Irrigation decision-making method and device for crop area and storage medium

Abstract

The invention relates to the field of agricultural production, and provides an irrigation decision-making method and device for a crop area and a storage medium. The irrigation decision-making method comprises the following steps: identifying terrain information of a crop area based on unmanned aerial vehicle image data, and dividing the crop area into a plurality of terrain subareas according to the terrain information; after a water level measuring device is installed in the target terrain subarea, water level data of the target terrain subarea are obtained based on the water level measuring device under the condition that the water level of the crop area is kept unchanged within a preset duration, and water level data of other terrain subareas are collected so as to determine the water level difference value between different terrain subareas; in the crop growth period, real-time water level data of the target terrain subarea are obtained through water level measuring equipment, and water level data of other terrain subareas are calculated according to the real-time water level data and the water level difference value; and determining an irrigation scheme of each terrain subarea according to the current growth stage of the crops and the water level data of each terrain subarea. And intelligent decision-making on-demand irrigation is realized.

Description

Technical Field

[0001] This application relates to the field of agricultural production, specifically to an irrigation decision-making method, apparatus, and storage medium for crop areas. Background Technology

[0002] Scientific and rational irrigation methods can not only increase crop yields but also reduce water waste and alleviate regional water shortage pressures. However, current paddy field irrigation largely relies on traditional experience or simple automated equipment, which generally suffers from problems such as inaccurate irrigation timing, difficulty in accurately measuring water consumption, and a lack of basis for optimizing regional water resource allocation.

[0003] Water level gauges typically only acquire water level data at a specific location within a field, making it difficult to comprehensively reflect the moisture status of the entire field. Differences in factors such as topography, soil type, and crop distribution within a field can lead to uneven water distribution, and single-point data cannot accurately represent the overall situation. Irrigation decisions based on single-point data may result in under- or over-irrigation in some areas, affecting crop growth and yield. Furthermore, the use of future meteorological data in decision-making often only considers future rainfall, neglecting the influence of other meteorological indicators. Summary of the Invention

[0004] The purpose of this application is to provide an irrigation decision-making method, apparatus, and storage medium for crop areas.

[0005] To achieve the above objectives, the first aspect of this application provides an irrigation decision-making method for crop areas, the irrigation decision-making method comprising: Based on UAV imagery data, the terrain information of crop areas is identified, and the crop areas are divided into multiple terrain zones according to the terrain information. After the water level measuring device is installed in the target terrain zone, and the water level in the crop area remains unchanged within a preset time, the water level data of the target terrain zone is obtained based on the water level measuring device, and water level data of other terrain zones are collected to determine the water level difference between different terrain zones. During crop growth, real-time water level data of the target terrain zone is obtained through water level measurement equipment, and water level data of other terrain zones are calculated based on the real-time water level data and water level difference. The irrigation plan for each terrain zone is determined based on the current growth stage of the crop and the water level data of each terrain zone.

[0006] In this embodiment of the application, the irrigation plan for each terrain zone is determined based on the current growth stage of the crop and the water level data of each terrain zone, including: obtaining the standard water level range corresponding to the current growth stage of the crop; determining the total area ratio of the terrain zones within the standard water level range based on the water level data of each terrain zone; and generating irrigation instructions based on the total area ratio and the real-time water level data of the target terrain zone.

[0007] In this embodiment of the application, an irrigation instruction is generated based on the total area ratio and the real-time water level data of the target terrain zone, including: when the total area ratio is higher than a first threshold and the real-time water level data of the target terrain zone is greater than zero, an instruction to maintain the water layer is generated to control the irrigation equipment to standby; when the total area ratio is lower than the first threshold and / or the real-time water level data of the target terrain zone is less than or equal to zero, an instruction to start irrigation is generated to control the irrigation equipment to irrigate the crop area.

[0008] In this embodiment of the application, after generating the instruction to start irrigation, the irrigation decision method further includes: controlling the irrigation equipment to irrigate until the stop condition is met. The stop condition is that the total area ratio of the terrain zone within the standard water level range is higher than the second threshold, and the real-time water level data of the target terrain zone is lower than the preset maximum water level standard.

[0009] In this embodiment of the application, the irrigation decision-making method further includes: acquiring predicted meteorological data; and dynamically adjusting the standard water level range and irrigation control instructions based on the predicted meteorological data.

[0010] In this embodiment of the application, the standard water level range or irrigation control command is dynamically adjusted according to the predicted meteorological data, including: if the predicted meteorological data indicates that high temperature weather that meets the first condition will occur within a preset time period in the future, the standard water level range will be adjusted upward as a whole; if the predicted meteorological data indicates that rainfall weather that meets the second condition will occur within a preset time period in the future, the irrigation amount will be postponed or reduced.

[0011] In this embodiment of the application, the crop area is divided into multiple terrain zones according to the terrain information, including: dividing the crop area into at least a high terrain zone, a medium terrain zone and a low terrain zone according to the terrain height, wherein the high terrain zone is the target terrain zone.

[0012] In this embodiment of the application, collecting water level data of other terrain zones includes: for each other terrain zone, selecting multiple measurement points within the zone to measure water level, and taking the average water level of the multiple measurement points as the water level data of the terrain zone.

[0013] A second aspect of this application provides an irrigation decision-making apparatus for crop areas, comprising: a memory configured to store instructions; a processor configured to retrieve instructions from the memory and, when executing the instructions, to implement any of the above-described irrigation decision-making methods for crop areas.

[0014] A third aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform any of the above-described irrigation decision-making methods for crop areas.

