131 results about "Irrigation management" patented technology

The invention relates to an irrigation amount control method and system for prune cultivation. The method comprises the steps of performing timestamp calibration and dynamic weight distribution processing according to soil data, meteorological data and transpiration data to obtain a multi-modal data set; based on the multi-modal data set, microclimate abnormal event detection and soil humidity correction processing are carried out, and anti-interference soil water content data are obtained; according to the anti-interference soil moisture content data and preset prune growth stage parameters, carrying out root system layered water absorption dynamic modeling treatment to obtain a deep root system moisture demand prediction value; according to the deep root system moisture demand prediction value and the transpiration data, irrigation amount dynamic planning processing is carried out, and the target irrigation amount is obtained. According to the method, through multi-source data fusion, root system water absorption modeling processing and dynamic irrigation planning, the target irrigation amount of prune cultivation can be accurately determined, the accuracy and scientificity of irrigation management are remarkably improved, and powerful guarantee is provided for high-quality growth of prunes and efficient utilization of water resources.

The invention belongs to the technical field of garden irrigation, and particularly discloses a garden landscape intelligent irrigation management system which comprises a sensing layer, a transmission layer, a platform layer and an execution layer. The sensing layer is used for collecting garden environment and plant related data; the transmission layer is used for transmitting the data acquired by the sensing layer to the platform layer; the platform layer is used for realizing data visualization, intelligent decision generation and equipment control; the execution layer executes irrigation operation according to an instruction of the platform layer; according to the system, through accurate monitoring and intelligent control, water resource waste and irrigation cost are effectively reduced. Manual inspection and operation workloads are reduced through automatic operation, one administrator can manage thousands of mu of gardens, and the management efficiency is improved. The risk of plant diseases and insect pests can be reduced through on-demand irrigation, and the stability and attractiveness of the garden landscape effect are kept. The environment, irrigation and plant growth data accumulated by the system provides a scientific decision basis for garden maintenance, and is convenient for long-term planning and management strategy optimization.

The present invention discloses a multi-modal system for real-time plant hydration monitoring and irrigation management. The system comprises at least one acoustic sensor configured to emit controlled sound waves through plant tissues and detect corresponding vibrations and resonance frequencies using sensitive microphones or piezoelectric sensors to assess plant hydration. The system includes at least one bioelectrical sensor for monitoring bioelectrical signals. At least one nanotechnology-based sensor is configured to detect molecular changes in water content within plant cells through embedded or externally applied nanosensors. An aerial imaging device mounted on an unmanned aerial vehicle captures data related to canopy temperature, leaf color, and hydration indicators using infrared, multi-spectral, and thermal cameras. A central processing unit receives and analyzes data using predictive models, integrates the data via data fusion algorithm to generate a plant hydration profile, and controls water delivery through an irrigation management unit based on the hydration profile.

The invention provides an irrigation district water resource scheduling optimization method based on cloud computing, and the method comprises the following steps: collecting irrigation district multi-source data in real time through a space-air-ground integrated sensing network, and uploading the data to a cloud platform after preprocessing; the method comprises the following steps: constructing digital twin bodies of an irrigation area in a cloud platform, dividing the irrigation area into a plurality of sub-areas according to water consumption attributes, and integrating hydraulics and hydrological agriculture models; real-time data and future weather information are fused, and a water demand prediction value is generated through a water demand prediction model by taking the sub-region as a unit; a scheduling scheme is generated by adopting a multi-objective optimization algorithm based on the predicted value, and the minimum economic benefit, water resource utilization rate and energy consumption are taken as optimization objectives; and performing simulation evaluation on the scheme by using the digital twinborn body, outputting a performance index aided decision, and finally determining an optimal scheduling scheme. According to the method, accurate prediction, dynamic optimization and intelligent scheduling of the water resources in the irrigation area are realized, and the utilization efficiency of the water resources and the irrigation management level are improved.

