A variable irrigation decision system

Abstract

This invention relates to the field of agricultural automation and intelligent irrigation technology, and particularly to a variable irrigation decision system, comprising: a meteorological data acquisition module for acquiring daily meteorological data of a designated farmland within a first future time period starting from the current time; a soil monitoring module for real-time monitoring of soil data of the designated farmland; a data processing module for acquiring, based on the daily meteorological data and the current soil data of the designated farmland within the first future time period, the current soil moisture content, daily evapotranspiration, and daily soil drainage within the first future time period of the designated farmland, and further acquiring the soil moisture content of the designated farmland after a first preset time period; and a control module for determining the current irrigation demand of the designated farmland and issuing a first irrigation instruction to the irrigation device corresponding to the designated farmland; the first irrigation instruction is an instruction to irrigate the designated farmland according to the current irrigation demand of the designated farmland.

Description

Technical Field

[0001] This invention relates to the field of agricultural automation and intelligent irrigation technology, and in particular to a variable irrigation decision system. Background Technology

[0002] With the development of agricultural production, the precision and intelligence of irrigation technology have gradually become key factors in improving water resource utilization efficiency and ensuring normal crop growth. Traditional irrigation methods often rely on human experience or fixed time intervals, failing to dynamically adjust according to the actual water needs of farmland, leading to water waste and uneven crop growth. Therefore, how to accurately calculate and adjust irrigation volume based on meteorological data, soil data, and crop needs has become a research focus in modern agricultural irrigation management.

[0003] Currently, many irrigation decision-making systems still suffer from the following problems: lack of accurate identification and regulation of different crop growth stages; neglect of the comprehensive influence of multiple variables such as meteorological factors and soil moisture; lack of intelligent prediction based on real-time data, resulting in low irrigation efficiency; and relatively simple system calculation and control mechanisms, failing to effectively achieve automated and precise irrigation. Summary of the Invention

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a variable irrigation decision system.

[0005] To achieve the above objectives, the main technical solutions adopted by the present invention include:

[0006] This invention provides a variable irrigation decision system, comprising:

[0007] The meteorological data acquisition module is used to acquire daily meteorological data for a specified farmland within the first time period from the present.

[0008] The soil monitoring module is used to monitor soil data of a designated farmland in real time.

[0009] The data processing module is used to obtain the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland based on the daily meteorological data and the current soil data of the designated farmland in the first future time period. It also obtains the soil moisture content of the designated farmland after the first preset time period based on the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland.

[0010] The control module is used to determine the irrigation demand of the designated farmland at the current moment based on the soil moisture content after a first preset time period, a preset soil moisture content threshold, and the predicted rainfall in a second preset time period after the current moment, and to issue a first irrigation command to the irrigation device corresponding to the designated farmland.

[0011] The first irrigation instruction is an instruction to irrigate the designated farmland according to the irrigation demand of the designated farmland at the current moment.

[0012] Preferably,

[0013] The data processing module, based on daily meteorological data and current soil data for the designated farmland within the first future time period, obtains the current soil moisture content, daily evapotranspiration, and daily soil drainage for the designated farmland within the first future time period. Specifically, this includes:

[0014] The data processing module obtains the daily evapotranspiration of the designated farmland within the first future time period based on the daily meteorological data of the designated farmland from the present.

[0015] The data processing module obtains the daily soil drainage volume of the specified farmland within the first future time period based on the daily meteorological data and the current soil data of the specified farmland.

[0016] Preferably,

[0017] The meteorological data includes: temperature, humidity, wind speed, solar radiation, and precipitation;

[0018] The soil data includes soil moisture content.

[0019] Preferably,

[0020] The data processing module obtains the daily evapotranspiration of the specified farmland within the first future time period, based on the daily meteorological data of the specified farmland from the current time. Specifically, this includes:

[0021] The data processing module uses the meteorological data of the specified farmland from the current day to the tth day in the future to obtain the evapotranspiration of the specified farmland on the tth day in the future using formula (1);

[0022] The formula (1) is:

[0023] ET(t)=α T ·f T (T(t))+α H ·f H (H(t))+α V ·f V (V(t))+α θ·f θ (θ(t))+α C ·f C (C);

[0024] α T For temperature weighting coefficient; f T (T(t)) is the temperature function; α H f is the humidity weighting factor; H (H(t)) is the humidity function; α V f is the wind speed weighting coefficient. V (V(t)) is the wind speed function; α θ f is the soil moisture content weighting coefficient; θ (θ(t)) is a function of soil moisture content; α C f is the crop weighting coefficient for the specified farmland; C (C) is the crop coefficient function; ET(t) is the evapotranspiration of the specified farmland on the t-th day in the future.

