An intelligent method for optimizing water and fertilizer supply for corn

Abstract

The present invention relates to the field of supply data analysis technology, and specifically to an intelligent corn water and fertilizer supply optimization method, comprising the following steps: using a sensor network to monitor soil moisture, soil nutrient level and crop physiological state, the physiological state including chlorophyll content and transpiration rate; building a dynamic water and fertilizer demand model based on monitoring data of a cloud platform; monitoring the current crop physiological state and combining the dynamic water and fertilizer demand model to design a collaborative supply strategy to determine the timing and supply amount of irrigation and fertilization; establishing an integrated intelligent supply unit to automatically control irrigation and fertilization equipment, and intelligently adjust water and fertilizer supply according to the collaborative supply strategy designed by S3. The present invention, by establishing demand functions for each stage, can flexibly adjust the supply strategy to cope with special needs at different growth stages, which not only optimizes resource utilization, but also provides effective support for sustainable agricultural development.

Description

Technical Field

[0001] The present invention relates to the technical field of supply data analysis, and in particular to an intelligent corn water and fertilizer supply optimization method. Background Art

[0002] In agricultural production, water and fertilizer are key factors affecting crop growth and yield. However, traditional water and fertilizer management often relies on experience and fixed application standards, lacks specificity and flexibility, resulting in insufficient or excessive water and fertilizer supply, seriously affecting crop growth, health and yield.

[0003] In recent years, the development of smart agriculture and Internet of Things technology has provided new solutions for agricultural management. Through sensor networks and data analysis, real-time monitoring of soil moisture, nutrient levels, and crop physiological status can be achieved. However, existing water and fertilizer management systems often fail to make full use of these data and lack dynamic adjustment capabilities, resulting in insufficient accuracy in water and fertilizer supply.

[0004] In addition, crops at different growth stages have varying demands for water and nutrients, but existing technologies often fail to fully consider this when supplying water and fertilizer. Traditional methods are difficult to effectively respond to changes in the physiological state of crops and are unable to provide sufficient water and nutrients at critical moments, further affecting the sustainability of agricultural production. Summary of the invention

[0005] The invention provides an intelligent corn water and fertilizer supply optimization method.

[0006] An intelligent corn water and fertilizer supply optimization method comprises the following steps:

[0007] S1, soil and crop status monitoring: using a sensor network to monitor soil moisture, soil nutrient levels and crop physiological status, including chlorophyll content and transpiration rate, and upload the monitoring data to the cloud platform;

[0008] S2, establish a dynamic water and fertilizer demand model: based on the monitoring data of the cloud platform, analyze the water and nutrient requirements at different growth stages and build a dynamic water and fertilizer demand model;

[0009] S3, collaborative supply strategy design: monitor the current crop physiological state and combine it with the dynamic water and fertilizer demand model to design a collaborative supply strategy to determine the timing and supply of irrigation and fertilization;

[0010] S4, intelligent supply implementation: Establish an integrated intelligent supply unit to automatically control irrigation and fertilization equipment, and intelligently adjust water and fertilizer supply according to the collaborative supply strategy designed in S3.

[0011] Optionally, the sensor network in S1 is based on Internet of Things technology, including a soil moisture sensor, an electrochemical sensor, an optical sensor and a transpiration rate meter;

[0012] The soil moisture sensor obtains the moisture content by measuring the capacitance change of the soil;

[0013] The electrochemical sensor detects changes in the conductivity of soil nutrients and provides soil nutrient level data;

[0014] The optical sensor monitors the chlorophyll content and calculates the chlorophyll concentration using the absorption characteristics of light of corresponding wavelengths. At the same time, a transpiration rate meter is used to measure the transpiration rate of leaves;

[0015] All monitoring data is uploaded to the cloud platform in real time via wireless network to ensure real-time update of information and provide accurate growth environment information for subsequent data analysis and decision-making.

[0016] The chlorophyll concentration C can be calculated by the following formula: ,in, is the chlorophyll concentration (in ), yes The reflectivity at the wavelength, yes The reflectivity at the wavelength, is the calibration factor, which is adjusted according to the specific sensor and experimental conditions;

[0017] Wavelength selection: 680nm and 700nm wavelengths correspond to the absorption peak and non-absorption peak of chlorophyll a respectively.