The invention discloses an intelligent irrigation dynamic decision-making method based on reference crop evapotranspiration estimation. The method comprises the following steps: firstly, acquiring historical day-by-day weather data of a farmland area, calculating the evapotranspiration of a reference crop, and preprocessing the evapotranspiration; secondly, weather factors are screened to serve as input features, reference crop evapotranspiration serves as a target variable, and a reference crop evapotranspiration estimation model is constructed and trained; and then, through a reference crop evapotranspiration estimation model, obtaining reference crop evapotranspiration estimation results of the current day and the future n days. And finally, according to an evapotranspiration estimation result in combination with a crop coefficient corresponding to the growth stage of the rice, evapotranspiration of the current day and n days in the future is calculated, an irrigation decision is made, and the water storage capacity of the reservoir is adjusted based on the irrigation water consumption of the current day and the irrigation water consumption of the n days in the future. The invention provides an efficient, economic and sustainable technical scheme for intelligent irrigation management of large-scale farmland, and has important value for promoting agricultural precision and ecological development.

The invention belongs to the technical field of irrigation management and control, and provides a multi-source sensing data-driven irrigation area water demand prediction and accurate water distribution method. The method comprises the steps of obtaining multi-source data of a target irrigation area when a preset irrigation condition is met; performing quantitative evaluation on the first soil water content data and the second soil water content data based on the meteorological data to determine respective confidence degrees, and giving dynamic weights based on the confidence degrees, the temporal and spatial variation characteristics and the actual condition of the irrigation area; performing fusion processing on the first soil water content data and the second soil water content data based on the dynamic weight to obtain third soil water content data; on the basis of the third soil water content data and the crop growth stage data of the target irrigation area, the water demand of the stage is predicted; and generating a control instruction of the intelligent valve of the water delivery canal system based on the water demand. The accuracy of water demand prediction can be remarkably improved, the problems of water resource waste and insufficient supply are reduced, and efficient utilization and scientific management of water resources in an irrigation area are achieved.

The invention provides a garlic planting full-process database service system based on agricultural big data, which relates to the technical field of agricultural planting and comprises a planting planning module, a seeding management module, a growth monitoring module, a topdressing irrigation management module, a pest control module and a harvesting decision module. The planting planning module accurately matches varieties and base fertilizer formulas according to soil and yield data; the seeding management and growth monitoring module dynamically optimizes planting measures in combination with real-time data and historical experience; the topdressing irrigation module, the pest control module and the harvesting decision-making module utilize technologies such as image recognition and spectrum detection to realize a precise and scientific management strategy, effectively improve the resource utilization rate, reduce the influence of environmental fluctuation on garlic growth, reduce the use of chemical agents, realize full-process data driving by integrating multi-source data, and improve the management efficiency. Meanwhile, a complete planting file is formed to support strategy iteration, large-scale and efficient development of garlic planting is facilitated, and then the economic benefits of garlic planting are effectively improved.

The invention discloses a winter wheat multi-target irrigation dynamic optimization method based on a crop model and artificial intelligence, and belongs to the field of agricultural intelligent irrigation optimization and decision, the method comprises the following steps: preprocessing meteorological data, soil data, crop parameter data and irrigation management data to obtain standardized input data; carrying out coupling processing on the AquaCrop model and the PyFAO56 model based on the standardized input data to obtain final output of a coupling model; based on the final output of the coupling model, optimizing decision variables by adopting an NSGA-2 multi-objective optimization algorithm to obtain a Pareto optimal solution set; and sorting the candidate schemes in the Pareto optimal solution set by combining a fusion strategy of an entropy weight method and a random forest algorithm to obtain a final irrigation strategy. According to the invention, through multi-objective optimization, objective weighting and machine learning intelligent evaluation, a novel precise irrigation scheme considering both yield and water efficiency is provided.

The invention discloses a soil humidity sensing system and method based on millimeter wave radar, and relates to the technical field of agricultural Internet of Things and soil monitoring. In order to solve the technical defect that a soil humidity sensing system suitable for various agricultural environments is urgently needed in the prior art, the technical scheme provided by the invention comprises the following steps: acquiring millimeter wave radar echo signal intensity in a training stage; performing frequency domain transformation on the echo signal intensity, and constructing a two-dimensional signal intensity matrix with a frequency dimension and a virtual antenna dimension; constructing a neural network model based on the signal intensity matrix, and training the model according to the corresponding real soil humidity value; acquiring millimeter wave radar echo signal intensity in a test stage, constructing a corresponding signal intensity matrix, and inputting the signal intensity matrix into the trained neural network model for soil humidity prediction; and outputting the predicted soil humidity value as a sensing result. The system can be applied to non-contact real-time monitoring and precise irrigation management of soil humidity in smart agriculture.

