Crop irrigation decision-making method and system based on water demand characteristics of crops during growth period

Abstract

The present invention belongs to the field of crop growth monitoring, and in particular relates to a crop irrigation decision-making method and system based on the water demand characteristics of the crop growth period. The method comprises the following steps: first, obtaining remote sensing satellite images of the soil area of ​​the crop to be determined, as well as the segmented growth period and transition time period attributes of each crop; second, using the remote sensing satellite images and the temperature vegetation drought index to monitor the soil water content of the soil area of ​​the crop to be determined; and finally, combining the soil water content with the relevant attributes of the crop growth period, using a preset variable precision irrigation decision model to generate a variable irrigation prescription map, thereby implementing irrigation decisions for the soil area of ​​the crop to be determined. By comprehensively utilizing remote sensing data and crop growth period information, the present invention can rationally plan irrigation plans based on the water demand characteristics of different crops at different growth stages, effectively improving water resource utilization efficiency and ensuring crop growth.

Description

Technical Field

[0001] The present invention belongs to the field of crop growth monitoring, and in particular relates to a crop irrigation decision-making method and system based on the water demand characteristics of crops during their growth period. Background Art

[0002] In modern agricultural production, irrigation is one of the key links to ensure high and stable crop yields. Traditional irrigation methods mostly rely on farmers' experience and intuition, and lack a precise grasp of the actual water requirements of crops, which often leads to water waste or insufficient irrigation. With the intensification of global climate change and the increasing shortage of water resources, how to irrigate scientifically and rationally has become an important issue that needs to be addressed urgently. Although existing technologies have introduced some automated irrigation systems based on soil moisture, these systems usually fail to fully consider the specific water requirements of crops at different growth stages, making it difficult to achieve refined management. In addition, traditional irrigation decision-making models mostly use static parameter settings, which cannot adapt to complex changes in farmland environments and have limited prediction accuracy.

[0003] For example, a Chinese patent application with publication number CN116579872A discloses a precise irrigation decision-making method based on crop growth models and weather forecasts, including the steps of constructing a crop water requirement prediction model based on the crop growth model and constructing an irrigation decision-making model based on the crop growth model. The method first corrects and verifies the crop growth model based on historical data related to crop growth, and then simulates different irrigation strategies for different crop growth models to determine the water requirements of water-saving and efficient crops in each growth period. Then, the real-time irrigation decision results of the crops in the study area are determined based on the serial number of the day after crop sowing and the real-time weather forecast information.

[0004] For example, a Chinese patent with authorization announcement number CN110580657B discloses a method for predicting agricultural irrigation water demand, which includes selecting a base period and selecting crops with a sowing area greater than a set ratio as typical crops; calculating the irrigation water quota of typical crops during the growth period based on meteorological information; obtaining the ten-day rainfall at each meteorological station in the water-saving area, and calculating the effective rainfall of typical crops based on the ten-day rainfall; calculating the irrigation water demand per unit area of ​​typical crops; judging whether there is at least one typical crop whose irrigation water demand per unit area is greater than the average value of the actual irrigation water use per unit area in consecutive measured years; if so, revising the irrigation water demand per unit area of ​​all typical crops; otherwise, using the irrigation water demand per unit area of ​​each typical crop and the planting area of ​​the next year to calculate the total water demand for the next year.

[0005] The above-mentioned existing technologies have the following problems: when making irrigation decisions based on the water demand characteristics of crops during their growth period, the existing technologies are usually based on predictions made during the entire growth period of the crops. However, crop growth is a dynamic process, and the corresponding water demand for each day is different. Therefore, it is impossible to accurately grasp the irrigation time intervals corresponding to the crops in different growth periods, resulting in problems such as insufficient or excessive irrigation in the corresponding growth periods. Therefore, the present invention provides a crop irrigation decision-making method and system based on the water demand characteristics of crops during their growth period. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention proposes a crop irrigation decision-making method and system based on the water demand characteristics of the crop growth period, including: constructing a standard water demand space based on the crop segmented growth period attributes and the standard growth water demand, and constructing a water demand dynamic balance prediction equation in combination with the corresponding weather, evaporation, deep infiltration, and crop absorption rate, and then constructing a water demand two-way prediction model to obtain crop water demand, irrigation time interval and water demand deviation factor, and accordingly constructing an irrigation trigger interval, irrigation time interval and irrigation intensification decision model to generate irrigation strategy, and monitor and evaluate crop growth and water storage status, and feed the evaluation results back to the prediction model and decision model to adjust the predicted water storage and irrigation strategy in real time; the present invention can accurately calculate water demand and generate irrigation decisions based on the water demand characteristics of the crop growth period, effectively improve irrigation efficiency and water resource utilization, and ensure good growth of crops.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] Crop irrigation decision-making methods based on crop water demand characteristics during the growth period include:

[0009] Obtain remote sensing satellite images of the soil area of ​​the crop to be decided and the segmented growth period and transition period attributes of each crop;

[0010] Monitoring the soil water content of the crop soil area to be decided based on the remote sensing satellite imagery and the temperature vegetation drought index;

[0011] Based on the soil water content and the segmented growth period and transition time attributes of each crop, combined with a preset variable precision irrigation decision model, a variable irrigation prescription map is generated to decide on crop irrigation in the soil area of ​​the crop to be decided.

[0012] Specifically, monitoring the soil water content of the crop soil area to be decided includes:

[0013] Based on the remote sensing satellite images, soil water content, temperature vegetation drought index, surface temperature and normalized difference vegetation index under drought stress are inverted;

[0014] The soil water content of the crop soil area to be decided is predicted based on the measured soil water content, soil water content under drought stress, temperature vegetation drought index, surface temperature and normalized difference vegetation index.

[0015] Specifically, the variable precision irrigation decision model is obtained by integrating the water demand bidirectional prediction model with the irrigation intensification decision model; the step of generating the variable irrigation prescription map by combining the preset variable precision irrigation decision model includes:

[0016] Based on the segmented growth period and transition period attributes of each crop and the soil moisture content, a two-way water demand prediction model is constructed and pre-trained to obtain the water demand and irrigation time interval of the corresponding crop type;

[0017] The water demand deviation factor is obtained according to the water tolerance of each growth stage of the corresponding type of crop;

[0018] According to the water requirement of the corresponding type of crops, irrigation time interval and water demand deviation factor, the configured irrigation intensification decision model is used to generate a variable irrigation prescription map for the soil area of ​​the crop to be decided.