[0025] Preferably,

[0026] Temperature function f T (T(t)) is:

[0027] f T (T(t)) = T(t), or, f T (T(t))=T(t) 2 ;

[0028] Where T(t) is the average temperature of the specified farmland on the t-th day in the future;

[0029] Humidity function f H (H(t)) is:

[0030] f H (H(t))=(100-H(t)), or, f H (H(t))=ln(100-H(t));

[0031] Where H(t) is the average humidity of the specified farmland on the t-th day in the future;

[0032] Wind speed function f V (V(t)) is:

[0033] f V (V(t)) = V(t), or, f V (V(t))=V(t) 2 ;

[0034] Where V(t) is the average wind speed of the specified farmland on the t-th day in the future;

[0035] Soil moisture content function f θ (θ(t)) is:

[0036] f θ (θ(t)) = θ(t), or, f θ (θ(t)=lnθ(t);

[0037] Wherein, θ(t) is the average soil moisture content of the specified farmland on the t-th day in the future;

[0038] Crop coefficient function f C (C) is:

[0039] f C (C) = C, or, f C (C)=C 2 ;

[0040] Where C is the influence factor of the current growth stage of the crop in the specified farmland on evapotranspiration.

[0041] Preferably,

[0042] Among them, the temperature weighting coefficient α T It is calculated using formula (2) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0043] Formula (2) is as follows:

[0044]

[0045] in, The average temperature of the specified farmland over the historical period starting from the current date;

[0046] a 1x The average temperature of the specified farmland on day x within the historical time period starting from the current date;

[0047] The average of all first evapotranspirations over the historical period starting from the current time for the specified farmland;

[0048] Z x The value of the first evapotranspiration on day x within the historical time period starting from the current date for the specified farmland;

[0049] X represents the total number of days in the historical period;

[0050] The first evapotranspiration of the designated farmland on day x within the current historical time period is calculated using the Penman-Monteith formula based on the meteorological data of the designated farmland on day x within the current historical time period.

[0051] Humidity weighting coefficient α H It is calculated using formula (3) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0052] Formula (3) is as follows:

[0053]

[0054] in, The average humidity of the specified farmland over a historical period starting from the current date;

[0055] a 2x The average humidity of the specified farmland on day x within a historical time period starting from the current date;

[0056] Wind speed weighting coefficient α V It is calculated using formula (4) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0057] Formula (4) is as follows:

[0058]

[0059] in, The average wind speed over the specified farmland over the current historical period;

[0060] a 3x The average wind speed of the specified farmland on day x within the historical time period starting from the current date;

[0061] Soil moisture content weighting coefficient α θ It is calculated using formula (5) by using soil data for each day of the specified farmland within the current historical time period and the corresponding first evapotranspiration.

[0062] Formula (5) is as follows:

[0063]

[0064] in, The average soil moisture content of the specified farmland over a historical period starting from the present;

[0065] a 4x The average soil moisture content of the specified farmland on day x within a historical time period starting from the current date;

[0066] When the current growth stage of the crop in the specified farmland is the germination stage, then α C The range is 0.4-0.6; when the current growth stage of the crop in the specified farmland is the vegetative growth stage, then αC The range is 0.7-0.85; when the current growth stage of the crop in the specified farmland is the flowering stage, then α C The range is 0.85-0.9; when the current growth stage of the crop in the specified farmland is the grain-filling stage, then α C The range is 0.8-0.95; when the current growth stage of the crop in the specified farmland is maturity, then α C The range is 0.5-0.7.

[0067] Preferably,

[0068] If the current growth stage of the crop in the designated farmland is the germination stage, then C = 0.4;

[0069] If the current growth stage of the crop in the designated farmland is the vegetative growth stage, then C = 0.8;

[0070] If the current growth stage of the crop in the designated farmland is the flowering stage, then C = 1.1;

[0071] If the current growth stage of the crop in the designated farmland is the grain-filling stage, then C = 0.9;

[0072] If the current growth stage of the crop in the designated farmland is the maturity stage, then C = 0.6.

[0073] Preferably,

[0074] The data processing module, based on daily meteorological data and current soil data for a specified farmland within the first future time period, obtains the daily soil drainage volume for that specified farmland within the first future time period. Specifically, this includes:

[0075] Formula (6) is used for iterative calculation to obtain the average daily soil moisture content of the specified farmland in the first time period of the future.

[0076] The formula (6) is:

[0077] θ(t+1)=θ(t)-ET(t)-k(θ(t)-θ max );

[0078] Where, θ max Indicates the field water holding capacity corresponding to the specified farmland;

[0079] θ(t+1) is the average soil moisture content of the specified farmland on the next t+1 days.

[0080] k is a pre-obtained empirical constant;

[0081] Based on the average daily soil moisture content of the designated farmland in the current future first time period and the field water holding capacity of the designated farmland, the soil drainage volume of the designated farmland on day t in the current future first time period is obtained by formula (7).

[0082] The formula (7) is:

[0083] D t =k(θ(t)-θ max );

[0084] D t This represents the soil drainage volume of the specified farmland on the t-th day from the current time.

[0085] Preferably,

[0086] The soil moisture content of the designated farmland after the first preset time period is obtained based on the current soil moisture content, daily evapotranspiration during the first future time period, and daily soil drainage during the first future time period. Specifically, this includes:

[0087] Based on the current soil moisture content of the designated farmland, the daily evapotranspiration during the first time period in the future, and the daily soil drainage during the first time period in the future, the soil moisture content of the designated farmland after the first preset time period is obtained using formula (8).