A system and method for irrigation, and managing irrigation of, a property or landscape. The system may include an irrigation management server (“IMS”) with the information for an irrigation system. The system may access the irrigation system that has been input into the IMS as well as access a third party map that may display the irrigation system over the map. The system may provide for a user's GPS location to be accessed through and identify the location of the user with respect to the map and irrigation overlay. The system display may provide information regarding the landscape to a user. The system may allow for manipulation of the irrigation system while overlaid on the map by the creation and manipulation of zones within the irrigation system as well as the creation and manipulation of other commands, including watering times and watering duration.

The invention belongs to the technical field of equipment planning, and discloses an automatic planning method for intelligent agricultural Internet of Things equipment, which comprises the following steps: dividing a target region into grid units, collecting soil moisture parameters and nutrient parameters of each grid, and establishing a regional digital twinborn model; obtaining a target crop of each grid, obtaining growth information from the crop knowledge base, and executing time sequence simulation to generate a water and fertilizer demand table of each grid; executing a water-fertilizer balance strategy based on the water-fertilizer demand table, and generating a water-fertilizer supply plan of each grid; analyzing spatial distribution characteristics of supply plans, and clustering adjacent grids with similar plans into irrigation management partitions; and generating a pipe network topological structure and an equipment planning scheme based on the partition layout. According to the method, a traditional uniformly distributed area is scientifically divided into management subareas with consistent internal requirements, so that unified management of the same subarea is realized, and the problem of supply and demand mismatching caused by neglect of spatial heterogeneity in a traditional method is solved.

The invention discloses an intelligent agricultural irrigation management method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. The method comprises the following steps: acquiring regional environment monitoring data, regional condition information and historical irrigation demand corresponding to an irrigation management region; determining an initial irrigation time point and an initial irrigation demand quantity corresponding to the irrigation management area based on the area condition information; obtaining an environment influence coefficient corresponding to the irrigation management area based on the area environment monitoring data; based on the environmental influence coefficient, correcting the initial irrigation time to obtain a corrected irrigation time point corresponding to the irrigation management area; based on the environmental influence coefficient, the historical irrigation demand quantity and the initial irrigation demand quantity, obtaining a corrected irrigation demand quantity corresponding to the irrigation management area; and agricultural irrigation equipment in the irrigation management area is controlled to carry out irrigation operation according to the corrected irrigation demand quantity at the corrected irrigation time point. According to the technical scheme, the irrigation effect and the irrigation accuracy can be effectively improved.

The invention relates to construction and application of a wheat biomass estimation model fusing phenological information and hyperspectral data, and aims to solve the technical problem that wheat biomass estimation prediction precision and interpretability are difficult to balance. According to the method, Boruta-SHAP feature selection is combined with an attention-based neural network to estimate the wheat biomass, and the wheat biomass comprises phenological indexes (GDD, GD and ZS) with physiological significance and spectral features which are optimally selected by using a hierarchical attention mechanism. The model can effectively capture the crop development time and spectral dynamics. SHAP analysis shows that the contribution of phenological information to the model performance is 31.5%, key spectral characteristics comprise NIR (22.9%) and SWIT1 (12.3%), and the estimation precision is further improved. The robust and explainable framework model has a potential wide application prospect in the aspects of wheat yield prediction, fertilization optimization and irrigation management by providing an extensible biomass estimation method.

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.

Systems, user interfaces, and methods related to irrigation control systems are provided herein. In some embodiments, an irrigation control system includes an irrigation management application which, when executed by an electronic device, causes a user interface to be displayed to a user. The user interface includes features that permit a user to do one or more of configure, monitor, program, control, adjust various components and / or operations of the irrigation management system.