[0019] Specifically, the attributes of each crop's segmented growth period and transition period include: corresponding meteorological data, evaporation status, deep soil infiltration status, crop absorption rate, standard water requirement space of the kth growth period, and meteorological data, evaporation status, soil moisture conditions, and crop absorption rate data of the pre-transition and post-transition periods of the kth growth period;

[0020] The steps of constructing and training the water demand bidirectional prediction model include:

[0021] S301, setting a pre-transition section and a post-transition section corresponding to each growth stage of the crop to be decided, and constructing a transition change function based on the historical water requirements corresponding to the pre-transition section and the post-transition section;

[0022] The front transition section is a time period corresponding to the growth stage and shifted forward by a first preset number of days, and the rear transition section is a time period corresponding to the growth stage and shifted backward by a first preset number of days;

[0023] S302: constructing K water demand prediction sub-models integrated into the water demand bidirectional prediction model based on the BP neural network and particle swarm algorithm, embedding the transition change function into the water demand prediction sub-models corresponding to adjacent growth stages, and embedding the crop water demand dynamic balance prediction equation of the corresponding growth stage into the water demand prediction sub-models of the corresponding growth stage;

[0024] S303: Inputting the corresponding meteorological data, evaporation status, soil moisture, crop absorption rate, standard water requirement space of the kth growth period, meteorological data, evaporation status, soil moisture, crop absorption rate data of the pre-transition section and post-transition section of the kth growth period, and soil water content of the soil area of ​​the crop to be decided into the water demand prediction sub-model corresponding to the kth growth period for training, and obtaining transition section prediction training errors and non-transition section training errors corresponding to the K water demand prediction sub-models;

[0025] S304. Set a training error threshold, construct a comprehensive training loss function based on the obtained K transition segment prediction training errors and K-1 non-transition segment training errors, and obtain a trained water demand prediction sub-model when the comprehensive training loss function is less than the training error threshold.

[0026] Specifically, the steps of obtaining the transition segment prediction training error and the non-transition segment training error include:

[0027] S3031. When the predicted water demand time point of the kth growth period is in the non-transition period of the current growth period, the water demand at the corresponding time point is predicted using the water demand prediction sub-model corresponding to the kth growth period, and the predicted water demand is compared with the historical actual water demand to obtain the training error of the kth non-transition period.

[0028] S3032. When the water demand prediction time point at the current moment of the k-th growth period is in the pre-transition section of the current growth period, a pre-prediction is performed through the water demand prediction sub-model corresponding to the k-th growth period to obtain the water demand prediction at time t corresponding to the pre-transition section of the k-th growth period. At the same time, the water demand prediction sub-model corresponding to the k-1-th growth period is used to predict the water demand prediction at the same moment.

[0029] Specifically, the steps of obtaining the transition segment prediction training error and the non-transition segment training error further include:

[0030] S3033. Using the average of the water demand forecast obtained by the water demand forecast sub-model corresponding to the kth growth period and the water demand forecast obtained by the water demand forecast sub-model corresponding to the k-1th growth period at the same time point as the water demand forecast corresponding to time t in the pre-transition period, and using the water demand forecast corresponding to time t and the water demand at the corresponding time point, obtain the transition period prediction training error at the corresponding time point of the pre-transition period;

[0031] S3034. When the water demand prediction time point of the current moment of the k-th growth period is in the post-transition section of the current growth period, repeat the S3032 process to obtain the water demand prediction amount of the water demand prediction sub-model corresponding to the k-th growth period at the post-transition section corresponding to the time t and the water demand prediction amount of the water demand prediction sub-model corresponding to the k+1-th growth period at the same time point, and repeat S3033 to obtain the transition section prediction training error at the time point corresponding to the post-transition section.

[0032] Specifically, the steps for obtaining the water demand deviation factor include:

[0033] S311. Configure water tolerance experiments for crops at different growth stages, and set a two-way water requirement prediction model to predict the water requirements of crops at corresponding growth stages as ideal water requirements;

[0034] S312: Using the ideal water requirement as a starting point, increase or decrease the water requirement of the crop during the corresponding growth period, monitor the crop growth status corresponding to the water requirement at each moment, and automatically evaluate the crop growth status score under the corresponding water requirement using a configured evaluation algorithm;

[0035] S313, using the water requirement and the growth status score corresponding to the water requirement, obtaining a water requirement-growth score change curve corresponding to different growth stages of the crop throughout its entire growth period;

[0036] S314. Taking the ideal water requirement corresponding to the crop growth status score in each growth stage as a starting point, when the water requirement is increased or decreased and the growth status score corresponding to the corresponding water requirement is less than the crop growth status score corresponding to the ideal water requirement for m consecutive times, obtain the minimum and maximum water requirements corresponding to the beginning of the decrease in the crop growth status score in the corresponding growth stage;

[0037] S315. Obtain a lower limit deviation factor of the water requirement for the corresponding growth period based on the minimum water requirement and the ideal water requirement. Meanwhile, obtain an upper limit deviation factor of the water requirement for the corresponding growth period based on the maximum water requirement and the ideal water requirement.

[0038] Specifically, the steps for constructing the irrigation intensification decision model include:

[0039] S401, constructing a water requirement interval for each growth stage of the crop based on the ideal water requirement, the water requirement lower limit deviation factor, and the water requirement upper limit deviation factor;

[0040] S402: Using the water requirement interval of each growth stage as a variable irrigation prescription map, and setting irrigation strategy execution action trigger information according to the variable irrigation prescription map.

[0041] Specifically, the steps of setting the irrigation strategy execution action trigger information include:

[0042] S4021: When the water holding capacity of the crop growing area at the current moment is lower than the lower limit of the water requirement range in the variable irrigation prescription map, irrigation is directly carried out until the ideal water requirement of the corresponding growth stage is reached;

[0043] S4022: When the current water holding capacity of the corresponding crop growing area is lower than the ideal water requirement for the corresponding growth stage, pre-adjust the parameters of the irrigation intensification decision model based on the ideal water requirement for the corresponding growth stage. When the water holding capacity of the corresponding crop growing area reaches the lower limit of the water requirement range in the variable irrigation prescription map, control the irrigation device to irrigate using the pre-adjusted parameters of the irrigation intensification decision model until the ideal water requirement for the corresponding growth stage is reached.