[0018] Reflectance measurement: The light reflectance of plant leaves at these two wavelengths is measured by an optical sensor.

[0019] Optionally, after the monitoring data in S1 is collected, it also includes growth stage division, including germination stage, jointing stage, heading stage, and filling stage. The monitored soil moisture, soil nutrient level and crop physiological state data are divided according to the germination stage, jointing stage, heading stage, and filling stage to obtain a divided data set.

[0020] Optionally, before constructing the dynamic water and fertilizer demand model in S2, it also includes performing a correlation analysis on soil moisture, soil nutrient level and crop physiological state to identify the impact of soil moisture and nutrient level on crop physiological state, and judging the dependence and influence degree between soil moisture, soil nutrient level and crop physiological state by analyzing the correlation coefficient obtained.

[0021] Optionally, the analysis of water and nutrient requirements at different growth stages in S2 includes stage-by-stage data analysis and linear regression modeling: based on the results of the correlation analysis, a linear regression model is established for each growth stage to reversely infer water and nutrient requirements, i.e., water and nutrient requirements, i.e., water and fertilizer requirements, and set the desired crop physiological state. (such as chlorophyll content or transpiration rate), and soil moisture can be inferred through a linear regression model. and soil nutrient levels :

[0022] ;

[0023] in, It is the physiological state of the crop at the corresponding growth stage, including chlorophyll content, transpiration rate, is the intercept of the regression model, which represents the crop physiological state value when the soil moisture and soil nutrient levels are zero. is soil moisture The regression coefficient of represents the influence of soil moisture on crop physiological state. is the soil nutrient level The regression coefficient of represents the influence of soil nutrient level on crop physiological state. is the error term, which represents the influence of other factors that the model cannot explain;

[0024] In each growth stage, the results of correlation analysis were used to select and , and reverse calculation to determine the corresponding water and nutrient requirements.

[0025] Optionally, the construction of the dynamic water and fertilizer demand model is based on the reversed soil moisture and soil nutrient levels , to reflect the water and fertilizer requirements at different growth stages, the model is expressed as:

[0026] Germination stage: ;

[0027] Jointing stage: ;

[0028] Heading stage: ;

[0029] Grouting stage: ;

[0030] in, The water or nutrient requirements for each stage, , ,..., It is the coefficient of each stage obtained through regression analysis, which indicates the degree of influence of water and nutrients on the requirements of each growth stage.

[0031] Optionally, the S3 specifically includes:

[0032] S31, real-time monitoring of the crop physiological state at the current growth stage. If the crop physiological state at the current growth stage reaches the average chlorophyll content and transpiration rate of the current growth stage, the water and fertilizer supply strategy is not optimized. If the crop physiological state at the current growth stage is lower than the average chlorophyll content and transpiration rate of the current growth stage, the average level of the crop physiological state at the current growth stage is set to And through the linear regression model, the soil moisture W and soil nutrient level N that should be in the current growth stage are obtained;

[0033] S32, real-time monitoring of soil moisture at the current growth stage and soil nutrient levels , and compare it with the soil moisture W and soil nutrient level N obtained in the current growth stage to determine the irrigation and fertilization timing of the current growth stage. or , then irrigation or fertilization is triggered.

[0034] Optionally, after determining the irrigation and fertilization timing in S32, the method further includes:

[0035] Water requirement calculate: ,in, is the total water supply per irrigation, is the soil moisture required for the current growth stage. It is the soil moisture monitored in real time. is the area of ​​the irrigated area;

[0036] Nutrient requirements calculate: ,in, is the total nutrient supply per fertilization, is the soil nutrient level that should be present at the current growth stage. It is the real-time monitoring of soil nutrient levels. is the area of ​​the fertilized area.

[0037] Optionally, S3 also includes the transpiration rate and chlorophyll content combined with the crop's physiological state as additional basis to ensure irrigation and fertilization at critical times of crop demand. When the transpiration rate is high and the chlorophyll content is low, it means that the crop is growing rapidly and lacks nutrients, and timely fertilization is required to meet crop needs. The following conditions are met: If the transpiration rate exceeds a preset threshold and the chlorophyll content is lower than a preset threshold, the fertilization operation is triggered.

