Data analysis and monitoring methods of drought tolerance of corn and wheat in the field

Abstract

The present invention relates to the field of agricultural production technology, and in particular to a method for analyzing and monitoring drought tolerance data of corn and wheat in the field, comprising the following steps: S1, data acquisition: collecting environmental and physiological data of corn and wheat in the field based on multi-source sensors; S2, data preprocessing: preprocessing the collected environmental and physiological data, and integrating the environmental and physiological data into comprehensive data; S3, leaf angle regulation transpiration mechanism: analyzing the influence of leaf angle changes of corn and wheat on light absorption and transpiration rate, optimizing leaf angles in real time to adapt to changes in light intensity, and predicting water consumption of corn and wheat under different light conditions; S4, drought tolerance analysis: evaluating the drought tolerance of corn and wheat. The present invention improves the efficiency of water resource utilization, ensures the healthy growth of crops in arid environments, and improves the practicality of farmland management and the scientific nature of decision-making.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural production, and in particular to a method for analyzing and monitoring drought resistance data of corn and wheat in the field. Background Art

[0002] In the cultivation process of major food crops such as corn and wheat, insufficient water supply will directly affect the growth and yield of crops. Drought resistance monitoring and water management of field crops are of great significance to maintaining the sustainable development of agriculture. Traditional irrigation management methods mostly rely on human experience and cannot respond to environmental changes in real time, resulting in inefficient use of water resources. At the same time, the use of sensors and data analysis technology to optimize irrigation management and improve crop drought resistance has become an important direction for the development of modern agricultural technology.

[0003] Existing drought tolerance monitoring methods mainly focus on monitoring a single environment or physiological parameters, lack comprehensive response analysis of crops under different environmental conditions, and are difficult to fully reflect the drought tolerance of crops. In addition, most monitoring systems have a low reliance on data processing and only stay at the basic collection and monitoring level. They fail to fully utilize multimodal data fusion technology to achieve comprehensive data analysis. Existing technologies also have deficiencies in data cleaning, standardization, and data fusion processing, resulting in low data quality and reliability, which in turn affects the accuracy and real-time nature of monitoring, especially in key links such as light regulation, water management, and stomatal regulation efficiency. There is a lack of dynamic control mechanisms, and it is impossible to flexibly adjust water utilization and transpiration control strategies according to real-time environmental changes, making it difficult to meet the requirements of precision irrigation and efficient drought resistance management.

[0004] The present invention aims to provide a method for analyzing and monitoring drought tolerance data of corn and wheat in the field, which provides a basis for precise irrigation and drought resistance management, and improves the water resource utilization efficiency of crops and the practicability of drought tolerance monitoring. Summary of the invention

[0005] The invention provides a method for analyzing and monitoring drought resistance data of corn and wheat in the field.

[0006] The method for analyzing and monitoring drought tolerance data of corn and wheat in the field includes the following steps:

[0007] S1, data collection: Collect environmental and physiological data of corn and wheat in the field based on multi-source sensors, including air temperature, air humidity, soil moisture, leaf angle, leaf temperature and stomatal conductance;

[0008] S2, data preprocessing: preprocess the collected environmental and physiological data, including cleaning and standardization, and use multimodal data fusion technology to integrate environmental and physiological data into comprehensive data;

[0009] S3, leaf angle regulation of transpiration mechanism: Analyze the impact of leaf angle changes of corn and wheat on light absorption and transpiration rate, optimize leaf angle in real time to adapt to changes in light intensity, and dynamically adjust transpiration thresholds based on light conditions and water requirements of corn and wheat, and predict water consumption of corn and wheat under different light conditions, including:

[0010] S31, Analysis of the relationship between leaf angle and light intensity: By collecting leaf angle and corresponding transpiration rate data under different light conditions, the effect of leaf angle changes on light absorption efficiency and transpiration rate in the field environment is analyzed;

[0011] S32, dynamic adaptive light regulation: Based on the results of the analysis of the relationship between leaf angle and light intensity, the changes in ambient light intensity are monitored in real time, and the leaf angle is dynamically optimized to maximize the photosynthesis efficiency under different light conditions;

[0012] S33, Dynamic Control of Transpiration Threshold and Water Consumption Prediction: Based on the results of leaf angle and light adaptation adjustment, combined with the real-time water demand of corn and wheat, the transpiration threshold is dynamically set to predict the water consumption under different light conditions;

[0013] S4, Drought tolerance analysis: Based on the integrated comprehensive data and combined with the analysis results of the leaf angle regulation transpiration mechanism, the drought tolerance of corn and wheat was evaluated, including water use efficiency, transpiration response index and stomatal regulation efficiency.