[0088] The formula (8) is:

[0089] θ N =θ(0)-(ET(1)+D1)-…-(ET(t)+D t )…-(ET(N)+D N );

[0090] θ(0) represents the current soil moisture content of the specified farmland;

[0091] θ N This refers to the soil moisture content of the specified farmland on the Nth day from the current time.

[0092] N represents the total number of days in the first time period.

[0093] Preferably,

[0094] The control module, based on the soil moisture content of the designated farmland after a first preset time period, a pre-set soil moisture content threshold, and the predicted rainfall within a second preset time period after the current moment, determines the irrigation demand of the designated farmland at the current moment, specifically including:

[0095] The first moisture content value is calculated using formula (9) based on the soil moisture content after the first preset time period of the designated farmland and the predicted rainfall during the second preset time period after the current time.

[0096] Formula (9) is as follows:

[0097] W = θ N +β·P;

[0098] W represents the initial moisture content value;

[0099] P represents the predicted rainfall during the second preset time period after the current moment;

[0100] β is the pre-set influence coefficient of rainfall;

[0101] Determine whether the preset soil moisture content threshold is greater than the first moisture content value. If it is greater, then calculate the irrigation demand of the specified farmland at the current moment using formula (10) based on the preset soil moisture content threshold and the first moisture content value.

[0102] Formula (10) is as follows:

[0103] M=γ·(θ a -W);

[0104] M represents the irrigation demand of the specified farmland at the current moment;

[0105] γ is a pre-set irrigation adjustment coefficient;

[0106] θ a The preset soil moisture content threshold.

[0107] The beneficial effects of this invention are as follows: The variable irrigation decision system of this invention, by integrating meteorological data, soil data, and crop growth data, can automatically adjust the irrigation amount according to the real-time status of farmland, ensuring that crops receive optimal water conditions. Specifically, it has the following beneficial effects:

[0108] The present invention provides a variable irrigation decision system that, based on various factors such as soil moisture content, evapotranspiration, and soil drainage of a specified farmland, can accurately predict future changes in soil moisture content through mathematical models and formulas, thereby formulating reasonable irrigation requirements for the farmland.

[0109] This invention provides a variable irrigation decision system that dynamically adjusts crop coefficients based on crop growth stages (such as budding, flowering, and maturity) to ensure precise adjustments to irrigation amounts at different growth stages, meeting specific crop growth needs. Through automated control of the irrigation device, combined with real-time weather forecasts and soil data, the system can automatically determine irrigation requirements and issue irrigation commands, reducing human intervention and improving efficiency and accuracy.

[0110] The present invention provides a variable irrigation decision system that makes short-term predictions based on future meteorological data, enabling early irrigation planning and reasonable scheduling of irrigation time and amount, thereby further reducing unnecessary water loss.

[0111] In summary, the variable irrigation decision system provided by this invention, by integrating multi-dimensional data sources and advanced algorithms, effectively improves the accuracy and intelligence of agricultural irrigation, solves many problems existing in traditional irrigation methods, and has significant water-saving and crop yield-increasing effects, which is of great significance to modern agricultural production. Attached Figure Description

[0112] Figure 1 This is a schematic diagram of a variable irrigation decision system according to the present invention;

[0113] Figure 2 This is a schematic diagram showing the connection between a variable irrigation decision system and an irrigation device according to the present invention. Detailed Implementation

[0114] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0115] To better understand the above technical solutions, exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that the present invention can be understood more clearly and thoroughly, and that the scope of the present invention can be fully conveyed to those skilled in the art.

[0116] Example 1

[0117] See Figure 1 This embodiment provides a variable irrigation decision system, including:

[0118] The meteorological data acquisition module is used to acquire daily meteorological data for a specified farmland within the first time period from the present.

[0119] The soil monitoring module is used to monitor soil data of a designated farmland in real time.

[0120] The data processing module is used to obtain the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland based on the daily meteorological data and the current soil data of the designated farmland in the first future time period. It also obtains the soil moisture content of the designated farmland after the first preset time period based on the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland.

[0121] In this embodiment, the data processing module obtains the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland based on daily meteorological data and current soil data for the first future time period starting from the current time of the designated farmland. Specifically, this includes:

[0122] The data processing module obtains the daily evapotranspiration of the designated farmland within the first future time period based on the daily meteorological data of the designated farmland from the present.

[0123] The data processing module obtains the daily soil drainage volume of the specified farmland within the first future time period based on the daily meteorological data and the current soil data of the specified farmland.

[0124] The control module is used to determine the irrigation demand of the designated farmland at the current moment based on the soil moisture content after a first preset time period, a preset soil moisture content threshold, and the predicted rainfall during a second preset time period after the current moment, and to issue a first irrigation command to the irrigation device corresponding to the designated farmland. (See [link to relevant documentation]). Figure 2 ;

[0125] The first irrigation instruction is an instruction to irrigate the designated farmland according to the irrigation demand of the designated farmland at the current moment.

[0126] In this embodiment, the variable irrigation decision system makes intelligent decisions based on real-time meteorological data, soil data, and crop requirements, enabling precise calculation of irrigation needs. This precise control of irrigation volume avoids the over- or under-irrigation phenomena common in traditional irrigation methods, thereby conserving water resources and improving water use efficiency.