[0038] Beneficial effects of the present invention:

[0039] The present invention monitors soil moisture, nutrient levels and crop physiological status in real time through an integrated sensor network, builds a dynamic water and fertilizer demand model, ensures the accuracy of water and fertilizer supply, and can dynamically adjust irrigation and fertilization amounts based on real-time data, avoiding resource waste caused by human judgment in traditional methods. Through precise supply methods, crops can obtain the required water and nutrients at each growth stage, thereby improving crop yield and quality.

[0040] The present invention systematically analyzes the water and nutrient requirements at different growth stages, and uses correlation analysis and linear regression models to reversely infer the demand, so that the water and fertilizer supply at each growth stage is based on scientific data, thereby improving the accuracy of supply. At the same time, by establishing demand functions for each stage, the supply strategy can be flexibly adjusted to cope with the special needs of different growth stages. This not only optimizes resource utilization, but also provides effective support for the sustainable development of agriculture, and promotes the scientific and intelligent process of modern agriculture.

[0041] The present invention can respond to changes in the physiological state of crops in real time. When the transpiration rate exceeds a preset threshold and the chlorophyll content is lower than the standard, it automatically triggers fertilization and irrigation operations to ensure that the needs of crops at key growth stages are met in a timely manner. It can also effectively prevent growth stagnation and yield reduction caused by lack of water or fertilizer. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A schematic diagram of the optimization method flow of an embodiment of the present invention;

[0044] Figure 2 A schematic diagram of constructing a dynamic water and fertilizer demand model according to an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0046] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0047] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0048] like Figure 1-Figure 2 As shown, an intelligent method for optimizing water and fertilizer supply for corn comprises the following steps:

[0049] S1, soil and crop status monitoring: using a sensor network to monitor soil moisture, soil nutrient levels and crop physiological status, including chlorophyll content and transpiration rate, and upload the monitoring data to the cloud platform;

[0050] S2, establish a dynamic water and fertilizer demand model: based on the monitoring data of the cloud platform, analyze the water and nutrient requirements at different growth stages and build a dynamic water and fertilizer demand model;

[0051] S3, collaborative supply strategy design: monitor the current crop physiological state and combine it with the dynamic water and fertilizer demand model to design a collaborative supply strategy to determine the timing and supply of irrigation and fertilization;

[0052] S4, intelligent supply implementation: Establish an integrated intelligent supply unit to automatically control irrigation and fertilization equipment, and intelligently adjust water and fertilizer supply according to the collaborative supply strategy designed in S3.

[0053] The sensor network in S1 is based on IoT technology and includes soil moisture sensors, electrochemical sensors, optical sensors, and transpiration rate meters;

[0054] Soil moisture sensors measure the change in capacitance of the soil to obtain moisture content;

[0055] Electrochemical sensors detect changes in the electrical conductivity of soil nutrients and provide data on soil nutrient levels;

[0056] Optical sensors monitor chlorophyll content and calculate chlorophyll concentration using the absorption characteristics of light of corresponding wavelengths. At the same time, a transpiration rate meter is used to measure the transpiration rate of leaves;

[0057] All monitoring data is uploaded to the cloud platform in real time via wireless network to ensure real-time update of information and provide accurate growth environment information for subsequent data analysis and decision-making.

[0058] The chlorophyll concentration C can be calculated by the following formula: ,in, is the chlorophyll concentration (in ), yes The reflectivity at the wavelength, yes The reflectivity at the wavelength, is the calibration factor, which is adjusted according to the specific sensor and experimental conditions;

[0059] Wavelength selection: 680nm and 700nm wavelengths correspond to the absorption peak and non-absorption peak of chlorophyll a respectively.

[0060] Reflectance measurement: The light reflectance of plant leaves at these two wavelengths is measured by an optical sensor.

[0061] After the monitoring data in S1 is collected, it also includes growth stage division, including germination stage, jointing stage, heading stage, and filling stage. The monitored soil moisture, soil nutrient level, and crop physiological state data are divided according to the germination stage, jointing stage, heading stage, and filling stage to obtain a divided data set.