The invention provides a fruit tree growth state monitoring method and device based on a flexible sensor, is applied to a fruit tree growth state detection system, and relates to the technical field of agriculture. The system comprises a stem diameter detection device and a terminal device. The stem diameter detection device includes: at least one flexible sensor; the flexible sensor wraps a target position of the fruit tree; the target position comprises a trunk of the fruit tree, side branches of the fruit tree and fruits of the fruit tree; the method comprises the steps that the stem diameter detection equipment acquires diameter data of a target position; the terminal equipment judges the growth state of the fruit tree based on the diameter data; wherein the growth state at least comprises one of the following states: a normal state, a drought state, a fruit cracking early warning state and a nutrient deficiency early warning state; the diameters of trunks and fruits of the fruit trees are obtained through the flexible sensors, the growth state of the fruit trees is judged based on diameter changes, the equipment and labor cost is reduced, irrigation management is optimized, the crop yield and quality are improved, environmental stress is monitored, and agricultural digital transformation is promoted.

The invention relates to a hilly area intelligent irrigation decision-making system based on ET precise irrigation management, and the system comprises an ET-based intelligent irrigation forecasting system: simplifying a calculation model through ET0, and constructing a hilly area simplified ET0 prediction model; the farmland soil moisture content monitoring and early warning system is used for monitoring soil moisture in real time and realizing high-speed transmission and accurate analysis of soil moisture content and weather forecast data according to soil drought early warning indexes; a crop growth environment information real-time online acquisition and analysis system and a long-distance low-energy-consumption lossless information transmission technology are integrated; farmland irrigation control is managed in real time by using ultra-long-distance wireless transmission based on a spread spectrum technology and a pulse type electromagnetic valve low-energy-consumption wireless communication remote control technology based on a narrow-band internet of things. Quantitative and efficient water distribution of crop irrigation is achieved, and a precise irrigation regulation and control technology integration and efficient management mode meeting multi-crop, multi-climate and multi-growth-period requirements is met.

The invention relates to the technical field of monitoring and adjusting, and particularly discloses a water-saving irrigation system, method and equipment based on concealed pipe drainage. The system comprises a farmland irrigation water quality reference evaluation value comparison module, a farmland irrigation water quality compensation evaluation value comparison module, a farmland irrigation water quality real-time monitoring module, a farmland irrigation water quality compensation scheme comparison module and a farmland water-saving adjustment module, the problems that the prior art bureau is limited to directly monitor the water quality discharged from each water seepage underground pipe water outlet to a water well, and the influence of the use state of each water seepage underground pipe on water quality monitoring and water saving is neglected are solved; the influence of the amount of water, which is monitored by a flow meter and flows out of a water outlet of each water seepage concealed pipe to a water well, on the adjustment of unqualified farmland irrigation water quality and water saving is not considered, so that better irrigation management service can be provided for the farmland, and the unqualified water quality can be adjusted in a targeted manner; and the water-saving regulation and management level of farmland irrigation water quality is improved.

The invention provides a greenhouse crop habitat optimization method, system and device and a storage medium, and belongs to the technical field of intelligent agriculture, and the method comprises the steps: continuously obtaining greenhouse internal environment multi-element data, greenhouse external meteorological data, crop root zone current soil moisture data and device state data; predicting greenhouse internal environment multi-element data and soil moisture content of different soil layers in a future time period based on the data; predicting photosynthetic physiological habitat conditions of crops in the greenhouse in the future time period based on the multi-element data of the internal environment of the greenhouse in the future time period and soil moisture contents of different soil layers in the crop root zone; and taking the predicted habitat condition as an optimization target, performing optimization decision on current irrigation and environment multi-element regulation and control, generating a control scheme, further generating a regulation and control signal, and controlling greenhouse terminal equipment to operate according to the regulation and control signal. According to the invention, the accuracy, real-time performance, collaboration and intelligent level of greenhouse environment regulation and irrigation management are obviously improved, and the crop yield, quality and resource utilization efficiency are further improved.