[0127] For example, during dry seasons, farmland may require more irrigation water. However, if the weather forecast indicates abundant rainfall in the coming days, the system will automatically adjust irrigation needs based on the predicted rainfall, avoiding over-irrigation and saving a significant amount of water resources.

[0128] The system monitors soil moisture and weather conditions in real time through soil monitoring and meteorological data acquisition modules, ensuring that the monitoring data on the farmland environment is always up-to-date. Furthermore, based on this real-time data, the system can dynamically adjust irrigation decisions, avoiding irrigation errors caused by delayed data. If the temperature suddenly rises during a certain period, the system can quickly calculate changes in evapotranspiration based on data such as temperature, humidity, and wind speed, thereby increasing irrigation in a timely manner to prevent crops from being affected by insufficient water.

[0129] In this embodiment, the variable irrigation decision system combines historical meteorological data and soil moisture content with predicted meteorological data (such as precipitation) for a preset future time period to predict irrigation demand in advance and formulate irrigation plans accordingly. This intelligent prediction effectively avoids the high water consumption and costs associated with temporary irrigation. For example, in the lead-up to the rainy season, the system reduces irrigation volume based on precipitation forecasts, preventing unnecessary water waste. This pre-planning enables more scientific water management in farmland.

[0130] Precision irrigation systems ensure crops receive optimal water supply during their growth, avoiding the negative effects of over-irrigation or under-irrigation. This not only helps increase crop yield but also improves crop quality. Irrigation needs vary at different growth stages. For example, crops require more water during flowering, while irrigation can be reduced during maturity. In this embodiment, the variable irrigation decision system automatically adjusts the irrigation plan based on the crop's growth stage, ensuring that crops receive the necessary water at each stage, thereby improving both yield and quality.

[0131] In this embodiment, the meteorological data includes: temperature, humidity, wind speed, solar radiation, and precipitation; the soil data includes soil moisture content.

[0132] The data processing module obtains the daily evapotranspiration of the specified farmland within the first future time period, based on the daily meteorological data of the specified farmland from the current time. Specifically, this includes:

[0133] The data processing module uses the meteorological data of the specified farmland from the current day to the tth day in the future to obtain the evapotranspiration of the specified farmland on the tth day in the future using formula (1);

[0134] The formula (1) is:

[0135] ET(t)=α T ·f T (T(t))+α H ·f H (H(t))+α V ,·f V (V(t))+α θ ·f θ (θ(t))+α C ·f C (C);

[0136] α T For temperature weighting coefficient; f T (T(t)) is the temperature function; α H f is the humidity weighting factor; H (H(t)) is the humidity function; αV f is the wind speed weighting coefficient. V (V(t)) is the wind speed function; α θ f is the soil moisture content weighting coefficient; θ (θ(t)) is a function of soil moisture content; α C f is the crop weighting coefficient for the specified farmland; C (C) is the crop coefficient function; ET(t) is the evapotranspiration of the specified farmland on the t-th day in the future.

[0137] In this embodiment, the evapotranspiration in formula (1) is a function of multiple factors, including the temperature weighting coefficient α. T Temperature function f T (T(t)), humidity weighting coefficient α H Humidity function f H (H(t)), wind speed weighting coefficient α V Wind speed function f V (V(t)), soil moisture content weighting coefficient α θ Soil moisture content function f θ (θ(t)), the crop weighting coefficient α in the specified farmland C Crop coefficient function f C (C). These variables cover the main factors affecting evapotranspiration, such as: temperature affecting the evaporation rate, humidity reflecting the saturation of water vapor in the air, wind speed affecting evaporation and transpiration, soil moisture content limiting the source of evaporation, and crop coefficients combined with the specific growth needs of crops. Therefore, the variable irrigation decision system in this embodiment comprehensively considers meteorological, soil, and crop characteristics to avoid prediction bias caused by a single factor. It dynamically adapts to different climatic conditions and crop needs, improving the accuracy and universality of prediction results.

[0138] Temperature function f T (T(t)) is:

[0139] f T (T(t)) = T(t), or, f T (T(t))=T(t) 2 ;

[0140] Where T(t) is the average temperature of the specified farmland on the t-th day in the future;

[0141] Humidity function f H (H(t)) is:

[0142] f H (H(t))=(100-H(t)), or, f H (H(t))=ln(100-H(t));

[0143] Where H(t) is the average humidity of the specified farmland on the t-th day in the future;

[0144] Wind speed function f V (V(t)) is:

[0145] f V (V(t)) = V(t), or, f V (V(t))=V(t) 2 ;

[0146] Where V(t) is the average wind speed of the specified farmland on the t-th day in the future;

[0147] Soil moisture content function f θ (θ(t)) is:

[0148] f θ (θ(t)) = θ(t), or, f θ (θ(t))=lnθ(t);

[0149] Wherein, θ(t) is the average soil moisture content of the specified farmland on the t-th day in the future;

[0150] The crop coefficient function fC(C) is:

[0151] f C (C) = C, or, f C (C)=C 2 ;

[0152] Where C is the influence factor of the current growth stage of the crop in the specified farmland on evapotranspiration.