[0062] Before constructing the dynamic water and fertilizer demand model in S2, the correlation analysis between soil moisture, soil nutrient level and crop physiological state is also included to identify the impact of soil moisture and nutrient level on crop physiological state. The specific calculation expression is:

[0063] ;

[0064] in, is soil moisture or soil nutrient level, For crop physiological status, The correlation coefficient between soil moisture or soil nutrient level and crop physiological status indicates the strength of the linear relationship between the two. It is The soil moisture or soil nutrient level value of the observation sample, is the average soil moisture or soil nutrient level, It is The crop physiological status value (chlorophyll content or transpiration rate) of each observation sample, It is the average value of the crop physiological state. The correlation coefficient obtained by analysis can be used to determine the dependence and influence degree between soil moisture, soil nutrient level and crop physiological state.

[0065] The analysis of water and nutrient requirements at different growth stages in S2 includes stage-by-stage data analysis and linear regression modeling: Based on the results of correlation analysis, a linear regression model is established for each growth stage to reversely infer water and nutrient requirements, namely water and fertilizer requirements, and set the expected crop physiological state. (such as chlorophyll content or transpiration rate), and soil moisture can be inferred through a linear regression model and soil nutrient levels :

[0066] ;

[0067] in, It is the physiological state of the crop at the corresponding growth stage, including chlorophyll content, transpiration rate, is the intercept of the regression model, which represents the crop physiological state value when the soil moisture and soil nutrient levels are zero. is soil moisture The regression coefficient of represents the influence of soil moisture on crop physiological state. is the soil nutrient level The regression coefficient of represents the influence of soil nutrient level on crop physiological state. is the error term, which represents the influence of other factors that the model cannot explain;

[0068] By deformation we get:

[0069] ;

[0070] ;

[0071] In each growth stage, the results of correlation analysis were used to select and , and reverse calculation to determine the corresponding water and nutrient requirements.

[0072] Regression coefficient , Based on the results of correlation analysis and the least squares method, the following are the estimates:

[0073] First, the correlation coefficient was calculated to understand the linear relationship between soil moisture and nutrient levels and crop physiological status. Using the correlation analysis formula mentioned above, we can get (correlation coefficient between soil moisture and crop physiological status) and (Correlation coefficient between soil nutrient level and crop physiological status);

[0074] Next, use the above linear regression analysis method to fit the model and calculate the linear regression parameters:

[0075] 1. Prepare data: select a set of observations ,in is the data sample index;

[0076] 2. Build the Matrix: Build the Design Matrix and the response variable vector :

[0077] ;

[0078] 3. Calculate parameters: Use the least squares method to estimate the parameters: ,in, is included and A vector of is the effect of each unit change in soil moisture on the crop’s physiological state; It is the effect of each unit change in soil nutrient level on the physiological state of the crop.

[0079] Construction of dynamic water and fertilizer demand model based on inverse soil moisture and soil nutrient levels , to reflect the water and fertilizer requirements at different growth stages, the model is expressed as:

[0080] Germination stage: ;

[0081] Jointing stage: ;

[0082] Heading stage: ;

[0083] Grouting stage: ;

[0084] in, The water or nutrient requirements for each stage, , ,..., It is the coefficient of each stage obtained through regression analysis, which indicates the degree of influence of water and nutrients on the requirements of each growth stage.

[0085] The method for determining is similar to the above, as follows:

[0086] 1. Data collection: Collect water at different growth stages (germination, jointing, heading, filling). and nutrients Demand data, and the physiological state of the crop corresponding to these stages ;

[0087] 2. Build a model: For each growth stage, build a linear model: ,in, is the water or nutrient requirement for this stage, and is the parameter to be estimated;

[0088] 3. Linear regression analysis: Use the collected water and nutrient demand data to perform linear regression analysis and fit the model to estimate and , the specific steps include:

[0089] Constructing the Design Matrix and the response variable ;

[0090] Apply the least squares method: ; In the result and That is, the influence coefficient of each stage on the water and nutrient demand. After interpreting the parameters, the final result is It reflects the degree to which water and nutrients affect the physiological state of crops at each growth stage, ensuring that the model can effectively predict demand.