[0015] This application presents an irrigation decision-making method for crop areas. By analyzing UAV imagery to identify terrain elevation and divide the area into zones, water level gauges are deployed in the highest representative areas. A single calibration establishes a water level relationship model for the entire field, enabling the estimation of overall water conditions from a single point measurement. Combining real-time water level data, zone area ratios, and forecasted meteorological information, an intelligent decision-making mechanism is constructed based on dual judgments of area compliance rate and high-level water level. Ultimately, this achieves precise irrigation across the entire area at the cost of single-point monitoring, effectively addressing the industry pain point of insufficient representativeness of single-point data and significantly improving water resource utilization efficiency and irrigation decision-making accuracy.

[0016] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings: Figure 1 The illustration shows a schematic flowchart of an irrigation decision-making method for crop areas according to an embodiment of this application; Figure 2 This illustration schematically shows a process diagram for rice identification and field zoning according to an embodiment of this application; Figure 3 This illustration schematically shows an irrigation decision-making process according to an embodiment of the present application; Figure 4 This illustration schematically shows a rice identification image (a) generated from UAV imagery at a specific moment according to an embodiment of this application. Figure 5 This illustration schematically shows a rice identification image (II) generated from UAV imagery at a specific moment according to an embodiment of this application. Figure 6 This illustration schematically shows a rice score distribution diagram according to an embodiment of this application; Figure 7 A schematic diagram illustrating the results of field zoning according to an embodiment of this application is shown. Figure 8 The diagram illustrates the internal structure of a computer device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0019] Figure 1 The illustration schematically shows a flowchart of an irrigation decision-making method for crop areas according to an embodiment of this application. Figure 1 As shown in one embodiment of this application, an irrigation decision-making method for crop areas is provided, including the following steps: Step 101: Identify the terrain information of the crop area based on UAV image data, and divide the crop area into multiple terrain zones according to the terrain information; Step 102: After installing the water level measuring device in the target terrain zone, and under the condition that the water level in the crop area remains unchanged within a preset time, the water level data of the target terrain zone is obtained based on the water level measuring device, and water level data of other terrain zones are collected to determine the water level difference between different terrain zones. Step 103: During the crop growth period, real-time water level data of the target terrain zone is obtained through water level measurement equipment, and water level data of other terrain zones are calculated based on the real-time water level data and water level difference. Step 104: Determine the irrigation plan for each terrain zone based on the current growth stage of the crop and the water level data of each terrain zone.

[0020] Within 7-10 days after rice transplanting or 15-20 days after direct seeding, the field should be drained before irrigation. After draining, acquire drone data 1, and then acquire one drone data image every half hour, for a minimum of 5 images. Record drone data 1, drone data 2, drone data 3, ..., drone data n according to the acquisition time. The acquired high-resolution images must have a resolution of at least 512×512 pixels. Label the acquired rice drone image data with rice, and train a rice image recognition algorithm, or directly use a publicly available rice recognition algorithm. Using the rice recognition algorithm, obtain a rice recognition image for each drone image scene; the presence of rice is marked as 1, and the absence of rice is marked as 0, thus obtaining the rice recognition result. Figure 1 Rice identification Figure 2Rice identification Figure 3 ...rice paddy identification image n. Multiple images at different water levels are overlaid and analyzed to obtain a score for each pixel. The scores are then evenly distributed, with higher scores indicating higher elevations. The fields are divided into high-lying, mid-lying, and low-lying areas. More levels can be used for higher accuracy in practical applications. The area of ​​the high-lying area is then obtained. Area of ​​the central section of the field Low-lying area of ​​the field .

[0021] The water level measuring equipment was installed in the most representative target terrain zone, i.e., the highest elevation zone. Maintaining a constant level for a preset time period means the field is in a static water balance state, ensuring that the measured water level difference is purely caused by topographic elevation differences, rather than dynamic water flow interference. A moment when the water layer in the field is stable was selected for a one-time comprehensive manual measurement. Data from the high elevation zone water level gauge was read, and simultaneously, water levels at multiple points in the middle and low elevation zones were manually measured and averaged. The stable water level difference between each zone and the high elevation zone was calculated. The water level difference refers to the fixed difference in water depth between different terrain zones due to topographic elevation differences. In this embodiment, , , and The water level is assumed to remain essentially constant throughout the growing season. After the one-time measurement completed in the aforementioned steps, no further manual field measurements of the water levels in the middle and lower zones are required throughout the crop growing season. The system only needs to periodically read the real-time data from the water level gauge installed in the higher zone, and then use the pre-determined, fixed water level difference to calculate the water level data for the entire field. Throughout the crop growing season, the real-time water level in the higher zone is obtained through the water level gauge. And based on the calculated difference , Calculate the real-time water level in the central area and real-time water level in low-lying areas .

[0022] Irrigation plans for each terrain zone are determined based on the crop's current growth stage and water level data for each zone. Rice has different water depth requirements at different growth stages, which are defined as the standard water level range. The highest water level standard is defined as... The system calculates the percentage of field area that has reached the standard water level based on the overall field water level data obtained from the preceding steps. Irrigation is triggered if the percentage of compliant area is low, or if the high-level water level gauge reading has dropped to zero or below. In one embodiment, a high-level water level gauge reading of zero or below indicates that the highlands are beginning to dry out. The goal of irrigation is to bring the vast majority of field area up to standard while ensuring that the high-level water level does not exceed the maximum limit to prevent over-irrigation of low-level areas.