[0153] Among them, the temperature weighting coefficient α T It is calculated using formula (2) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0154] Formula (2) is as follows:

[0155]

[0156] in, The average temperature of the specified farmland over the historical period starting from the current date;

[0157] a 1x The average temperature of the specified farmland on day x within the historical time period starting from the current date;

[0158] The average of all first evapotranspirations over the historical period starting from the current time for the specified farmland;

[0159] Zx The value of the first evapotranspiration on day x within the historical time period starting from the current date for the specified farmland;

[0160] X represents the total number of days in the historical period;

[0161] The first evapotranspiration of the designated farmland on day x within the current historical time period is calculated using the Penman-Monteith formula based on the meteorological data of the designated farmland on day x within the current historical time period.

[0162] This embodiment incorporates the impact of air temperature on evapotranspiration, enabling more accurate prediction of crop water requirements and thus optimizing irrigation management. Considering temperature variations under different weather conditions makes the model more flexible and applicable to various climatic regions. Air temperature is a crucial factor influencing evapotranspiration, and its changes directly affect the rate of water evaporation. Quantifying this impact allows for better adjustment of irrigation strategies to meet crop needs.

[0163] Humidity weighting coefficient α H It is calculated using formula (3) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0164] Formula (3) is as follows:

[0165]

[0166] in, The average humidity of the specified farmland over a historical period starting from the current date;

[0167] a 2x The average humidity of the specified farmland on day x within a historical time period starting from the current date;

[0168] In this embodiment, humidity also significantly affects the evapotranspiration process, and adding a humidity weighting coefficient helps to further improve the accuracy of the model. This allows for adjustments to irrigation plans based on different humidity conditions, reducing water waste. There is a direct correlation between air temperature and evapotranspiration; evaporation slows down under high humidity conditions and accelerates under low humidity. Therefore, considering humidity factors can make irrigation decisions more scientific and rational.

[0169] Wind speed weighting coefficient α V It is calculated using formula (4) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration.

[0170] Formula (4) is as follows:

[0171]

[0172] in, The average wind speed over the specified farmland over the current historical period;

[0173] a 3x The average wind speed of the specified farmland on day x within the historical time period starting from the current date;

[0174] In this embodiment, wind speed affects airflow velocity, thereby altering the evaporation rate. By using a wind speed weighting coefficient, irrigation water volume can be better controlled, avoiding over- or under-irrigation. This helps reduce water loss due to improper irrigation and promotes sustainable development. Increased wind speed accelerates surface moisture evaporation, reducing soil moisture. Accurately assessing the effect of wind speed can help develop more reasonable irrigation schedules.

[0175] Soil moisture content weighting coefficient α θ It is calculated using formula (5) by using soil data for each day of the specified farmland within the current historical time period and the corresponding first evapotranspiration.

[0176] Formula (5) is as follows:

[0177]

[0178] in, The average soil moisture content of the specified farmland over a historical period starting from the present;

[0179] a 4x The average soil moisture content of the specified farmland on day x within a historical time period starting from the current date;

[0180] It directly reflects the soil's actual water-holding capacity, ensuring optimal results with each irrigation, avoiding both waste and water shortage. Maintaining suitable soil moisture levels is beneficial for root development, improving crop yield and quality. Soil moisture content is a key indicator determining the available water for plants. By monitoring and adjusting this parameter, optimal water supply can be achieved, supporting healthy crop growth.

[0181] When the current growth stage of the crop in the specified farmland is the germination stage, then α C The range is 0.4-0.6; when the current growth stage of the crop in the specified farmland is the vegetative growth stage, then α C The range is 0.7-0.85; when the current growth stage of the crop in the specified farmland is the flowering stage, then α C The range is 0.85-0.9; when the current growth stage of the crop in the specified farmland is the grain-filling stage, then α C The range is 0.8-0.95; when the current growth stage of the crop in the specified farmland is maturity, then α C The range is 0.5-0.7.

[0182] In this embodiment, the water requirements of crops at different growth stages vary significantly, and customized weighting coefficients can better meet the needs of each stage. Providing appropriate water supply during germination, vegetative growth, flowering, grain filling, and maturity helps promote crop growth and improve the quality and yield of the final product. The water requirements and response mechanisms differ at each stage of the crop growth cycle. For example, appropriately increasing water supply during the vegetative growth stage can promote rapid plant growth; while appropriately reducing water during the maturity stage helps fruit ripening. Therefore, dynamically adjusting irrigation strategies according to growth stages is essential.

[0183] If the current growth stage of the crop in the designated farmland is the germination stage, then C = 0.4;

[0184] If the current growth stage of the crop in the designated farmland is the vegetative growth stage, then C = 0.8;

[0185] If the current growth stage of the crop in the designated farmland is the flowering stage, then C = 1.1;

[0186] If the current growth stage of the crop in the designated farmland is the grain-filling stage, then C = 0.9;

[0187] If the current growth stage of the crop in the designated farmland is the maturity stage, then C = 0.6.