The invention discloses an intelligent irrigation method based on deep learning, and the method comprises the steps: 1) data collection and arrangement: employing collected related meteorological data to calculate the water demand of crops; 2) establishing a data set, performing judgment by using the sorted meteorological data and the crop water demand and grey relational analysis, constructing a lightweight crop water demand model, constructing a four-classification data set based on irrigation decision, and dividing the data set into a training set and a test set; 3) by taking the LSTM model as a baseline, performing optimization to construct an LSTM-Attention model and construct a dynamic learning rate; 4) inputting the test set into the trained model for testing to obtain related performance indexes of the model; and 5) constructing a system platform, and developing an intelligent irrigation management platform at a web port. According to the invention, a crop water demand model can be trained by using a network data set, so that the irrigation amount of a target farmland can be formulated more accurately and scientifically, and control and assistance of irrigation management of the farmland can be realized in the system platform.

The present application relates to the field of garden irrigation, and specifically discloses a garden intelligent monitoring irrigation system for environmental remediation, comprising: an environmental parameter and plant health index monitoring module for real-time monitoring of environmental parameters of each plant in the garden, including soil humidity, light intensity and temperature, and plant health indexes of each plant, including growth conditions, leaf color, and calculating a comprehensive index F of each plant at any time t i (t); the present application realizes efficient and accurate irrigation management through the multiple modules integrated by the system, significantly improves the pertinence and effectiveness of irrigation, the real-time monitoring function of the environmental parameter and plant health index monitoring module enables the system to accurately capture the growth needs of plants and environmental changes, providing a scientific basis for the development of irrigation strategies, the personalized irrigation strategy not only meets the growth needs of plants, but also avoids unnecessary irrigation waste, improving water resource utilization efficiency.

The invention discloses a rice field water recession zero direct drainage circulating irrigation management system, which relates to the technical field of irrigation and water conservancy engineering, and specifically comprises a multi-source information acquisition module, an information monitoring module, a distributed calculation module, an equipment control module, an intelligent early warning module, a master control room and a data processing module, the multi-source information acquisition module, the information monitoring module, the distributed calculation module, the intelligent early warning module and the data processing module are all controlled by a master control room. The whole management system adopts a pollutant prevention and control measure combined with a rice field recession'zero direct discharge 'engineering construction technology and the like, so that the rice field recession is not directly converged into a surrounding receiving water body. Meanwhile, unmanned operation of the farmland irrigation system is achieved through multiple links such as farmland irrigation, recession water filtration and purification and recycled water recycling by means of the Internet of Things technology in the farmland water supply and drainage process, manpower resources are saved, and waste of irrigation water is reduced.

The invention provides a comprehensive benefit evaluation method and system for saline-alkali soil improvement based on drainage basin water-salt balance, and belongs to the technical field of agricultural ecological management. The method comprises the following steps: collecting and preprocessing geographical partition data, soil hydrological data, crop salt tolerance data and irrigation management data; constructing a salt migration-yield coupling model, namely establishing a salt migration dynamic equation, substituting the preprocessed data into the salt migration dynamic equation, and calculating the downstream salt concentration; a yield response model is established, the downstream salinity concentration and the preprocessed data are substituted into the yield response model, and the upstream treatment area increased yield and the downstream reduced yield are calculated; and calculating the net benefit value of saline-alkali soil improvement according to the upstream treatment area yield increase and the downstream yield reduction. According to the method, the salinity increment, exceeding the salt tolerance threshold value, of the soil of the treatment area is considered, overall systematic evaluation is carried out on upstream and downstream yield changes, and therefore reliable guidance is provided for selection and optimization of irrigation measures.

The invention provides a cold region greenhouse irrigation method and system, and belongs to the technical field of environment control. The method comprises the steps that soil temperature data and humidity data of different depths in all partitions of a greenhouse are acquired in real time; carrying out preprocessing and spatial-temporal feature enhancement on the collected data, inputting the data into a soil temperature-humidity dynamic coupling monitoring model, and calculating to obtain a root retting risk index of the current soil environment; if the root retting risk index exceeds a dynamic risk threshold, generating an irrigation strategy for inhibiting the root retting risk; if the root retting risk index does not exceed a dynamic risk threshold, generating a conventional irrigation strategy based on a multi-objective optimization algorithm; according to the generated irrigation strategy, driving the irrigation execution mechanism of the corresponding subarea to perform independent action, and realizing subarea accurate irrigation; the problem of root retting caused by low temperature and high humidity in a cold region greenhouse environment is solved, and precision, intelligence and high efficiency of irrigation management are achieved.