[0023] This application uses drone imagery analysis to divide the terrain into zones and deploys water level gauges in the highest representative areas. Through a one-time calibration, a water level relationship model for the entire field is established, enabling single-point measurement to predict the overall water distribution. Combining zoned water level data with crop growth stage requirements, an intelligent decision-making mechanism is constructed based on dual judgments of area compliance rate and high-level water level. Ultimately, this solves the industry pain point of insufficient representativeness of traditional methods at the cost of single-point monitoring, achieving a leap from local perception to precise irrigation across the entire region, and significantly improving water resource utilization efficiency and irrigation decision accuracy.

[0024] In one embodiment, the crop area is divided into multiple terrain zones based on topographic information, including: dividing the crop area into at least a high-altitude zone, a mid-altitude zone, and a low-altitude zone according to elevation, wherein the high-altitude zone is the target terrain zone. Topographic information refers to relevant data reflecting changes in surface elevation within the crop area, typically expressed in the form of elevation values, slope, and aspect. Topographic information can be obtained through various methods, such as measurement or calculation using Global Positioning System (GPS), Real-Time Kinematics (RTK), UAV mapping, remote sensing imagery, or Digital Elevation Model (DEM). Based on topographic information, the same crop area is divided into multiple sub-regions with relatively consistent or similar topographic characteristics. There are significant differences between different terrain zones in indicators such as elevation or slope. Through terrain zoning, differentiated water and fertilizer management or irrigation strategies can be formulated for different regions. A high-altitude zone refers to a terrain zone within the crop area with a relatively high elevation value, typically referring to an area with an elevation value greater than a preset elevation threshold, or an area in the upper quantile range in elevation ranking. In this embodiment, the top 30% of elevations can be designated as high-altitude areas. Mid-altitude areas refer to terrain zones within a crop region where elevation values ​​are at a moderate level, typically areas within a preset mid-altitude range. In this embodiment, terrain ranking between 30% and 70% of elevations can be designated as mid-altitude areas. Low-altitude areas refer to terrain zones within a crop region where elevation values ​​are relatively low, typically areas with elevation values ​​below a preset low-altitude threshold or in the lower quantile range of elevation ranking. In this embodiment, terrain ranking in the bottom 30% of elevations can be designated as mid-altitude areas. Target terrain zones refer to key management targets selected from multiple terrain zones based on preset strategies or control requirements. In this embodiment, target terrain zones can be designated as high-altitude areas.

[0025] In one embodiment, collecting water level data for other terrain zones includes: for each other terrain zone, selecting multiple measurement points within the zone to measure water levels, and using the average water level of the multiple measurement points as the water level data for that terrain zone. In this embodiment, it is assumed that the crop area has been divided into high-altitude, mid-altitude, and low-altitude zones based on the terrain information of the crop area, and the high-altitude zone is set as the target terrain zone. Correspondingly, the mid-altitude and low-altitude zones can be collectively referred to as other terrain zones. In other embodiments, other terrain zones may also include any one or more terrain sub-regions other than the target terrain zone. To collect water level data for other terrain zones, in this embodiment, multiple measurement points are selected within each other terrain zone, specifically including but not limited to the following steps: pre-setting the number of measurement points based on the area size, topographic relief, and uniformity of crop planting of the other terrain zones. In this embodiment, for smaller, relatively flat central areas, 3-5 measurement points can be set; for larger, more complex low-lying areas, 5-10 or more measurement points can be set to improve the representativeness of the water level data. Within each other terrain zone, measurement points can be selected using a regular grid or block method to ensure even distribution within the zone and avoid concentration in local areas. The density of measurement points can be appropriately increased in areas with significant local terrain differences, and appropriately reduced in areas with relatively flat and uniform terrain, balancing accuracy and cost. After the measurement points are deployed, water level measurements are performed on multiple measurement points within each other terrain zone. In some implementations, anomaly detection can be performed on the water level data of each measurement point, for example, removing measurement data that significantly exceeds a reasonable range (such as extreme values ​​caused by sensor malfunction) to avoid affecting the accuracy of the zone's average value. For each other terrain zone, this embodiment obtains the water level data of that zone by statistically analyzing the water level values ​​of multiple measurement points. The arithmetic mean of water level data from multiple measurement points within each other terrain zone is calculated to obtain the average water level of that terrain zone at time point t. Alternatively, different weights can be assigned to the water level data of each measurement point based on its importance or reliability, resulting in a weighted average water level value. In one embodiment, measurement points closer to the irrigation inlet have a higher weight, while those in peripheral areas have a relatively lower weight. The calculated average value is stored as the water level data for that terrain zone at the corresponding time point. A "Terrain Zone Water Level Data Table" can be established in the database to record information such as zone number, timestamp, average water level value, and the number of measurement points involved in the calculation. Subsequently, in the irrigation control algorithm, the water level data of each terrain zone is used as input parameters to achieve fine-grained irrigation regulation based on the water level status of each zone.As an aggregated data that has undergone spatial statistical processing, the average water level of each zone is more suitable as input for subsequent irrigation prediction models, yield assessment models, or water balance analysis models, which is beneficial to improving the stability and generalization ability of model training and prediction.