[0188] In this embodiment, the influence factor C on evapotranspiration of different growth stages of crops in farmland is specified, indicating that crops at different growth stages have different water requirements. For example, less water is needed during the germination stage to prevent root rot, while more water is needed during the vegetative growth stage to support rapid growth. Customized irrigation strategies can better meet these needs. Appropriate water supply helps crops achieve optimal growth conditions at various critical stages, thereby improving the final product quality and yield. The significant differences in water requirements of crops at different growth stages have been confirmed by agricultural scientific research. Therefore, adjusting irrigation strategies according to different growth stages is scientifically sound. Many modern agricultural practices have also demonstrated that precision irrigation management can significantly improve crop growth and economic benefits. Modern sensor technology and data analysis capabilities make it possible to monitor and respond to crop needs in real time, providing technical support for implementing such precision irrigation.

[0189] Germination stage (C=0.4): At this stage, the crop has a low water requirement, and excessive water may lead to root diseases. The low C value ensures that there is no overwatering, protecting the healthy growth of seedlings.

[0190] The vegetative growth period (C=0.8): This is a critical period for rapid crop growth. Appropriately increasing the water supply can promote plant development without wasting water resources.

[0191] Flowering stage (C=1.1): Flowering is a crucial stage of crop reproductive growth, and sufficient water is essential for pollen dispersal and successful pollination. A higher C value ensures adequate water supply and promotes high yields.

[0192] Grain-filling stage (C=0.9): During this stage, crops begin to accumulate dry matter. Adequate water helps fruits or seeds to fill out, but excessive water should be avoided to prevent excessive vegetative growth.

[0193] Maturity stage (C=0.6): As the crop approaches harvest, its water requirement gradually decreases. Lowering the C value helps the crop mature and reduces irrigation costs later on.

[0194] In this embodiment, the data processing module obtains the daily soil drainage volume of the designated farmland within the first future time period based on the daily meteorological data and the current soil data of the designated farmland. Specifically, this includes:

[0195] Formula (6) is used for iterative calculation to obtain the average daily soil moisture content of the specified farmland in the first time period of the future.

[0196] The formula (6) is:

[0197] θ(t+1)=θ(t)-ET(t)-k(θ(t)-θ max );

[0198] Where, θ max Indicates the field water holding capacity corresponding to the specified farmland;

[0199] θ(t+1) is the average soil moisture content of the specified farmland on the next t+1 days.

[0200] k is a pre-obtained empirical constant;

[0201] In this embodiment, formula (6) uses a substitution calculation to predict future soil moisture content by considering the current soil moisture content, evapotranspiration and the difference between the current soil moisture content and field capacity.

[0202] Based on the average daily soil moisture content of the designated farmland in the current future first time period and the field water holding capacity of the designated farmland, the soil drainage volume of the designated farmland on day t in the current future first time period is obtained by formula (7).

[0203] The formula (7) is:

[0204] D t=k(θ(t)-θ max );

[0205] D t This represents the soil drainage volume of the specified farmland on the t-th day from the current time.

[0206] In this embodiment, formula (7) predicts soil drainage by calculating the difference between the current soil moisture content and the field water holding capacity and multiplying it by an empirical constant k.

[0207] In this embodiment, the current soil moisture content, evapotranspiration, and the difference between the soil moisture content and field capacity are considered through iterative calculation using formula (6), ensuring that the predicted soil moisture content is more accurate. The difference between the current soil moisture content and field capacity is calculated using formula (7) and multiplied by an empirical constant k to predict soil drainage, thereby achieving precision irrigation.

[0208] In summary, these formulas enable more precise and efficient irrigation management by accurately predicting soil moisture content and drainage, which not only improves agricultural production efficiency but also promotes the development of resource-saving agriculture.

[0209] The soil moisture content of the designated farmland after the first preset time period is obtained based on the current soil moisture content, daily evapotranspiration during the first future time period, and daily soil drainage during the first future time period. Specifically, this includes:

[0210] Based on the current soil moisture content of the designated farmland, the daily evapotranspiration during the first time period in the future, and the daily soil drainage during the first time period in the future, the soil moisture content of the designated farmland after the first preset time period is obtained using formula (8).

[0211] The formula (8) is:

[0212] θ N =θ(0)-(ET(1)+D1)-…-(ET(t)+D t )…-(ET(N)+D N );

[0213] θ(0) represents the current soil moisture content of the specified farmland;

[0214] θ N This refers to the soil moisture content of the specified farmland on the Nth day from the current time.

[0215] N represents the total number of days in the first time period.

[0216] In this embodiment, the initial condition of formula (8) is θ(0), which represents the current soil moisture content. The daily evapotranspiration ET(t) and soil drainage D are also considered. tAll of these losses will be deducted from the current soil moisture content to reflect the actual water loss. Cumulative effect: By accumulating these water losses day by day, the soil moisture content on the Nth day in the future can be accurately predicted.

[0217] In this embodiment, by considering evapotranspiration and soil drainage, the method can more accurately predict future soil moisture content, avoiding errors caused by relying solely on current data. Daily updates of data (evapotranspiration and drainage) make the prediction model more flexible, enabling it to adapt promptly to changes in weather and crop growth. By predicting soil moisture content for the next N days, farmers can plan irrigation in advance and optimize water management throughout the entire growth cycle.