[0044] S4023. When the water holding capacity of the crop growth area at the current moment is higher than the ideal water requirement of the corresponding growth stage, no irrigation is performed. When the water holding capacity is lower than the ideal water requirement of the corresponding growth stage, the process of S4022 is repeated.

[0045] The crop irrigation decision-making system based on the water demand characteristics of crops during their growth period includes: a prediction module and an irrigation decision-making module.

[0046] A prediction module, configured to monitor the soil water content of the crop soil area to be determined based on the remote sensing satellite imagery and the temperature vegetation drought index;

[0047] The irrigation decision module is used to generate a variable irrigation prescription map based on the soil water content and the segmented growth period and transition time period attributes of each crop, combined with a preset variable precision irrigation decision model, to decide on crop irrigation in the soil area of ​​the crop to be decided.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] The present invention addresses the deficiencies of existing technologies and accurately predicts soil moisture content by acquiring remote sensing satellite images and crop segmented growth stages and other attributes. Utilizing a uniquely constructed and trained two-way water demand prediction model, combined with weather, evaporation, soil moisture, absorption rate and other conditions of each crop growth stage, the present invention can accurately determine the water demand and irrigation time intervals for different segmented growth stages, thus avoiding the problem of insufficient or excessive irrigation. Simultaneously, a water demand deviation factor is obtained based on the degree of water tolerance, and a variable irrigation prescription map is generated through an irrigation intensification decision model, thus achieving dynamic and accurate irrigation decision-making. This not only improves water resource utilization efficiency and reduces waste, but also provides appropriate moisture for crops at different growth stages, effectively ensuring their healthy growth. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a flow chart of a crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to Example 1 of the present invention;

[0051] Figure 2 This is a diagram showing the non-transition section and transition section of Example 1 of the present invention;

[0052] Figure 3 A recommended depth prescription map for predicted water demand corresponding to the rice planting plot in Example 1 of the present invention;

[0053] Figure 4 This is a module diagram of a crop irrigation decision-making system based on the water requirement characteristics of crops during their growth period according to Example 2 of the present invention. DETAILED DESCRIPTION

[0054] Example 1

[0055] See also Figure 1 The present invention provides an embodiment of a crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period, comprising the following steps:

[0056] S1. Obtain remote sensing satellite images of the crop soil area to be determined and the segmented growth period and transition period attributes of each crop;

[0057] S2. Monitoring the soil water content of the crop soil area to be determined based on the remote sensing satellite imagery and the temperature vegetation drought index;

[0058] Furthermore, in this embodiment, the soil water content of the crop soil area to be determined is predicted, including:

[0059] Based on the remote sensing satellite images, soil water content, temperature vegetation drought index, surface temperature and normalized difference vegetation index under drought stress are inverted;

[0060] Furthermore, in this embodiment, the specific steps of inverting and obtaining the soil water content under drought stress include:

[0061] Based on remote sensing satellite images, a statistical model is simulated to invert the data. Using the measured soil water and the corresponding VTCI values, a linear equation is fitted between VTCI and soil water. Based on this linear equation, the soil water content of the crop soil area to be determined is obtained.

[0062] Furthermore, the step of obtaining the temperature vegetation drought index in this embodiment includes:

[0063] Extract near-infrared and red light bands based on remote sensing images of the area to be irrigated;

[0064] According to the reflectance values ​​of the near-infrared band and the red light band, the NDVI value of each pixel in the area to be irrigated is obtained;

[0065] Where NDVI stands for Normalized Difference Vegetation Index;

[0066] Based on the remote sensing images of the area to be irrigated, two thermal infrared bands are extracted and the true surface temperature (LST) is obtained through the split window algorithm.

[0067] According to the NDVI value of each pixel and the corresponding true surface temperature value, a two-dimensional LST-NDVI feature space is constructed with NDVI as the horizontal coordinate and LST as the vertical coordinate;

[0068] Analyze the scattered points in the LST-NDVI feature space to determine the dry edge and wet edge;

[0069] According to the LST-NDVI feature space and the dry and wet edges, the drought index of each pixel is obtained, specifically:

[0070] ,

[0071] in, The normalized vegetation index corresponding to the i-th pixel is surface temperature; is the ith normalized difference vegetation index in the region The maximum surface temperature; is the ith normalized difference vegetation index in the region The minimum surface temperature.

[0072]

[0073] Among them, the coefficients of the equation and Represents the corresponding equation coefficient, which can be obtained by fitting the equation of the dry-wet edge scatter plot of the "LST-NDVI feature space".

[0074] The soil water content of the crop soil area to be decided is inverted based on the measured soil water content, soil water content under drought stress, temperature vegetation drought index, surface temperature and normalized difference vegetation index.

[0075] S3. Based on the soil water content and the segmented growth period and transition time attributes of each crop, combined with a preset variable precision irrigation decision model, a variable irrigation prescription map is generated to decide on crop irrigation in the soil area of ​​the crop to be decided.

[0076] Furthermore, the variable precision irrigation decision model in this embodiment is obtained by integrating the water demand bidirectional prediction model with the irrigation intensification decision model; the step of generating a variable irrigation prescription map in combination with the preset variable precision irrigation decision model includes:

[0077] Based on the segmented growth period and transition period attributes of each crop and the soil moisture content, a two-way water demand prediction model is constructed and pre-trained to obtain the water demand and irrigation time interval of the corresponding crop type;

[0078] The water requirement deviation factor is obtained according to the water tolerance of each growth stage of the corresponding type of crop;

[0079] According to the water requirement of the corresponding type of crops, irrigation time interval and water demand deviation factor, the configured irrigation intensification decision model is used to generate a variable irrigation prescription map for the soil area of ​​the crop to be decided.