[0026] In one embodiment, the irrigation plan for each terrain sub-region is determined based on the current growth stage of the crop and water level data for each sub-region. This includes: obtaining the standard water level range corresponding to the current growth stage of the crop; determining the total area proportion of terrain sub-regions within the standard water level range based on the water level data for each sub-region; and generating irrigation instructions based on the total area proportion and real-time water level data of the target terrain sub-region. Rice has different water requirements at different growth stages. In one embodiment, the tillering stage of rice may require shallow irrigation, with a standard water level range of 1cm to 3cm. The jointing and booting stage of rice is most sensitive to water and may require deep irrigation, with the standard water level range potentially increasing to 3cm to 5cm. The ripening stage of rice may require alternating wet and dry periods, with a standard water level range of 0cm to 2cm. This basic knowledge is pre-stored within the system to automatically match the current growth stage and its corresponding ideal water environment based on date or external input. The total area proportion of terrain sub-regions within the standard water level range is determined based on the water level data for each sub-region. In one embodiment, the system determines that the water level in the high zone is 1.5cm, in the middle zone 2.6cm, and in the low zone 3.8cm. Assuming the current standard range is 3-5cm, only the low zone (3.8cm) meets the standard, while the high and middle zones do not. The system calculates the proportion of the compliant zones to the total field area. The total area proportion refers to the ratio of the sum of the areas of the terrain zones with water levels within the standard range to the total area of ​​the entire crop area. When the total area proportion is higher than a first threshold, and the real-time water level data in the high zone is greater than zero, it indicates that most areas of the field have sufficient moisture, and the system generates a "maintain" command to prevent the irrigation equipment from starting. In one embodiment, the first threshold can be 30%. When the total area proportion is lower than the first threshold, or the real-time water level data of the target terrain zone is less than or equal to zero, it indicates that either most areas of the field are short of water, or the high zone has begun to experience drought. Regardless of which condition is triggered, the system immediately generates a "start irrigation" command to start the irrigation equipment. The irrigation command refers to the control signal generated by the control system that directly drives the irrigation equipment to perform actions. The main instructions include "maintain current state" and "start irrigation". In more complex embodiments, a "stop irrigation" instruction may also be included, triggered when the proportion of irrigated area exceeds a higher second threshold and the water level in the high-lying area does not exceed the maximum limit. In one embodiment, the second threshold may be 90%. This embodiment makes irrigation decisions more comprehensive by calculating the total area proportion. By using the high-lying area water level as a key indicator, irrigation decisions become more forward-looking and reliable.

[0027] In one embodiment, an irrigation instruction is generated based on the total area ratio and real-time water level data of the target terrain zone. This includes: generating a water-maintaining instruction to keep the irrigation equipment on standby when the total area ratio is higher than a first threshold and the real-time water level data of the target terrain zone is greater than zero; and generating an irrigation start instruction to control the irrigation equipment to irrigate the crop area when the total area ratio is lower than the first threshold and / or the real-time water level data of the target terrain zone is less than or equal to zero. The trigger condition for generating the water-maintaining instruction is that the total area ratio > the first threshold and the real-time water level data of the target terrain zone > 0. A total area ratio higher than the first threshold indicates that, from a global perspective, the moisture status of most areas within the field is satisfactory or good. For example, if the first threshold is set to 30%, it means that as long as more than 30% of the field area is at a suitable water level, the situation is considered acceptable from a macroscopic perspective. A real-time water level data of the target terrain zone greater than zero indicates that there is still water in the higher areas, meaning that the most drought-prone areas have not yet suffered from drought, and the overall risk of water shortage in the field is low. Only when both conditions are met simultaneously does the system consider the current moisture sufficient, thereby generating the "maintain water level" instruction. Standby refers to the irrigation equipment receiving an instruction but not performing the start-up action, remaining in a static state. The trigger condition for generating a "start irrigation" instruction is that the total area percentage is less than a first threshold and / or the real-time water level data of the target terrain zone is ≤ 0. This condition defines the alarm state where the system determines "immediate irrigation is necessary." A total area percentage below the first threshold indicates that, globally, most areas of the field are already in a water-scarce state, requiring immediate water replenishment for the entire field. Real-time water level data of the target terrain zone being less than or equal to zero is a proactive warning, even if the total area percentage is still acceptable. In one embodiment, the total area percentage is 31%, but as long as the water level gauge reading in the high-altitude area drops to zero or below, it indicates that the highest point has begun to experience drought. If irrigation is not carried out in time, the crops in the high-altitude area will quickly suffer from water stress, therefore immediate irrigation is necessary. If either of the above two conditions is met, the system will immediately generate a "start irrigation" instruction. This embodiment establishes a dual-insurance intelligent decision-making mechanism. By considering both the overall situation through area proportions and closely monitoring the target terrain zones through high-level water levels, irrigation decisions become more comprehensive, effectively avoiding the localized stress and irrigation delays associated with traditional single-point decision-making.

[0028] In one embodiment, the control method further includes: after generating an irrigation start command, the irrigation decision method further includes: controlling the irrigation equipment to irrigate until a stop condition is met. The stop condition is that the total area ratio of the terrain zones within the standard water level range is higher than a second threshold, and the real-time water level data of the target terrain zone is lower than a preset maximum water level standard. Once the system generates an "irrigate start" command based on the aforementioned conditions and starts the irrigation equipment, the system begins to monitor and evaluate the irrigation process in real time to determine the optimal stop time. When the total area ratio of the terrain zones within the standard water level range > the second threshold and the real-time water level data of the target terrain zone < the preset maximum water level standard, the system determines the state of "irrigation target achieved, should stop immediately". A total area ratio higher than the second threshold indicates that, from a global perspective, irrigation has brought most of the field area to the ideal moisture state. In one embodiment, the second threshold is set to 90%. This means that when more than 90% of the field area has reached the standard water level range, the irrigation target is considered to have been achieved. The real-time water level data of the target terrain zone being lower than the preset maximum water level standard is a key safety limit judgment. It aims to prevent over-irrigation. The maximum water level standard is a safe upper limit set to protect crops, prevent fertilizer runoff, and conserve water resources. Using the real-time water level of the highest area as the control benchmark is crucial because as long as the highest area does not exceed this safe limit, over-irrigation is absolutely impossible in the lower middle and low-lying areas. This effectively prevents water waste and waterlogging in low-lying areas. The system considers the irrigation task complete only when both conditions are met simultaneously, generating a "stop irrigation" command and shutting down the irrigation equipment. The maximum water level standard is a safe upper limit for the field water depth set to protect crop growth and resource efficiency. This standard is typically related to the crop's flood tolerance and root aeration requirements, preventing root hypoxia, lodging, or nutrient leaching due to excessive water depth. This embodiment, together with the conditions for starting irrigation, forms a perfect decision-making closed loop. On the one hand, the second threshold ensures sufficient irrigation, solving the problem of insufficient local irrigation; on the other hand, the maximum water level standard in the high-lying areas fundamentally prevents over-irrigation and water and fertilizer waste in low-lying areas, achieving water conservation and increased yield.