[0091] S3 specifically includes:

[0092] S31, real-time monitoring of the crop physiological state at the current growth stage. If the crop physiological state at the current growth stage reaches the average chlorophyll content and transpiration rate of the current growth stage (by consulting relevant agricultural and plant physiology literature, the standard data of chlorophyll content and transpiration rate of corn at different growth stages are known), the water and fertilizer supply strategy is not optimized. If the crop physiological state at the current growth stage is lower than the average chlorophyll content and transpiration rate of the current growth stage, the average level of the crop physiological state at the current growth stage is set to And through the linear regression model, the soil moisture W and soil nutrient level N that should be in the current growth stage are obtained;

[0093] S32, real-time monitoring of soil moisture at the current growth stage and soil nutrient levels , and compare it with the soil moisture W and soil nutrient level N obtained in the current growth stage to determine the irrigation and fertilization timing of the current growth stage. or , then irrigation or fertilization is triggered.

[0094] After determining the timing of irrigation and fertilization in S32, it also includes:

[0095] Water requirement calculate: ,in, is the total water supply per irrigation (unit: L), is the soil moisture required for the current growth stage (unit: L), is the real-time monitored soil moisture (unit: L), is the area of ​​the irrigated area (unit: );

[0096] Nutrient requirements calculate: ,in, is the total nutrient supply per fertilization (unit: kg), is the soil nutrient level that should be present at the current growth stage (unit: ), is the soil nutrient level monitored in real time (unit: ), is the area of ​​the fertilization area (unit: ).

[0097] When calculating irrigation and fertilizer amounts, the units of each parameter should be consistent.

[0098] Irrigation implementation: After determining Finally, the intelligent supply unit adjusts the irrigation equipment to ensure that each irrigation can provide of moisture.

[0099] Fertilization implementation: After determining Finally, the fertilization equipment is adjusted through the intelligent supply unit to ensure that each fertilization can provide nutrients.

[0100] S3 also includes transpiration rate and chlorophyll content combined with crop physiological status as additional basis to ensure irrigation and fertilization at critical times of crop demand. When the transpiration rate is high and the chlorophyll content is low, it means that the crop is growing rapidly and lacks nutrients, and timely fertilization is required to meet crop needs. The conditions are met: If the transpiration rate exceeds the preset threshold and the chlorophyll content is lower than the preset threshold, the fertilization operation is triggered.