[0080] Furthermore, in this embodiment, the attributes of each crop's segmented growth period and transition period include: corresponding meteorological data, evaporation conditions, soil moisture conditions, crop absorption rate, standard water requirement space for the kth growth period, and meteorological data, evaporation conditions, soil moisture conditions, and crop absorption rate data for the pre-transition and post-transition sections of the kth growth period; the soil moisture conditions include deep soil infiltration conditions and water infiltration rate for the crop growth area corresponding to the growth stage;

[0081] Furthermore, the steps of constructing and training the water demand bidirectional prediction model in this embodiment include:

[0082] S301, setting a pre-transition section and a post-transition section corresponding to each growth stage of the crop to be decided, and constructing a transition change function based on the historical water requirements corresponding to the pre-transition section and the post-transition section;

[0083] Furthermore, the front transition section in this embodiment is a time period corresponding to the growth stage moving forward by the first preset number of days, and the rear transition section is a time period corresponding to the growth stage moving backward by the first preset number of days; further, the first preset number of days in this embodiment is set by those skilled in the art according to the corresponding crop type and crop growth properties.

[0084] S302: constructing K water demand prediction sub-models integrated into the water demand bidirectional prediction model based on the BP neural network and particle swarm algorithm, embedding the transition change function into the water demand prediction sub-models corresponding to adjacent growth stages, and embedding the crop water demand dynamic balance prediction equation of the corresponding growth stage into the water demand prediction sub-models of the corresponding growth stage;

[0085] Furthermore, the construction of the crop water demand dynamic balance prediction equation in this embodiment includes:

[0086] ,

[0087] in, It represents the change in water demand of the i-th crop at the k-th growth stage at time t, It represents the cumulative average rainfall in the crop growth area at the kth growth stage at time t. represents the irrigation water requirement of the i-th crop at the k-th growth stage at time t in the corresponding growth area, represents the combined evapotranspiration of the i-th crop at the k-th growth stage at time t in the corresponding growing area; It represents the surface runoff of the crop at the kth growth stage at time t in the corresponding growth area; It represents the effective permeability factor of the i-th crop at the k-th growth stage at the corresponding growth area at time t; It represents the reference evapotranspiration of the crop at the kth growth stage at time t in the corresponding growth area; It represents the crop coefficient of the i-th crop in the k-th growth stage of the corresponding growth area after being corrected by the radiation intensity, crop leaf surface area and radiation angle at the corresponding time point; represents the water absorption rate of the i-th crop in the k-th growth stage in the corresponding growth area; It represents the amount of water evaporation corresponding to the crop growing area at the kth growth stage; represents the average effective root depth of the i-th crop at the k-th growth stage in the growth area, that is, the crop root depth used for water absorption; represents the infiltration of the i-th crop at the k-th growth stage in the corresponding growth area at time t; represents the average infiltration of the i-th crop in the corresponding growing area at the k-th growth stage; It represents the water infiltration rate corresponding to the crop growth area at the kth growth stage; represents the standard growth water requirement of the i-th crop in the crop growing area at the k-th growth stage; further, represents the average water penetration depth of the i-th crop at the k-th growth stage in the crop growing area;

[0088] In this embodiment, multiple factors are of great significance to the calculation of crop water requirements and irrigation decisions. The effective infiltration volume, after deducting runoff losses, affects the calculation of water demand changes. When the soil texture is good, its value is large, which can reduce irrigation needs and maintain soil balance. The effective infiltration factor is multiplied by the infiltration volume and corrected according to the crop and growth stage. This factor is large in the late growth stage of crops with well-developed root systems, which can more accurately reflect the soil water utilization of different crops at different stages. The crop coefficient varies according to the type of crop and growth stage. It is large during the vigorous growth period, such as the wheat heading stage, affecting the combined evaporation and water demand changes, which helps to accurately adapt to evaporation needs. The water absorption rate affects the water demand change. It is large during the vigorous growth period and requires more irrigation, while it is small when the growth is slow or under stress. Based on this, the dynamics of water demand can be grasped to achieve precise irrigation. The average effective root depth affects the calculation of effective infiltration volume. In the early stage, only shallow water can be used. As the root system deepens during growth, deep water can be used to optimize the irrigation depth and amount decisions. Water infiltration rate and average effective root depth jointly influence infiltration and effective infiltration. Faster infiltration rates facilitate water replenishment and storage, reducing surface runoff after rain or irrigation, improving irrigation effectiveness and promoting crop growth. These combined factors help accurately predict crop water requirements, optimize irrigation strategies, and improve water resource utilization efficiency.

[0089] S303: Inputting the corresponding meteorological data, evaporation status, soil moisture, crop absorption rate, standard water requirement space of the kth growth period, meteorological data, evaporation status, soil moisture, crop absorption rate data of the pre-transition section and post-transition section of the kth growth period, and soil water content of the soil area of ​​the crop to be decided into the water demand prediction sub-model corresponding to the kth growth period for training, and obtaining transition section prediction training errors and non-transition section training errors corresponding to the K water demand prediction sub-models;

[0090] Furthermore, in this embodiment, the steps of constructing the standard water requirement space during the crop growth period include:

[0091] S101. Divide the growth period of each crop into segments based on its biological characteristics and growth patterns. For example, wheat can be divided into the sowing-emergence stage, seedling stage, jointing stage, booting stage, heading stage, grain filling stage, and maturity stage.

[0092] S102. Refer to agricultural science literature, crop cultivation manuals, and long-term field observation records to determine the start and end times and characteristic features of each growth period. These features may include changes in plant morphology and physiological indicators;

[0093] S103. Acquire data through field trials, i.e., measure the actual water requirement of each crop during each growth period in different test plots while keeping other environmental factors relatively constant. Furthermore, this embodiment uses a water balance method, i.e., measures irrigation water volume, precipitation, and soil moisture changes to calculate the actual water consumption of the crop during the growth period, which serves as a reference for the standard growth water requirement.

[0094] S104. Collect historical agricultural data, including years of agricultural irrigation records, weather station data, and crop yield data. Use statistical analysis methods to extract information on the average water requirements of each crop at each growth stage from this data. Also, refer to standard crop water requirements data published by agricultural research institutions.

[0095] S105. Use a three-dimensional spatial model (growing period, water requirement, environmental factors) or a multidimensional matrix model to represent the standard water requirement space for each crop growth period. For example, using the growing period as one dimension and the standard growth water requirement as another dimension, a two-dimensional matrix is ​​constructed, with rows representing different growing periods and columns representing different water requirement ranges or specific values. Auxiliary variables are introduced, such as soil type and variety differences. Because different soils have different water retention capacities and crop varieties have different water requirements and utilization efficiencies, these variables can be used as adjustment parameters of the model to make the construction of the standard water requirement space more accurate.