[0029] In one embodiment, the control method further includes: the irrigation decision-making method further includes: acquiring predicted meteorological data; and dynamically adjusting the standard water level range and irrigation control instructions based on the predicted meteorological data. First, predicted meteorological data is acquired. This data can be obtained through various meteorological sensors, weather stations, satellite remote sensing technology, etc., and includes information such as precipitation, temperature, humidity, and wind speed for the next few days. Based on the acquired predicted meteorological data, the irrigation decision-making system dynamically adjusts the standard water level range according to factors such as current soil moisture and crop needs. If sufficient precipitation is expected in the next few days, the irrigation system can reduce the irrigation amount or temporarily shut down the irrigation equipment. If the forecast shows drought or no precipitation, the irrigation system will increase the water level range to supplement the water needed for crop growth. In one embodiment, if the meteorological data predicts zero precipitation and high temperatures in the next few days, the irrigation decision-making system may increase the water level range to ensure sufficient soil moisture; if precipitation is predicted, the irrigation system can appropriately reduce the irrigation amount based on the precipitation to avoid wasting water resources. Based on the dynamically adjusted standard water level range, the system automatically generates irrigation control instructions. These instructions will include specific irrigation time, irrigation amount, and other information, and will be executed by automated irrigation equipment. The control commands are also fine-tuned based on real-time soil moisture data monitored by sensors to ensure precise control of irrigation volume. The system monitors the irrigation process in real time, providing feedback on irrigation effectiveness through data such as soil moisture and crop growth status. If soil moisture is found to be above or below the set range, the system will readjust the irrigation control commands. This embodiment, by dynamically adjusting the standard water level range and irrigation control commands, can rationally arrange irrigation based on predicted weather data, avoiding water waste. By precisely controlling the irrigation volume, the appropriate water level required by crops can be maintained, preventing over- or under-irrigation from affecting the healthy growth of crops.

[0030] In one embodiment, the standard water level range or irrigation control instructions are dynamically adjusted based on predicted meteorological data, including: if the predicted meteorological data indicates that high-temperature weather meeting a first condition will occur within a preset future time period, the standard water level range is adjusted upwards as a whole; if the predicted meteorological data indicates that rainfall meeting a second condition will occur within a preset future time period, the irrigation amount is postponed or reduced. Meteorological data for the preset future time period is obtained through a meteorological forecasting system. This data includes meteorological elements such as temperature, precipitation, humidity, and wind speed, and the accuracy of the data prediction can be continuously adjusted through a weather forecasting model. Within the preset future time period, the system determines whether the conditions for high-temperature weather are met based on the meteorological forecast data. High-temperature weather is defined as a temperature exceeding a set temperature threshold. In this embodiment, the set temperature threshold can be 35°C. If the meteorological data indicates that the temperature will exceed this threshold within a certain future time period, it is considered that high-temperature weather will occur during that period. When the meteorological data indicates that high-temperature weather will occur, the system automatically adjusts the standard water level range of the irrigation system, raising it upwards as a whole. In this embodiment, if the standard water level range is set between 60% and 80% of soil moisture, the system can adjust the water level range to 70%-90% under high-temperature weather warnings. This operation ensures sufficient soil moisture to cope with water evaporation loss during high-temperature weather and protects normal crop growth. If the precipitation in the acquired meteorological data exceeds a predetermined threshold in a future period, the system will assume that rainfall will occur during that period. In this embodiment, the set precipitation threshold can be 10 mm. If rainfall is predicted, the system will postpone or reduce irrigation based on the precipitation. The irrigation system can receive changes in meteorological data in real time and adjust irrigation instructions promptly based on updated meteorological forecasts. If the meteorological forecast changes, the system will reassess the standard water level range or irrigation volume to ensure that each adjustment meets the crop's irrigation needs to the greatest extent possible.

[0031] Figure 1 This is a flowchart illustrating an irrigation decision-making method for crop areas in one embodiment. It should be understood that, although... Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise explicitly stated herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least some of the sub-steps or stages of other steps.