[0218] The control module, based on the soil moisture content of the designated farmland after a first preset time period, a pre-set soil moisture content threshold, and the predicted rainfall within a second preset time period after the current moment, determines the irrigation demand of the designated farmland at the current moment, specifically including:

[0219] The first moisture content value is calculated using formula (9) based on the soil moisture content after the first preset time period of the designated farmland and the predicted rainfall during the second preset time period after the current time.

[0220] Formula (9) is as follows:

[0221] W = θ N +β·P;

[0222] W represents the first moisture content value; P represents the predicted rainfall in the second preset time period after the current moment; β represents the preset influence coefficient of rainfall.

[0223] Determine whether the preset soil moisture content threshold is greater than the first moisture content value. If it is greater, then calculate the irrigation demand of the specified farmland at the current moment using formula (10) based on the preset soil moisture content threshold and the first moisture content value.

[0224] Formula (10) is as follows:

[0225] M=γ·(θ a -W);

[0226] M represents the irrigation demand of the specified farmland at the current moment; γ is a pre-set irrigation adjustment coefficient; θ a The preset soil moisture content threshold.

[0227] In this embodiment, since soil moisture content at different time periods has a significant impact on crop growth, these relationships have been confirmed by agricultural scientific research. Formulas (9) and (10) can be used to more accurately predict future soil moisture content and adjust irrigation amounts according to actual needs.

[0228] This embodiment of a variable irrigation decision system can accurately predict future changes in soil moisture content based on various factors such as soil moisture content, evapotranspiration, and soil drainage of a specified farmland through mathematical models and formulas, thereby formulating reasonable irrigation requirements for the farmland.

[0229] This embodiment presents a variable irrigation decision system that dynamically adjusts crop coefficients based on crop growth stages (such as budding, flowering, and maturity) to ensure precise adjustments to irrigation amounts at different growth stages, meeting specific crop growth needs. Through automated control of the irrigation device, combined with real-time weather forecasts and soil data, the system can automatically determine irrigation requirements and issue irrigation commands, reducing human intervention and improving efficiency and accuracy.

[0230] Example 2

[0231] This embodiment also provides a variable irrigation decision method, which is executed by the variable irrigation decision in Embodiment 1.

[0232] In the description of this invention, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0233] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0234] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first and second features are in direct contact, or that they are in indirect contact through an intermediate medium. Furthermore, "above," "over," or "on top" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply indicates that the first feature is at a higher horizontal level than the second feature. "Below," "below," or "beneath" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply indicates that the first feature is at a lower horizontal level than the second feature.

[0235] In the description of this specification, the terms "one embodiment," "some embodiments," "embodiment," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0236] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make modifications, alterations, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A variable irrigation decision system, characterized in that, include: The meteorological data acquisition module is used to acquire daily meteorological data for a specified farmland within the first time period from the present. The soil monitoring module is used to monitor soil data of a designated farmland in real time. The data processing module is used to obtain the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland based on the daily meteorological data and the current soil data of the designated farmland in the first future time period. It also obtains the soil moisture content of the designated farmland after the first preset time period based on the current soil moisture content, daily evapotranspiration, and daily soil drainage of the designated farmland. The control module is used to determine the irrigation demand of the designated farmland at the current moment based on the soil moisture content after a first preset time period, a preset soil moisture content threshold, and the predicted rainfall in a second preset time period after the current moment, and to issue a first irrigation command to the irrigation device corresponding to the designated farmland. The first irrigation instruction is an instruction to irrigate the designated farmland according to the irrigation demand of the designated farmland at the current moment; The data processing module, based on daily meteorological data and current soil data for a specified farmland within a given future time period, obtains the current soil moisture content, daily evapotranspiration, and daily soil drainage for that same time period. Specifically, the data processing module obtains the daily evapotranspiration for the specified farmland within the given future time period based on daily meteorological data. The data processing module uses meteorological data from the current day to the next day t for the specified farmland to obtain the evapotranspiration of the specified farmland on the next day t using formula (1); the formula (1) is: ； Temperature weighting coefficient; It is a function of temperature; Humidity weighting coefficient; It is a function of humidity; This is the wind speed weighting coefficient; It is a function of wind speed; This is the soil moisture content weighting coefficient; It is a function of soil moisture content; To specify the crop weighting coefficients in the farmland; This is a crop coefficient function; To specify the evapotranspiration of farmland on the t-th day in the future; The data processing module obtains the daily soil drainage volume of the specified farmland within the first future time period based on the daily meteorological data and the current soil data of the specified farmland.

2. The variable irrigation decision system according to claim 1, characterized in that, The meteorological data includes: temperature, humidity, wind speed, solar radiation, and precipitation; The soil data includes soil moisture content.

3. The variable irrigation decision system according to claim 2, characterized in that, Temperature function for: ,or, ; in, The average temperature of the specified farmland on the t-th day in the future; Humidity function for: ,or, ; in, The average humidity of the specified farmland on the t-th day in the future; Wind speed function for: ,or, ; in, The average wind speed over the specified farmland on the t-th day in the future; Soil moisture content function for: ,or, ; in, The average soil moisture content of the specified farmland on the t-th day in the future; Crop coefficient function for: ,or, ; Where C is the influence factor of the current growth stage of the crop in the specified farmland on evapotranspiration.