[0096] S106. Fill the collected crop segmented growth stage attribute data and standard growth water requirement data into the established model framework. For each growth stage, fill the corresponding standard growth water requirement value or range into the corresponding position; for example, in a two-dimensional matrix, fill the standard growth water requirement range of wheat seedling stage into the corresponding row;

[0097] S107. Calibrate the populated model by comparing data from different sources (field test data, historical data, and standard data). If large errors are found between the data, analyze the causes of the errors and perform error optimization calibration.

[0098] Furthermore, the steps of obtaining the transition segment prediction training error and the non-transition segment training error in this embodiment include:

[0099] S3031. When the predicted water demand time point of the kth growth period is in the non-transition period of the current growth period, the water demand at the corresponding time point is predicted using the water demand prediction sub-model corresponding to the kth growth period, and the predicted water demand is compared with the historical actual water demand to obtain the training error of the kth non-transition period.

[0100] Further, see Figure 2, wherein the portion numbered 1 is the pre-transition segment corresponding to the k-th growth stage, the portion numbered 2 is the post-transition segment corresponding to the k-1-th growth stage, in this embodiment, the transition segments numbered 1 and 2 constitute the complete adjacent growth stage transition segments between the k-1-th growth stage and the k-th growth stage, the portion numbered 3 is the non-transition segment portion corresponding to the k-th growth stage, and the other black portions in the figure are the same as described above;

[0101] S3032. When the water demand forecast time point at the current moment of the k-th growth period is in the pre-transition period of the current growth period, a pre-forecast is performed using the water demand forecast sub-model corresponding to the k-th growth period to obtain the water demand forecast at time t corresponding to the pre-transition period of the k-th growth period. Simultaneously, the water demand forecast sub-model corresponding to the k-1-th growth period is used to predict the water demand forecast at the same moment.

[0102] S3033. Using the average of the water demand forecast obtained by the water demand forecast sub-model corresponding to the kth growth period and the water demand forecast obtained by the water demand forecast sub-model corresponding to the k-1th growth period at the same time point as the water demand forecast corresponding to time t in the pre-transition period, and using the water demand forecast corresponding to time t and the water demand at the corresponding time point, obtain the transition period prediction training error at the corresponding time point of the pre-transition period;

[0103] Furthermore, in this embodiment, the specific process of using the average of the water demand forecast obtained by the water demand forecast sub-model corresponding to the k-th growth period and the water demand forecast obtained by the water demand forecast sub-model corresponding to the k-1-th growth period at the same time point as the water demand forecast corresponding to time t in the pre-transition section is as follows: assuming that at time t in the pre-transition section 1, water demand is predicted using the water demand forecast sub-model corresponding to the k-th growth period and the water demand forecast sub-model corresponding to the k-1-th growth period respectively, and then the average of the water demand forecast obtained for the k-th growth period and the water demand forecast obtained for the k-1-th growth period is used as the water demand forecast at time t.

[0104] S3034. When the water demand prediction time point of the kth growth period at the current moment is in the post-transition section of the current growth period, repeat the process of S3032 to obtain the water demand prediction amount of the water demand prediction sub-model corresponding to the kth growth period at the post-transition section corresponding to time t and the water demand prediction amount of the water demand prediction sub-model corresponding to the k+1th growth period at the same time point, and repeat S3033 to obtain the transition section prediction training error at the time point corresponding to the post-transition section;

[0105] Furthermore, in this embodiment, when the current water demand prediction time point is in the transition period of the current growth period, the water demand at each time point of the corresponding transition period is obtained by taking the average of the backward sliding prediction of the previous growth period and the forward sliding prediction of the next growth period. This can accurately integrate the crop attribute characteristics of the two adjacent periods and reduce the impact of the transition of the crop growth stage corresponding to a single growth period on the predicted water demand.

[0106] S304. Set a training error threshold, construct a comprehensive training loss function based on the obtained K transition segment prediction training errors and K-1 non-transition segment training errors, and obtain a trained water demand prediction sub-model when the comprehensive training loss function is less than the training error threshold.

[0107] This process first establishes pre- and post-transition segments and constructs a transition function to better reflect the gradual change in crop water requirements during growth phase transitions, consistent with crop growth patterns. Using water requirement prediction sub-models for adjacent growth phases, the transition phases are jointly predicted, incorporating the characteristics of crops at different growth phases. This reduces the impact of growth phase transitions on water requirement predictions and improves prediction accuracy. The model, constructed using a BP neural network and particle swarm optimization algorithm, effectively handles complex multivariate relationships and optimizes model performance. By comparing predicted water requirements with historical actual water requirements to calculate training errors, model parameters can be adjusted promptly to ensure the model consistently approaches reality. The resulting trained model accurately predicts crop water requirements and irrigation time intervals, providing strong support for scientific irrigation.

[0108] Furthermore, the step of obtaining the water demand deviation factor in this embodiment includes:

[0109] S311. Configure water tolerance experiments for crops at different growth stages, and set a two-way water requirement prediction model to predict the water requirements of crops at corresponding growth stages as ideal water requirements;

[0110] S312: Using the ideal water requirement as a starting point, increase or decrease the water requirement of the crop during the corresponding growth period, monitor the crop growth status corresponding to the water requirement at each moment, and automatically evaluate the crop growth status score under the corresponding water requirement using a configured evaluation algorithm;

[0111] S313, using the water requirement and the growth status score corresponding to the water requirement, obtaining a water requirement-growth score change curve corresponding to different growth stages of the crop throughout its entire growth period;

[0112] S314. Taking the ideal water requirement corresponding to the crop growth status score in each growth stage as a starting point, when the water requirement is increased or decreased and the growth status score corresponding to the corresponding water requirement is less than the crop growth status score corresponding to the ideal water requirement for m consecutive times, obtain the minimum and maximum water requirements corresponding to the beginning of the decrease in the crop growth status score in the corresponding growth stage;

[0113] S315. Obtain a lower limit deviation factor of the water requirement for the corresponding growth period based on the minimum water requirement and the ideal water requirement. Meanwhile, obtain an upper limit deviation factor of the water requirement for the corresponding growth period based on the maximum water requirement and the ideal water requirement.