[0032] In one embodiment, Figure 2 This diagram illustrates a method for field zoning and water level gauge deployment based on UAV remote sensing data. The method mainly includes the following steps: First, multiple aerial photographs are taken above the target field using a UAV equipped with a visible light camera, acquiring multiple sets of UAV data at different times or flight paths, including UAV data 1, UAV data 2, UAV data 3… UAV data n. Each set of UAV data contains at least an RGB image covering the entire field area and corresponding location information. Then, the multiple sets of UAV data are preprocessed and resampled: UAV images at different flight altitudes, resolutions, or viewpoints are processed through geometric correction, projection transformation, and resolution unification, resampled to a unified spatial resolution and coordinate system for subsequent joint analysis. After resampling, the preprocessed RGB images are input into a rice identification technology based on RGB images for crop identification. Specifically, a classification model based on color features, texture features, and shape features, or a machine learning / deep learning model, can be used to determine whether each pixel is a rice pixel, thus obtaining rice identification results. Figure 1 Rice identification Figure 2 Rice identification Figure 3 …and rice identification map n. Next, the rice identification results from multiple time periods are overlaid and analyzed. Using the field spatial grid cell as the basic unit, the rice identification maps from each time period are overlaid pixel by pixel under the same spatial grid. The number of times each grid cell is identified as rice or the coverage ratio in multiple time periods is counted, and a rice distribution map is generated based on the statistical results. This rice distribution map can reflect the spatial differences and planting density of rice distribution within the entire field. After obtaining the rice distribution map, the field is divided into zones according to the spatial characteristics of rice distribution. In this embodiment, a zoning threshold can be set according to the degree of rice coverage or the size of the rice score. Areas with high rice coverage, dense planting, and good growth are divided into high-field zones; areas with medium rice coverage are divided into middle-field zones; and areas with low rice coverage or sparse planting are divided into low-field zones, thus obtaining the spatial distribution results of high-field zones, middle-field zones, and low-field zones. Finally, after obtaining the above zoning results, this embodiment, based on irrigation control requirements, selects suitable locations for installing monitoring equipment in the high zones of the field and deploys at least one water level gauge. Since the high zones of the field are typically located in areas with higher terrain or better crop growth, the water level monitored in these areas can better represent the areas requiring priority water supply, thus providing more representative data support for subsequent irrigation control strategies. Through the above steps, this embodiment utilizes multi-temporal UAV RGB imagery to achieve precise identification of rice distribution in the field, and based on this, completes the division of the field into high, middle, and low zones and the rational deployment of water level gauges in the high zones, providing reliable basic data for subsequent intelligent irrigation decisions based on zoning water level data.

[0033] In one embodiment, Figure 3This embodiment illustrates an irrigation decision-making method based on field zoning water levels and future meteorological data, following the division of the field into high, middle, and low zones and the installation of water level gauges. First, a "field water retention layer" is established within the target field. This is a pre-defined suitable field water level or soil moisture content range for the current crop growth stage, serving as a reference for subsequent water level assessment and irrigation decisions. The field water retention layer can be set based on crop type, soil type, and historical experience. Subsequently, based on the aforementioned zoning results, the field is divided into high, middle, and low zones. In the high zone, water level data is continuously collected using the aforementioned installed single-point water level gauges to obtain the high zone's single-point water level gauge data. In the middle and low zones, the field water depth is periodically measured manually at several representative locations using measuring tapes or portable water level measuring devices, and recorded as manually measured water level data. Preferably, the middle and low zones each correspond to their respective sets of manually measured data. Next, the single-point water level gauge data of the high-level area of ​​the field and the manually measured water level data of the middle and low-level areas are input into the field water level analysis module. This module first calculates the difference between the current water level and the maintained water layer of each zone, based on the field's water layer, to characterize whether the water level in each zone is too high or too low. Then, combining the zone range, the module interpolates or rasterizes the above differences across the field space to generate a field water level data map, reflecting the spatial distribution of water level across the entire field at the current moment. Based on this, the system reads pre-set water level standards. These standards can define different target water level zones according to different crop growth stages, such as different upper and lower limits for water layer depth corresponding to the seedling stage, tillering stage, and grain-filling stage. Simultaneously, the system also obtains future meteorological data for a preset time period from the meteorological service platform, including at least predicted rainfall and predicted temperature information. Water level standards and future meteorological data are input into the irrigation decision-making standard generation module. On the one hand, if significant rainfall is predicted within a preset time period, the tolerance for current low water levels is appropriately relaxed, or additional irrigation is suppressed. On the other hand, if high temperatures and low rainfall are predicted, the water level standards are adjusted upwards, or the irrigation trigger threshold is increased, ensuring a higher field water level before the arrival of hot weather. This module combines the water level deviation of each zone in the field water level data map to generate irrigation decision standards for different zones. For example, high-level zones are prioritized to ensure water levels do not fall below a certain minimum, while medium and low-level zones are configured with different irrigation strategies based on the degree of exceedance or deficiency. Finally, the irrigation decision module judges the current field water level data map based on the above irrigation decision standards: when the water level in a certain area is lower than the corresponding standard, irrigation is determined for that area; when the water level is higher than or close to the upper limit of the standard, irrigation is temporarily suspended or the irrigation amount is reduced. The system outputs specific irrigation decisions based on the judgment results, such as opening or closing the inlet valves of the corresponding zones, adjusting the irrigation duration or irrigation flow, thereby achieving refined irrigation control based on field zone water levels and future meteorological data.The solution in this embodiment can reduce unnecessary water consumption while ensuring crop growth needs, thereby improving the scientific nature and real-time performance of irrigation decisions.