[0014] Optionally, the data collection in S1 includes:

[0015] S11, air temperature and humidity collection: placing temperature sensors and humidity sensors in the field environment to detect air temperature and humidity in real time;

[0016] S12, soil moisture collection: soil moisture sensors were buried at different depths around the roots of corn and wheat to measure the moisture content of each soil layer;

[0017] S13, leaf angle monitoring: optical angle sensors are installed around crop plants to monitor the angle changes of leaves and record the real-time positions of leaves under different lighting conditions;

[0018] S14, blade temperature collection: monitor the surface temperature of the blade through an infrared temperature sensor to obtain the change of the blade temperature;

[0019] S15, stomatal conductance measurement: the opening and closing status of crop stomata is measured by a stomatal conductance sensor.

[0020] Optionally, the data preprocessing in S2 includes:

[0021] S21, data cleaning: outlier detection and noise elimination were performed on the collected environmental and physiological data, and outliers were removed using the IQR (interquartile range) method;

[0022] S22, data standardization: The Z-score standardization method was used to standardize the environmental and physiological data after cleaning;

[0023] S23, multimodal data fusion: A weighted average fusion algorithm is used to integrate the standardized environmental and physiological data into comprehensive data.

[0024] Optionally, the analysis of the relationship between the blade angle and the light intensity in S31 includes:

[0025] S311, light intensity and transpiration rate collection: under different light conditions (morning, noon and evening), the light intensity is collected in real time through transpiration sensors and light sensors and the corresponding transpiration rate ;

[0026] S312, calculate light absorption efficiency: calculate the light absorption efficiency of the blade based on the collected blade angle and light intensity ;

[0027] S313, transpiration rate response: combining leaf angle and light absorption efficiency, a regression analysis model was used to calculate the effect of leaf angle on transpiration rate.

[0028] Optionally, the dynamic adaptive adjustment of illumination in S32 includes:

[0029] S321, real-time monitoring of light intensity: real-time monitoring of changes in light intensity in the field, obtaining the current light intensity value (unit );

[0030] S322, blade angle optimization calculation: based on time The light intensity value at , combined with the target transpiration rate upper limit , calculate the time The optimal blade angle , and dynamically adjust the leaf angle based on the calculation results to maximize photosynthesis efficiency.

[0031] Optionally, the dynamic regulation of transpiration threshold and water consumption prediction in S33 includes:

[0032] S331, Dynamic setting of transpiration threshold: based on optimal leaf angle and light intensity , combined with the real-time water demand of corn and wheat, dynamically calculate the transpiration threshold ;

[0033] S332, Water consumption prediction: Based on the set transpiration threshold, predict the water consumption of corn and wheat under different light conditions.

[0034] Optionally, the drought tolerance analysis in S4 includes:

[0035] S41, Water use efficiency assessment: By analyzing the integrated comprehensive data, the photosynthesis efficiency of crops under current water conditions is assessed. By calculating the water use efficiency of corn and wheat at different growth stages and light conditions, the adaptability and water use capacity of crops under drought stress are judged.

[0036] S42, Calculation of transpiration response index: Based on the analysis results of the leaf angle regulation mechanism of transpiration, the transpiration response index of corn and wheat was calculated to quantify the sensitivity of transpiration rate to light changes;

[0037] S43, stomatal regulation efficiency evaluation: By analyzing the stomatal conductance data, the stomatal regulation efficiency, that is, the ability of crops to regulate the opening and closing of stomata under water stress, is evaluated to determine the self-protection mechanism of crops under water-limited conditions.

[0038] Optionally, the water use efficiency evaluation in S41 includes:

[0039] S411, Water use efficiency calculation: Based on the integrated comprehensive data, water use efficiency is calculated Assess crop photosynthesis efficiency under current water conditions;

[0040] S412, Application of growth stage adjustment coefficient: Combine the growth stages of corn and wheat and set the water use efficiency adjustment coefficient for each stage. , to reflect the water demand characteristics of different growth stages;

[0041] S413, Effect of light conditions on water use efficiency: Based on light intensity under different light conditions, , by setting the light adjustment factor To correct water use efficiency.