[0114] Furthermore, the calculation process of the water demand deviation factor in this embodiment quantifies the relative difference between the crop's ideal water requirement and its actual water tolerance threshold. Specifically, when determining the lower limit deviation factor for a particular growth stage, the ideal water requirement for that stage is first used as a benchmark, from which the experimentally measured minimum water requirement (i.e., the minimum water requirement corresponding to the first sustained decrease in the crop's growth status score) is subtracted. The difference between the two is then divided by the ideal water requirement. The resulting ratio represents the maximum relative threshold for the allowable reduction in water demand for that stage. Similarly, the upper limit deviation factor is calculated by dividing the difference between the critical maximum water requirement (the maximum water requirement before the growth status score has a sustained decrease) and the ideal water requirement by the ideal water requirement, reflecting the maximum increase in water demand that the crop can tolerate. This method converts the absolute difference between the critical water requirement and the ideal value into a normalized ratio based on the ideal value, eliminates the interference of the absolute difference in water demand at different growth stages, and constructs a dimensionless dynamic tolerance interval parameter, thereby accurately characterizing the response boundary of crops to water fluctuations and providing a unified and comparable deviation control benchmark for the dynamic adaptation of irrigation strategies across growth periods.

[0115] During this process, irrigation thresholds for different growth stages are determined based on the calculation results of a two-way prediction model for crop water requirements and the crop's tolerance to water stress. Irrigation thresholds include lower and upper thresholds. When the lower threshold is triggered, it indicates that soil moisture has dropped to a level that may affect normal crop growth, and irrigation is needed to replenish water. When the upper threshold is reached, it indicates that soil moisture is nearing saturation or that excessive moisture may have adverse effects on crops (such as root hypoxia), and irrigation should be stopped. For example, during the wheat jointing stage, based on previous research and model predictions, the lower threshold for soil volumetric water content is determined to be 60% of field capacity, and the upper threshold is 85% of field capacity.

[0116] This process determines ideal water requirements by configuring water tolerance experiments and combining them with predictive models, providing a benchmark for subsequent analysis. By adjusting and monitoring crop growth status and scores based on ideal water requirements, a precise water requirement-growth score curve can be plotted, visually presenting the relationship between water requirements and growth status. The resulting lower and upper water requirement deviation factors clarify the range of water requirements that crops can tolerate at each growth stage. This facilitates precise irrigation, avoiding the impact of insufficient or excessive irrigation on crop growth, ensuring crop growth and development under optimal water conditions, improving crop yield and quality, and achieving efficient use of water resources.

[0117] Furthermore, in this embodiment, the irrigation strategy for each crop corresponding to the segmented growth period includes detailed information such as the start and end time of irrigation, irrigation duration, irrigation water volume, and irrigation area;

[0118] Furthermore, the steps of constructing the irrigation intensification decision model in this embodiment include:

[0119] S401, constructing a water requirement interval for each growth stage of the crop based on the ideal water requirement, the water requirement lower limit deviation factor, and the water requirement upper limit deviation factor;

[0120] S402: The water requirement interval of each growth stage is used as a variable irrigation prescription map for the crop, and irrigation strategy execution action trigger information is set according to the variable irrigation prescription map, specifically:

[0121] S4021: When the water holding capacity of the crop growing area at the current moment is lower than the lower limit of the variable irrigation prescription map, irrigation is directly carried out until the ideal water requirement of the corresponding growth stage is reached;

[0122] Furthermore, when the corresponding irrigated crop is rice, the comparison here is: when the water holding capacity of the corresponding crop growth area at the current moment is lower than the water level depth of the plot corresponding to the lower limit of the variable irrigation prescription map, irrigation is directly carried out until the ideal water requirement depth of the corresponding growth stage of rice is reached;

[0123] Furthermore, in this embodiment, the corresponding variable irrigation prescription map includes the cumulative average rainfall, that is, meteorological data. When the weather changes, causing the water demand interval of the corresponding plot or the depth interval corresponding to the rice field to change, the corresponding variable irrigation prescription map or the plot water level depth change interval will change accordingly at the same time, thereby achieving the purpose of adjusting the water demand of the corresponding plot according to the weather conditions.

[0124] Furthermore, the water holding capacity of the crop growing area in this embodiment is the water holding capacity of the crop monitored at the current moment;

[0125] S4022: When the water holding capacity of the corresponding crop growing area at the current moment is lower than the ideal water requirement for the corresponding growth stage, pre-adjust the parameters of the irrigation intensification decision model according to the ideal water requirement for the corresponding growth stage. When the water holding capacity of the corresponding crop growing area reaches the lower limit of the variable irrigation prescription map, control the irrigation device to irrigate using the pre-adjusted parameters of the irrigation intensification decision model until the ideal water requirement for the corresponding growth stage is reached.

[0126] S4023: If the water holding capacity of the crop growth area at the current moment is higher than the ideal water requirement of the corresponding growth stage, no irrigation is performed; if the water holding capacity is lower than the ideal water requirement of the corresponding growth stage, the process of S4022 is repeated;

[0127] In this embodiment, the water requirement intervals corresponding to different crops at different time points during their growth period are obtained through the above process. If the irrigated crop is rice, the obtained water requirement is converted into a water requirement depth interval prescription map for the corresponding plot based on the corresponding plot area, the current water depth of the plot, and the soil moisture conditions of the plot, such as Figure 3 A-1 to A-5, B-1 to B-5, C-1 to C-5, and D-1 to D-5 correspond to rice plots at different current growth stages and the predicted rice water requirement depth intervals for the corresponding plots. However, in the actual irrigation and display process, the average value of the water requirement depth interval prescription map of the corresponding plot is used as the actual water requirement depth of the corresponding rice plot for real-time irrigation recommendation and display, such as Figure 3 The different colors in the middle legend correspond to the average recommended irrigation depths corresponding to the water requirement depth range prescription map of the plot, including 8mm, 10mm, 12mm, 14mm, etc.

[0128] For other dryland crops, such as wheat and corn, the corresponding water requirement interval is directly used as a variable irrigation prescription map for irrigation control;

[0129] The variable irrigation prescription map for each crop's corresponding growth stage is input into the configured irrigation device for irrigation. The growth status and water storage status of the crops in the corresponding irrigation area after irrigation are monitored and evaluated in real time. The evaluation results are fed back to the bidirectional water demand prediction model and the irrigation intensification decision model to adjust the predicted water storage and irrigation strategy in real time. Furthermore, the process uses the configured comprehensive fuzzy evaluation algorithm to conduct real-time assessment of the crop's growth status.