The invention relates to the technical field of data processing, in particular to an efficient monitoring and intelligent irrigation management platform for radar rainfall data. Obtaining a first abnormal point location and a non-initial abnormal point location according to a data volume feature in an intensity data cluster of the echo intensity and a data difference feature of the intensity data cluster; obtaining a point location connected domain according to the intensity data cluster of the non-initial abnormal point location; obtaining a spatial noise feature value according to the position distribution feature between the point location connected domains and the data difference feature in the point location connected domains; obtaining a time noise characteristic value according to the historical echo intensity of the non-initial abnormal point location; obtaining a second abnormal point location according to the spatial noise characteristic value and the time noise characteristic value; correcting the corresponding echo intensity according to the position features of the first abnormal point location and the second abnormal point location; and the rainfall is determined according to the corrected echo intensity, and the irrigation rule is adjusted, so that the accuracy of rainfall judgment and irrigation regulation is 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 belongs to the technical field of agricultural intelligent control and irrigation management, particularly relates to a self-adaptive irrigation decision-making system oriented to crop water demand response, and aims to solve the problems that a traditional irrigation system is lagged in response, extensive in water distribution and difficult to match actual physiological water demand of crops. The system collects crop physiological and environmental parameters through a multi-source sensor, inverts a water stress index in real time in combination with an edge computing node, dynamically calls a basic water demand curve based on a crop growth stage, and generates an instantaneous water demand deviation. A double-level decision-making mechanism is adopted, wherein the first layer triggers emergency irrigation according to a dynamic warning threshold value; and the second layer realizes irrigation plan feed-forward adjustment through trend analysis. The system also integrates a closed-loop feedback verification and federated learning optimization mechanism, and continuously improves the decision precision. By fusing physiological feedback and self-adaptive modeling, the conversion from watering on day to water supply on demand is realized, the water utilization efficiency is remarkably improved, and the stress risk is reduced.

The invention relates to the technical field of management systems, in particular to an intelligent spray irrigation management system under forestry plant maintenance, which comprises a soil layering model construction unit for collecting the types of planted saplings and topographic data of a planting area, extracting soil layering structure data of the planting area and a three-dimensional topographic model, and constructing a three-dimensional model; performing fusion processing to obtain a soil layering model; determining the distribution positions of planted saplings on the soil layering model, marking the position corresponding to each sapling on the soil layering model as a planting point position, constructing a sapling root growth model based on the type of the sapling and the soil layering structure of the position, and forming a sapling soil layering model together with the soil layering model; and the irrigation network construction unit is used for setting an irrigation point position for each sapling root growth model based on the sapling soil layering model, connecting a plurality of drip irrigation point positions to form a pipeline trend straight line, erecting branch pipes along the pipeline trend straight line, erecting a main pipe and connecting a plurality of branch pipes to form an irrigation network.

The invention relates to the technical field of agriculture, and discloses a fresh corn efficient harvesting and storage method based on ecological planting, which comprises the following steps: ecological planting management, harvesting opportunity determination, efficient harvesting operation and storage condition control. According to the ecological planting management, scientific fertilization and irrigation management are implemented, nutrient and water supply is dynamically adjusted according to the growth stage of the fresh corn, a balanced nutrition environment is provided for growth of the fresh corn, and therefore balanced development of quality indexes of the fresh corn is promoted; the harvesting opportunity is determined based on quality index monitoring and a harvesting prediction model, accurate judgment of the mature state of the fresh corn is achieved, it is guaranteed that harvesting is conducted at the optimal harvesting time, a foundation is laid for obtaining high-quality fresh corn, efficient harvesting operation reduces mechanical damage to the fresh corn by optimizing harvesting parameters and the harvesting environment, and the yield of the fresh corn is improved. And immediately transferring to a shading environment, and slowing down the physiological deterioration process after harvesting.

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.