[0034] In one embodiment, an irrigation control method based on terrain zoning and meteorological forecasting is provided, applied to a smart irrigation control system for farmland. This system includes: a meteorological data acquisition module, a terrain information acquisition module, a zoned water level monitoring module, a data processing and decision-making module, and an irrigation execution module. Specifically, the method includes the following steps: During the farmland construction phase, the spatial boundaries of the crop area to be managed are first determined. This crop area is a field area composed of continuous or several adjacent plots. Terrain information, including elevation values ​​and / or slope information, is acquired at various locations within the crop area using RTK-GPS measurements, UAV aerial surveys, or existing digital elevation model (DEM) data. The terrain information is then spatially discretized according to a preset grid unit to form a terrain distribution dataset for the crop area. The data processing and decision-making module performs statistical analysis on the terrain distribution dataset. Based on the distribution of elevation values ​​throughout the crop area, the crop area is divided into at least three terrain zones: high terrain zone, mid terrain zone, and low terrain zone. Specifically, grid cells with elevation values ​​in the upper quantile interval can be classified as high-altitude areas, those in the middle quantile interval as medium-altitude areas, and those in the lower quantile interval as low-altitude areas. In this embodiment, the high-altitude area is designated as the target terrain zone, serving as a priority reference area for subsequent irrigation control and water level assessment. After completing the terrain zoning, measurement points are deployed for each of the other terrain zones besides the target zone. For each other terrain zone, multiple measurement points are selected within the zone according to a preset grid scale. A corresponding water level measurement device is installed at each measurement point. The water level measurement device can be a soil moisture sensor or a water level gauge, and it is connected to the data acquisition terminal via a wireless communication module to achieve automatic uploading of measurement data. The zone water level monitoring module controls the water level measurement device at each measurement point to perform water level measurement according to a preset sampling cycle, and uploads the acquired water level data along with a timestamp and measurement point number to the data processing and decision-making module. For each other terrain zone, the data processing and decision-making module aggregates water level data from multiple measurement points within that zone at the same sampling time. After filtering outliers, it calculates the arithmetic mean or weighted average of the water levels at each measurement point. This average is used as the zone's water level data at that time and stored in the zone's water level database. The meteorological data acquisition module communicates with an external meteorological service platform or connects to a local meteorological monitoring station to obtain predicted meteorological data for a preset future time period. The predicted meteorological data includes at least hourly or daily meteorological elements such as temperature and rainfall.The data processing and decision-making module, based on predicted meteorological data, determines whether high-temperature weather meeting the first condition and / or rainfall weather meeting the second condition will occur within a preset time period. The first condition for high-temperature weather can be set as follows: within the next 24 hours, the predicted temperature continuously exceeds a first temperature threshold for a duration not less than a first duration threshold. The second condition for rainfall weather can be set as follows: within the next 24 hours, the predicted cumulative rainfall exceeds a second rainfall threshold. Specific thresholds can be preset or adaptively adjusted based on crop variety, soil properties, and local climate characteristics. The system has preset basic standard water level ranges for different crop growth stages, for example, using a soil volumetric moisture content of 60%–80% as the standard water level range for normal irrigation. If the predicted meteorological data indicates that high-temperature weather meeting the first condition will occur within a preset time period, the data processing and decision-making module will adjust the standard water level range upwards, for example, to 70%–90%, and during the decision-making process, require the target terrain zone and other terrain zones to prioritize approaching the adjusted upper limit value to enhance soil water retention capacity and resist high-temperature evaporation. If the predicted meteorological data indicates that rainfall meeting the second condition will occur within a preset time period, the data processing and decision-making module determines whether to postpone irrigation and / or reduce the total irrigation amount based on the predicted rainfall, current zonal water level data, and crop flood tolerance. For example, when the predicted rainfall is sufficient to compensate for the current water shortage, the system can postpone the upcoming irrigation task until after the rainfall ends, or reduce the irrigation amount to a certain percentage below the original plan. After determining the water level data of the target terrain zone and other terrain zones, and completing the irrigation control decision based on the dynamically adjusted standard water level range, the irrigation execution module controls the start and stop of the electric valves, pump stations, or drip / sprinkler irrigation branches corresponding to each zone according to the control instructions: when the water level data of the target terrain zone and the water level data of other terrain zones are all below the lower limit of the adjusted standard water level range, the system opens the corresponding irrigation branches to supply water to each zone. When the water level data of the target terrain zone reaches or exceeds the upper limit of the standard water level range, even if the water level of some low-lying zones is still slightly below the lower limit, the system can also control the gradual reduction of irrigation amount or stop water supply according to the preset strategy to avoid long-term water accumulation in low-lying zones. When significant rainfall is predicted, irrigation can be reduced or stopped in low-lying areas, while a small amount of water can be reserved for high-lying areas to ensure overall water balance. Irrigation results and water level changes in each area are continuously recorded for subsequent model optimization and parameter adjustments. Through these steps, this embodiment organically combines terrain zoning, multi-point water level monitoring, calculation of average water levels in each zone, and dynamic adjustment of standard water level ranges and irrigation control commands driven by predicted meteorological data. This enables refined zoning of farmland irrigation, meteorological adaptation, and efficient water resource utilization.

[0035] In one embodiment, drone data was collected on fields in Anhui province where rice seedlings were transplanted on June 15th, with the data collection time being June 23rd, resulting in five images. Using 24 years of drone image data collected in Anhui province, a rice recognition algorithm was trained by labeling the rice within the data. The rice recognition algorithm was then used to identify the five images, with two images showing the following characteristics: Figure 4 , Figure 5 As shown. Figure 6 As shown, five images are superimposed, with scores ranging from 0 to 5. Figure 7 As shown, the fields are divided into high-lying areas with scores of 4 and 5, and their areas are... =0.89 mu, the corresponding fractions for the divided plots are 2 and 3, and its area is... =0.97 mu, the low-lying area of ​​the field is divided into plots with scores of 0 and 1, its area =1.78 mu. Technicians installed water level gauges in the higher areas of the field on June 24th and collected water level data from three areas on June 25th. The water level gauge readings... =2.3cm, artificial =3.4cm (3cm\3.3cm\4.0cm) and =4.6cm (4.0cm\5.3cm\4.7cm). Technicians calculated the water level difference between different zones and obtained the following: =1.1cm, =2.3cm. The real-time water level in the high zone was then acquired every 4 hours to calculate the real-time water levels in the middle and low zones. Irrigation management was implemented for the field based on irrigation decisions, triggering irrigation 8 times. Statistics showed that rainfall affected the irrigation management time 3 times, and high temperatures affected the irrigation management standards 2 times.

