme weather, and the irrigation efficiency is high is effectively solved. The acute water stress of the plant cannot be found and responded in time.

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 a wheat root system growth model established through root system-rhizosphere-soil coupling by using water-carbon flux and a method thereof, and the wheat root system growth model is established through root system-rhizosphere-soil coupling by using water-carbon flux on the basis of response of wheat root system parameters, root system density and root system activity to moisture. Therefore, a crop root growth model system with applicability is formed. The wheat root growth model is further coupled with a rhizosphere model on the basis of CA and L-System, and a root-rhizosphere-soil model with water carbon flux as an internal relation is constructed, so that the tobacco root growth model is fully applied and developed. Model results show that root mean square error (RMSE) of test values and model values of root length distribution of moisture stress treatment and moisture sufficient treatment are 0.0261 and 0.0396 respectively, model precision R2 is 0.9179 and 0.8758 respectively, simulation precision is relatively close, and precision is very high.

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 discloses a deep-rooted plant underground carbon sink estimation method, and relates to the technical field of plant ecology, and the method comprises the steps: determining an underground biomass estimation model corresponding to a target moisture stress condition as a target underground biomass estimation model; determining an overground growth parameter corresponding to an independent variable of the target underground biomass estimation model as an overground growth parameter to be measured; acquiring a to-be-measured overground growth parameter value of the to-be-measured deep-rooted plant; inputting the to-be-measured overground growth parameter values of the to-be-measured deep-rooted plant into the target underground biomass estimation model to obtain the underground biomass of the to-be-measured deep-rooted plant so as to determine the underground carbon sink of the to-be-measured deep-rooted plant, and performing different water management in the stable growth period of the alhagi sparsifolia so as to determine the underground carbon sink of the to-be-measured deep-rooted plant. The correlation between the aboveground growth parameters of the alhagi sparsifolia under different water stress conditions and the underground biomass is compared, estimation of the underground biomass can be completed on the premise that the whole deep-rooted plant is not excavated, and finally the underground carbon sink is derived by using the underground biomass conversion carbon reserve coefficient.

The invention belongs to the technical field of crop irrigation control, and particularly discloses a multi-modal water-saving sprinkling irrigation system based on the Internet of Things, which comprises a data acquisition module, a stress analysis module connected with the data acquisition module, a sprinkling irrigation control module connected with the stress analysis module and a fault processing module connected with the sprinkling irrigation control module, the embedded stem micro-change sensor and the thermal infrared imager are cooperated to monitor the physiological dynamic state of crops in a multi-mode manner, the occurrence of water stress can be accurately identified in an early stage, a capillary pulse spray irrigation mode is automatically started when the water stress is identified, and on-demand, accurate, small-flow and high-frequency water supply is realized; the timeliness and physiological suitability of irrigation response are remarkably improved, the appropriate water potential of the root zone can be effectively maintained, efficient absorption and utilization of water are promoted, waste of water resources is reduced, and meanwhile normal physiological metabolism of crops 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 invention belongs to the technical field of sugarcane cultivation, and discloses a sugarcane drought stress treatment device which comprises a bottom frame, a mounting bin is fixedly mounted on the inner wall of the bottom frame, a planting cylinder is rotatably mounted at the top of the mounting bin, the planting cylinder is divided into four sections, the length of each section is four meters, and the planting cylinder is of a high-strength aluminum alloy sleeve structural design; obstacle avoidance mechanisms are arranged on the inner walls of the planting cylinders, and stirring mechanisms are arranged on the outer walls of the planting cylinders; by arranging the obstacle avoidance mechanism and designing a threaded groove and a cylindrical groove in the inner wall of the planting barrel, definite growth guidance is provided for sugarcane roots, the roots are guided to grow downwards along the threaded groove through the design, the problem of horizontal coiling of the roots is effectively avoided, the planting barrel is driven by the motor to rotate, and the planting efficiency is improved. Teeth in the obstacle avoidance mechanism work cooperatively with a gear transmission system, effective management of growth of sugarcane minor roots is achieved, and relevant index data of sugarcane drought resistance stress in different water stress states can be accurately obtained.

The invention provides a water stress monitoring method and device, a storage medium and electronic equipment, and relates to the field of ecological safety and environment. Wherein the electronic device obtains the sapwood areas of a plurality of trees in a research area, and the sapwood of each tree is a tissue used for conducting moisture; obtaining the water consumption of each tree in the research area according to the sapwood area of each tree in the research area; acquiring soil humidity of the research area; and determining whether water stress exists in the research area according to the water consumption and the soil humidity. Therefore, the water consumption and the soil humidity of each tree in the research area can be obtained, the moisture condition of the vegetation in the research area can be monitored and analyzed by comparing the water consumption and the soil humidity, and the moisture stress problem can be found and solved in time, so that healthy growth of the vegetation in the arid area is guaranteed, and the stability of the ecological environment is maintained.

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 invention provides a southern irrigation area fruit tree irrigation method based on space-air-ground integrated monitoring. The southern irrigation area fruit tree irrigation method comprises the steps that space-air-ground facility equipment is used for collecting data of the surface temperature, humidity, fruit tree transpiration intensity and soil moisture content of an irrigation area; constructing a time sequence fusion data set for the space-air-ground multi-source data, and generating a high-precision soil moisture content distribution map; day-by-day weather and fruit tree growth data are obtained, and subarea division is carried out on the fruit tree park according to soil texture and fruit tree plant distribution; constructing a fruit tree water demand model, constructing an LSTM rainfall forecasting model, and constructing an irrigation amount forecasting model based on a water amount balance principle; a decision-making system of feedforward prediction and feedback decision-making is constructed, and an irrigation decision-making instruction is issued to a water taking valve attached to the high-level water tank for irrigation by means of an internet-of-things platform; analyzing the water stress condition of fruit tree canopies according to an unmanned aerial vehicle thermal infrared imaging technology, and optimizing a fruit tree water demand model; and establishing an irrigation expert knowledge base, and iteratively updating an irrigation strategy.

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.