[0130] In this embodiment, the irrigation strategy is sent to the automated irrigation control subsystem, which can be a PLC (programmable logic controller)-based intelligent irrigation system or other types of automated irrigation equipment. The automated irrigation control subsystem accurately controls the opening and closing of irrigation valves, the start and stop of water pumps, and the operation of sprinklers based on the received instructions, thereby achieving automated and precise irrigation.

[0131] After each irrigation session, soil moisture sensors are used to monitor soil moisture changes. Combined with data from crop growth indicators (such as plant height, leaf area, and biomass), the effectiveness of the irrigation session is evaluated. For example, soil moisture levels are monitored to determine if they rise within a reasonable range and remain stable, and if irrigation promotes crop growth and prevents abnormal growth caused by excessive or insufficient water.

[0132] Based on the irrigation effectiveness evaluation results, feedback adjustments are made to the two-way crop water requirement prediction model, irrigation thresholds, and parameters in the irrigation plan. If soil moisture recovery after a particular irrigation session is found to be too slow or too rapid, the calculation parameters for irrigation volume or irrigation timing may need to be adjusted appropriately. If crop growth anomalies, such as yellowing leaves or stunted growth, the rationality of irrigation thresholds may need to be re-examined and optimized based on the two-way crop water requirement prediction model and actual conditions. Through continuous evaluation and feedback adjustments, the irrigation decision-making method can better adapt to the irrigation needs of crops in different years, seasons, and field conditions.

[0133] Furthermore, in this embodiment, a variable irrigation prescription map and an irrigation intensification decision-making model are constructed, and execution action trigger information is set based on the water demand interval, which can achieve precision irrigation. The decision on whether to irrigate and the method of irrigation are made based on the comparison between the water volume in the crop growth area and the trigger interval, avoiding excessive or insufficient irrigation and improving the efficiency of water resource utilization. The irrigation strategy is applied in practice and monitored and evaluated in real time. The feedback results are used to adjust the prediction and decision-making models, so that the models are continuously optimized and more in line with actual conditions. Precision irrigation helps create a suitable growth environment for crops, promotes crop growth and development, and improves crop yield and quality. At the same time, this dynamic adjustment mechanism can adapt to environmental changes and crop growth dynamics, enhancing the flexibility and adaptability of the irrigation system.

[0134] Example 2

[0135] See also Figure 4 Another embodiment provided by the present invention is a crop irrigation decision-making system based on the water requirement characteristics of crops during their growth period, comprising: a prediction module and an irrigation decision-making module.

[0136] A prediction module, for monitoring the soil water content of the crop soil area to be determined based on the remote sensing satellite image and the temperature vegetation drought index;

[0137] The irrigation decision module is used to generate a variable irrigation prescription map based on the soil water content and the segmented growth period and transition time period attributes of each crop, combined with a preset variable precision irrigation decision model, to decide on crop irrigation in the soil area of ​​the crop to be decided.

[0138] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also change, modify, replace and modify the above-mentioned embodiments without departing from the scope of protection of the purpose of the present invention and the claims, and all of these are protected by the present invention.

[0139] If the technical solution disclosed herein involves personal information, the product using the technical solution disclosed herein has clearly informed the individual of the personal information processing rules and obtained the individual's voluntary consent before processing the personal information. If the technical solution disclosed herein involves sensitive personal information, the product using the technical solution disclosed herein has obtained the individual's separate consent before processing the sensitive personal information and at the same time meets the "explicit consent" requirement. For example, on personal information collection devices such as cameras, a clear and prominent sign is set to inform that the individual has entered the personal information collection scope and that personal information will be collected. If the individual voluntarily enters the collection scope, it is deemed that they agree to the collection of their personal information; or on the personal information processing device, when the personal information processing rules are notified by obvious signs / information, the individual's authorization is obtained through pop-up information or by asking the individual to upload their personal information. The personal information processing rules may include information such as the personal information processor, the purpose of personal information processing, the processing method, and the type of personal information processed.

Claims

1. A crop irrigation decision-making method based on the water demand characteristics of crops during their growth period, characterized in that the steps include: Obtain remote sensing satellite images of the soil area of ​​the crop to be determined and attributes of the segmented growth period and transition time period of each crop; the attributes of the segmented growth period and transition time period of each crop include: corresponding meteorological data, evaporation conditions, soil moisture conditions, crop absorption rate, standard water requirement space of the kth growth period, and meteorological data, evaporation conditions, soil moisture conditions, and crop absorption rate data of the pre-transition section and post-transition section of the kth growth period; Monitoring the soil water content of the crop soil area to be decided based on the remote sensing satellite imagery and the temperature vegetation drought index; Based on the soil water content and the segmented growth period and transition period attributes of each crop, combined with a preset variable precision irrigation decision model, a variable irrigation prescription map is generated to determine the crop irrigation in the soil area to be determined; The variable precision irrigation decision model is obtained by integrating the water demand bidirectional prediction model and the irrigation intensification decision model; The steps of constructing and training the water demand bidirectional prediction model include: S301, setting a pre-transition section and a post-transition section corresponding to each growth stage of the crop to be decided, and constructing a transition change function based on the historical water requirements corresponding to the pre-transition section and the post-transition section; The front transition section is a time period corresponding to the growth stage that is shifted forward by a first preset number of days, and the rear transition section is a time period corresponding to the growth stage that is shifted backward by a first preset number of days; S302, based on BP neural network and particle swarm algorithm to build a water demand bidirectional prediction model integrated K A water demand prediction sub-model is built, and the transition change function is built into the water demand prediction sub-model corresponding to the adjacent growth period, and the crop water demand dynamic balance prediction equation of the corresponding growth period is built into the water demand prediction sub-model of the corresponding growth period; S303, the k The corresponding meteorological data, evaporation status, soil moisture, crop absorption rate, k The standard water space required for each growth period and the k The meteorological data, evaporation status, soil moisture, crop absorption rate data and soil water content of the crop soil area to be decided in the first growth period are input into the first k The water demand prediction sub-model corresponding to each growth period is trained to obtain K The training error of transition section prediction and the training error of non-transition section corresponding to each water demand prediction sub-model; S304, set the training error threshold, according to the obtained K The transition prediction training error and K -1 non-transition segment training error is used to construct a comprehensive training loss function. When the comprehensive training loss function is less than the training error threshold, a trained water demand prediction sub-model is obtained.

2. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 1, characterized in that: The monitoring of soil water content in the crop soil area to be determined includes: Based on the remote sensing satellite images, soil water content, temperature vegetation drought index, surface temperature and normalized difference vegetation index under drought stress are inverted; The soil water content of the crop soil area to be decided is inverted based on the measured soil water content, soil water content under drought stress, temperature vegetation drought index, surface temperature and normalized difference vegetation index.

3. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 2, characterized in that: The step of generating a variable irrigation prescription map by combining a preset variable precision irrigation decision model includes: Based on the properties of each crop's segmented growth period and transition period and soil moisture content, a two-way water demand prediction model is constructed and pre-trained to obtain the corresponding crop water demand and irrigation time interval; The water requirement deviation factor is obtained according to the water tolerance of each growth stage of the corresponding type of crop; Based on the water requirements of the corresponding types of crops, meteorological data, soil moisture conditions, irrigation time intervals and water demand deviation factors, a variable irrigation prescription map for the soil area of ​​the crop to be decided is generated through the configured irrigation intensification decision model.

4. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 3, characterized in that: The steps of obtaining the transition segment prediction training error and the non-transition segment training error include: S3031, when k If the water demand prediction time point of the kth growth period is in the non-transition period of the current growth period, the water demand prediction sub-model corresponding to the kth growth period is used to predict the water demand at the corresponding time point, and the predicted water demand is combined with the historical real water demand to obtain the kth growth period water demand. k Non-transition segment training error; S3032, when k When the water demand forecast time point of the current growth period is in the transition period before the current growth period, k The water demand prediction sub-model corresponding to each growth period is used for pre-prediction, and the first k The pre-transition period of the reproductive period corresponds to t The water demand forecast at the moment, and at the same time k -1 The water demand prediction sub-model corresponding to the growth period predicts the water demand at the same time.

5. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 4, characterized in that: The step of obtaining the transition segment prediction training error and the non-transition segment training error further includes: S3033, using k The water demand forecast obtained by the water demand forecast sub-model corresponding to the kth growth period and the water demand forecast obtained by the water demand forecast sub-model corresponding to the k-1th growth period at the same time point are used as the average of the water demand forecast in the pre-transition period. t The water demand forecast corresponding to the time t The water demand forecast corresponding to the moment and the water demand at the corresponding time point are used to obtain the transition segment prediction training error corresponding to the time point of the pre-transition segment; S3034, when k When the water demand forecast time point of the current growth period is in the post-transition period of the current growth period, repeat the S3032 process to obtain the k The water demand prediction sub-model for each growth period corresponds to the post-transition period. t The water demand forecast at the moment and k+ The water demand prediction amount corresponding to the water demand prediction sub-model of one growth period at the same time point is repeated in S3033 to obtain the transition segment prediction training error corresponding to the time point of the post-transition segment.

6. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 5, characterized in that: The step of obtaining the water demand deviation factor includes: S311. Configure water tolerance experiments for crops at different growth stages, and set a two-way water requirement prediction model to predict the water requirements of crops at corresponding growth stages as ideal water requirements; S312: Using the ideal water requirement as a starting point, increasing or decreasing the water requirement of the crop during the corresponding growth period, monitoring the crop growth status corresponding to the water requirement at each moment, and automatically evaluating the crop growth status score under the corresponding water requirement using a configured evaluation algorithm; S313, using the water requirement and the growth status score corresponding to the water requirement, obtaining a water requirement-growth score change curve corresponding to different growth stages of the crop throughout its entire growth period; S314, taking the ideal water requirement corresponding to the crop growth status score in each growth stage as the starting point, when the water requirement is increased or decreased and the corresponding growth status score corresponding to the water requirement is continuously increased or decreased, m When the water requirement is less than the crop growth status score corresponding to the ideal water requirement, the minimum water requirement and the maximum water requirement corresponding to the crop growth status score starting to decrease in the corresponding growth period are obtained; S315. Obtain a lower limit deviation factor of the water requirement for the corresponding growth period based on the minimum water requirement and the ideal water requirement. Meanwhile, obtain an upper limit deviation factor of the water requirement for the corresponding growth period based on the maximum water requirement and the ideal water requirement.

7. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 6, characterized in that: The steps of constructing the irrigation intensification decision model include: S401, constructing a water requirement interval for each growth stage of the crop based on the ideal water requirement, the water requirement lower limit deviation factor, and the water requirement upper limit deviation factor; S402: Using the water requirement interval of each growth stage as a variable irrigation prescription map, and setting irrigation strategy execution action trigger information according to the variable irrigation prescription map.

8. The crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to claim 7, characterized in that: The step of setting the irrigation strategy execution action trigger information includes: S4021: When the water holding capacity of the crop growing area at the current moment is lower than the lower limit of the water requirement range in the variable irrigation prescription map, irrigation is directly carried out until the ideal water requirement of the corresponding growth stage is reached; S4022: When the current water holding capacity of the corresponding crop growing area is lower than the ideal water requirement for the corresponding growth stage, pre-adjust the parameters of the irrigation intensification decision model based on the ideal water requirement for the corresponding growth stage. When the water holding capacity of the corresponding crop growing area reaches the lower limit of the water requirement range in the variable irrigation prescription map, control the irrigation device to irrigate using the pre-adjusted parameters of the irrigation intensification decision model until the ideal water requirement for the corresponding growth stage is reached. S4023. When the water holding capacity of the crop growth area at the current moment is higher than the ideal water requirement of the corresponding growth stage, no irrigation is performed. When the water holding capacity is lower than the ideal water requirement of the corresponding growth stage, the process of S4022 is repeated.

9. A crop irrigation decision-making system based on the water requirement characteristics of crops during their growth period, which is used to implement the crop irrigation decision-making method based on the water requirement characteristics of crops during their growth period according to any one of claims 1 to 8, characterized in that: include: Prediction module and irrigation decision module; The prediction module is used to monitor the soil water content of the crop soil area to be determined based on the remote sensing satellite image and the temperature vegetation drought index; The irrigation decision module is used to generate a variable irrigation prescription map based on the soil water content and the segmented growth period and transition time period attributes of each crop, combined with a preset variable precision irrigation decision model, to decide on crop irrigation in the soil area of ​​the crop to be decided.

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