[0036] In one embodiment, an irrigation decision-making device (not shown) for a crop area is provided, comprising: The memory is configured to store instructions; The processor is configured to retrieve instructions from memory and, when executing the instructions, to implement any of the above-mentioned irrigation decision-making methods for crop areas.

[0037] The processor contains a kernel, which retrieves the corresponding program units from memory. One or more kernels can be configured, and irrigation decision-making methods for crop zones can be implemented by adjusting kernel parameters.

[0038] The memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0039] This application provides a storage medium storing a program that, when executed by a processor, implements the above-described irrigation decision-making method for crop areas.

[0040] This application provides a processor for running a program, wherein the program executes the above-described irrigation decision-making method for crop areas.

[0041] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, the computer device includes a processor A01, a network interface A02, memory (not shown), and a database (not shown) connected via a system bus. The processor A01 provides computing and control capabilities. The memory includes internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 stored in the non-volatile storage medium A04. The network interface A02 is used for communication with external terminals via a network connection. When executed by the processor A01, the computer program B02 implements an irrigation decision-making method for crop areas.

[0042] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0043] This application provides a computer (electronic) device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements any of the above irrigation decision-making methods for crop areas.

[0044] This application also provides a computer program product that, when executed on a data processing device, is adapted to execute a program that initializes steps of an irrigation decision-making method for a crop area.

[0045] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0050] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0051] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0052] It should also be noted that the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0053] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for irrigation decision for a crop area, characterized in that, The irrigation decision-making method comprises: obtaining terrain information of the crop area based on unmanned aerial vehicle image data, and dividing the crop area into multiple terrain sub-zones according to the terrain information; installing a water level measuring device in a target terrain sub-zone, and obtaining water level data of the target terrain sub-zone based on the water level measuring device and water level data of other terrain sub-zones under the condition that the water level of the crop area remains unchanged within a preset time period, so as to determine water level differences between different terrain sub-zones; obtaining real-time water level data of the target terrain sub-zone through the water level measuring device during crop growth, and calculating water level data of other terrain sub-zones according to the real-time water level data and the water level differences; determining an irrigation scheme for each terrain sub-zone according to a current growth stage of the crop and the water level data of each terrain sub-zone.

2. The irrigation decision method of claim 1, wherein, The determination of the irrigation scheme for each terrain sub-zone according to the current growth stage of the crop and the water level data of each terrain sub-zone comprises: obtaining a standard water level range corresponding to the current growth stage of the crop; determining a total area proportion of terrain sub-zones within the standard water level range based on the water level data of each terrain sub-zone; generating an irrigation instruction based on the total area proportion and the real-time water level data of the target terrain sub-zone.

3. The irrigation decision method of claim 2, wherein, The generation of the irrigation instruction based on the total area proportion and the real-time water level data of the target terrain sub-zone comprises: generating a water layer maintaining instruction to control the irrigation equipment to standby in the case that the total area proportion is higher than a first threshold value and the real-time water level data of the target terrain sub-zone is greater than zero; generating a start irrigation instruction to control the irrigation equipment to irrigate the crop area in the case that the total area proportion is lower than the first threshold value and / or the real-time water level data of the target terrain sub-zone is less than or equal to zero.

4. The irrigation decision method of claim 2, wherein, After the start irrigation instruction is generated, the irrigation decision-making method further comprises: controlling the irrigation equipment to irrigate until a stop condition is met, the stop condition being that the total area proportion of terrain sub-zones within the standard water level range is higher than a second threshold value and the real-time water level data of the target terrain sub-zone is lower than a preset highest water level standard.

5. The irrigation decision method of claim 2, wherein, The irrigation decision-making method further comprises: obtaining predicted meteorological data; dynamically adjusting the standard water level range and the irrigation control instruction according to the predicted meteorological data.

6. The irrigation decision method of claim 5, wherein, The dynamic adjustment of the standard water level range or the irrigation control instruction according to the predicted meteorological data comprises: adjusting the standard water level range as a whole in the case that the predicted meteorological data indicates that high-temperature weather meeting a first condition will occur within a preset time period in the future; delaying or reducing irrigation amount in the case that the predicted meteorological data indicates that rainfall weather meeting a second condition will occur within a preset time period in the future.

7. The irrigation decision method of claim 1, wherein, The division of the crop area into multiple terrain sub-zones according to the terrain information comprises: dividing the crop area into at least a high terrain sub-zone, a medium terrain sub-zone and a low terrain sub-zone according to terrain height, wherein the high terrain sub-zone is the target terrain sub-zone.

8. The method of claim 1, wherein, The collection of water level data of other terrain sub-zones comprises: For each other topographic section, a plurality of measuring points are selected within the section for water level measurement, and an average of the water levels of the plurality of measuring points is taken as the water level data of the topographic section.

9. An irrigation decision device for a crop area, characterized by Comprising: a memory configured to store instructions; and a processor configured to call the instructions from the memory and enable the irrigation decision-making method for a crop area according to any one of claims 1 to 8 when the instructions are executed.

10. A machine-readable storage medium, characterized in that, The machine readable storage medium has stored instructions for causing a machine to perform the irrigation decision-making method for a crop area according to any one of claims 1 to 8.

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