[0101] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0102] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An intelligent corn water and fertilizer supply optimization method, characterized in that: The following steps are involved: S1, soil and crop status monitoring: using a sensor network to monitor soil moisture, soil nutrient levels and crop physiological status, including chlorophyll content and transpiration rate, and upload the monitoring data to the cloud platform; S2, establish a dynamic water and fertilizer demand model: based on the monitoring data of the cloud platform, analyze the water and nutrient requirements at different growth stages and build a dynamic water and fertilizer demand model; S3, collaborative supply strategy design: monitor the current crop physiological state and combine it with the dynamic water and fertilizer demand model to design a collaborative supply strategy to determine the timing and supply of irrigation and fertilization; S4, intelligent supply implementation: establish an integrated intelligent supply unit to automatically control irrigation and fertilization equipment, and intelligently adjust water and fertilizer supply according to the collaborative supply strategy designed in S3; The sensor network in S1 is based on Internet of Things technology and includes a soil moisture sensor, an electrochemical sensor, an optical sensor and a transpiration rate meter; The soil moisture sensor obtains the moisture content by measuring the capacitance change of the soil; The electrochemical sensor detects changes in the conductivity of soil nutrients and provides soil nutrient level data; The optical sensor monitors the chlorophyll content and calculates the chlorophyll concentration using the absorption characteristics of light of corresponding wavelengths. At the same time, a transpiration rate meter is used to measure the transpiration rate of leaves; All monitoring data are uploaded to the cloud platform in real time via wireless network; The chlorophyll concentration C is calculated by the following formula: ,in, is the chlorophyll concentration (in ), yes The reflectivity at the wavelength, yes The reflectivity at the wavelength, is the calibration factor; After the monitoring data in S1 is collected, the growth stage division is also included, including the germination stage, the jointing stage, the heading stage, and the filling stage. The monitored soil moisture, soil nutrient level, and crop physiological state data are divided according to the germination stage, the jointing stage, the heading stage, and the filling stage to obtain a divided data set; Before constructing the dynamic water and fertilizer demand model in S2, the method also includes performing a correlation analysis between soil moisture, soil nutrient level and crop physiological state to identify the influence of soil moisture and nutrient level on crop physiological state, and judging the dependence and influence degree between soil moisture, soil nutrient level and crop physiological state by analyzing the correlation coefficient obtained; Before constructing the dynamic water and fertilizer demand model in S2, the correlation analysis between soil moisture, soil nutrient level and crop physiological state is also included to identify the impact of soil moisture and nutrient level on crop physiological state. The specific calculation expression is: in, is soil moisture or soil nutrient level, For crop physiological status, The correlation coefficient between soil moisture or soil nutrient level and crop physiological status indicates the strength of the linear relationship between the two. It is The soil moisture or soil nutrient level value of the observation sample, is the average soil moisture or soil nutrient level, It is The crop physiological state value of observation samples, It is the average value of crop physiological state. By analyzing the correlation coefficient, we can determine the dependence and influence degree between soil moisture, soil nutrient level and crop physiological state. The analysis of water and nutrient requirements at different growth stages in S2 includes stage-by-stage data analysis and linear regression modeling: Based on the results of correlation analysis, a linear regression model is established for each growth stage to reversely infer water and nutrient requirements, i.e., water and fertilizer requirements, and set the expected crop physiological state. , soil moisture can be inferred through linear regression model and soil nutrient levels : ； in, It is the physiological state of the crop at the corresponding growth stage, including chlorophyll content, transpiration rate, is the intercept of the regression model, which represents the crop physiological state value when the soil moisture and soil nutrient levels are zero. is soil moisture The regression coefficient of represents the influence of soil moisture on crop physiological state. is the soil nutrient level The regression coefficient of represents the influence of soil nutrient level on crop physiological state. is the error term; In each growth stage, the results of correlation analysis were used to select and , and reversely determine the corresponding water and nutrient requirements; The construction of the dynamic water and fertilizer demand model is based on the reverse calculation of soil moisture and soil nutrient levels , to reflect the water and fertilizer requirements at different growth stages, the model is expressed as: Germination stage: ; Jointing stage: ; Heading stage: ; Grouting stage: ; in, The water or nutrient requirements for each stage, , ,..., It is the coefficient of each stage obtained through regression analysis, which indicates the degree of influence of water and nutrients on the requirements of each growth stage.

2. The intelligent corn water and fertilizer supply optimization method according to claim 1 is characterized in that: The S3 specifically includes: S31, real-time monitoring of the crop physiological state at the current growth stage. If the crop physiological state at the current growth stage reaches the average chlorophyll content and transpiration rate of the current growth stage, the water and fertilizer supply strategy is not optimized. If the crop physiological state at the current growth stage is lower than the average chlorophyll content and transpiration rate of the current growth stage, the average level of the crop physiological state at the current growth stage is set to And through the linear regression model, the soil moisture W and soil nutrient level N that should be in the current growth stage are obtained; S32, real-time monitoring of soil moisture at the current growth stage and soil nutrient levels , and compare it with the soil moisture W and soil nutrient level N obtained in the current growth stage to determine the irrigation and fertilization timing of the current growth stage. or , then irrigation or fertilization is triggered.

3. The intelligent corn water and fertilizer supply optimization method according to claim 2 is characterized in that: After determining the irrigation and fertilization timing in S32, the following steps are also included: Water requirement calculate: ,in, is the total water supply per irrigation, is the soil moisture required for the current growth stage. It is the soil moisture monitored in real time. is the area of ​​the irrigated area; Nutrient requirements calculate: ,in, is the total nutrient supply per fertilization, is the soil nutrient level that should be present at the current growth stage. It is the real-time monitoring of soil nutrient levels. is the area of ​​the fertilized area.

4. The intelligent corn water and fertilizer supply optimization method according to claim 3 is characterized in that: The S3 also includes combining the transpiration rate and chlorophyll content of the crop physiological state as additional basis to ensure irrigation and fertilization at critical times of crop demand, and the conditions are met: if the transpiration rate exceeds a preset threshold and the chlorophyll content is lower than a preset threshold, the fertilization operation is triggered.

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