4. The variable irrigation decision system according to claim 3, characterized in that, in, Temperature weighting coefficient It is calculated using formula (2) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration. Formula (2) is as follows: ； in, The average temperature of the specified farmland over the historical period starting from the current date; The average temperature of the specified farmland on day x within the historical time period starting from the current date; The average of all first evapotranspirations over the historical period starting from the current time for the specified farmland; The value of the first evapotranspiration on day x within the historical time period starting from the current date for the specified farmland; X represents the total number of days in the historical period; The first evapotranspiration of the designated farmland on day x within the current historical time period is calculated using the Penman-Monteith formula based on the meteorological data of the designated farmland on day x within the current historical time period. Humidity weighting factor It is calculated using formula (3) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration. Formula (3) is as follows: ； in, The average humidity of the specified farmland over a historical period starting from the current date; The average humidity of the specified farmland on day x within a historical time period starting from the current date; Wind speed weighting coefficient It is calculated using formula (4) by taking the meteorological data of each day in the historical time period of the specified farmland and the corresponding first evapotranspiration. Formula (4) is as follows: ； in, The average wind speed over the specified farmland over the current historical period; The average wind speed of the specified farmland on day x within the historical time period starting from the current date; Soil moisture content weighting coefficient It is calculated using formula (5) by using soil data for each day of the historical period of the specified farmland and the corresponding first evapotranspiration. Formula (5) is as follows: ； in, The average soil moisture content of the specified farmland over a historical period starting from the present; The average soil moisture content of the specified farmland on day x within a historical time period starting from the current date; When the current growth stage of the crop in the designated farmland is the germination stage, then The range is 0.4-0.6; when the current growth stage of the crop in the specified farmland is the vegetative growth stage, then... The range is 0.7-0.85; when the current growth stage of the crop in the specified farmland is the flowering stage, then... The range is 0.85-0.9; when the current growth stage of the crop in the specified farmland is the grain-filling stage, then... The range is 0.8-0.95; when the current growth stage of the crop in the specified farmland is maturity, then... The range is 0.5-0.

7.

5. The variable irrigation decision system according to claim 4, characterized in that, If the current growth stage of the crop in the designated farmland is the germination stage, then C = 0.4; If the current growth stage of the crop in the designated farmland is the vegetative growth stage, then C = 0.8; If the current growth stage of the crop in the designated farmland is the flowering stage, then C=1.1; If the current growth stage of the crop in the designated farmland is the grain-filling stage, then C = 0.9; If the current growth stage of the crop in the designated farmland is the maturity stage, then C = 0.

6.

6. The variable irrigation decision system according to claim 5, characterized in that, The data processing module, based on daily meteorological data and current soil data for a specified farmland within the first future time period, obtains the daily soil drainage volume for that specified farmland within the first future time period. Specifically, this includes: Formula (6) is used for iterative calculation to obtain the average daily soil moisture content of the specified farmland in the first time period of the future. The formula (6) is: ； in, Indicates the field water holding capacity corresponding to the specified farmland; The average soil moisture content of the specified farmland on the next day t+1. k is a pre-obtained empirical constant; Based on the average daily soil moisture content of the designated farmland in the first time period of the future and the field water holding capacity of the designated farmland, the soil drainage volume of the designated farmland on day t in the first time period of the future is obtained by formula (7). The formula (7) is: ； This represents the soil drainage volume of the specified farmland on the t-th day from the current time.

7. The variable irrigation decision system according to claim 6, characterized in that, The soil moisture content of the designated farmland after the first preset time period is obtained based on the current soil moisture content, daily evapotranspiration during the first future time period, and daily soil drainage during the first future time period. Specifically, this includes: Based on the current soil moisture content of the designated farmland, the daily evapotranspiration during the first time period in the future, and the daily soil drainage during the first time period in the future, the soil moisture content of the designated farmland after the first preset time period is obtained using formula (8). The formula (8) is: ； To specify the current soil moisture content of the farmland; This refers to the soil moisture content of a specified farmland on the Nth day from the current time. N represents the total number of days in the first time period.

8. The variable irrigation decision system according to claim 7, characterized in that, The control module, based on the soil moisture content of the designated farmland after a first preset time period, a pre-set soil moisture content threshold, and the predicted rainfall within a second preset time period after the current moment, determines the irrigation demand of the designated farmland at the current moment, specifically including: The first moisture content value is calculated using formula (9) based on the soil moisture content after the first preset time period of the designated farmland and the predicted rainfall during the second preset time period after the current time. Formula (9) is as follows: ； W represents the initial moisture content value; P represents the predicted rainfall during the second preset time period after the current moment; The influence coefficient of the pre-set rainfall amount; Determine whether the preset soil moisture content threshold is greater than the first moisture content value. If it is greater, then calculate the irrigation demand of the specified farmland at the current moment using formula (10) based on the preset soil moisture content threshold and the first moisture content value. Wherein, formula (10) is: ； M represents the irrigation demand of the specified farmland at the current moment; The pre-set irrigation adjustment coefficient; The preset soil moisture content threshold.

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