[0042] Optionally, the transpiration response index calculation in S42 includes:

[0043] S421, leaf angle and light change data collection: data collected under different light intensities The lower blade angle and the corresponding transpiration rate , forming a correlation data set of leaf angle, light intensity and transpiration rate;

[0044] S422, Calculation of sensitivity of transpiration rate to light changes: Calculate the transpiration response index based on the formed correlation data set , to quantify the sensitivity of transpiration rate to changes in light;

[0045] S423, Dynamic adjustment of transpiration response index: In order to reflect the effect of leaf angle regulation, the leaf angle in the transpiration mechanism is regulated according to the leaf angle The impact on light absorption efficiency and dynamic adjustment of the transpiration response index.

[0046] Optionally, the stomatal regulation efficiency evaluation in S43 includes:

[0047] S431, stomatal conductance data collection: real-time collection of stomatal conductance of corn and wheat at different air humidity and soil moisture levels , forming a dynamic data set of stomatal conductance changes with the environment;

[0048] S432, Stomatal Regulation Efficiency Calculation: Calculate stomatal regulation efficiency based on the dynamic data set formed , used to quantify the opening and closing regulation ability of stomata under water stress;

[0049] S433, Dynamic Regulation Analysis: Under the condition of limited water, combined with the analysis results of stomatal regulation efficiency, the self-protection mechanism of corn and wheat, that is, the ability of corn and wheat to reduce water loss under water stress, is determined. When the preset adjustment threshold is exceeded, it means that the pores have the ability to adjust.

[0050] Beneficial effects of the present invention:

[0051] The present invention uses multi-source sensors to comprehensively collect environmental and physiological data of field corn and wheat, including air temperature, air humidity, soil moisture, leaf angle, leaf temperature and stomatal conductance, to ensure the timeliness and accuracy of the data. Through multi-level and multi-dimensional data collection, the dynamic physiological changes of crops under different growth conditions can be fully captured. At the same time, the data preprocessing steps include cleaning, standardization and multimodal data fusion, which further improves the quality and consistency of the data, so that the data has equal importance in subsequent analysis.

[0052] The present invention achieves optimal regulation of light absorption and transpiration rate by blade angle through regulating transpiration mechanism through blade angle, thereby ensuring that crops can maximize photosynthesis efficiency under different lighting conditions, while avoiding excessive water loss caused by too high transpiration rate. In addition, through dynamic setting of transpiration threshold and prediction of water consumption, transpiration behavior can be flexibly adjusted according to current environmental conditions and crop water demand, thus realizing intelligent and dynamic water management. Under drought conditions, the dynamic regulation mechanism can more accurately predict and optimize crop water demand, providing scientific support for drought resistance management.

[0053] The present invention comprehensively measures the adaptability and self-protection mechanism of crops under different water and light conditions by combining water use efficiency, transpiration response index and stomatal regulation efficiency. By systematically analyzing the water use efficiency and transpiration sensitivity of crops, the drought resistance characteristics of crops under drought stress can be accurately evaluated, providing strong data support for scientific irrigation and drought-resistant management of farmland. This multi-level analysis makes irrigation strategies more accurate and efficient, improves water resource utilization efficiency, ensures the healthy growth of crops in arid environments, and enhances the practicality of farmland management and the scientific nature of decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] 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.

[0055] Figure 1 A schematic diagram of the analysis and monitoring method according to an embodiment of the present invention;

[0056] Figure 2 Schematic diagram of the transpiration control mechanism of blade angle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] 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.

[0058] 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).

[0059] 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.

[0060] like Figure 1-Figure 2 As shown, the method for analyzing and monitoring drought tolerance data of corn and wheat in the field includes the following steps:

[0061] S1, data collection: Collect environmental and physiological data of corn and wheat in the field based on multi-source sensors, including air temperature, air humidity, soil moisture, leaf angle, leaf temperature and stomatal conductance;

[0062] S2, data preprocessing: preprocessing the collected environmental and physiological data, including cleaning and standardization, to eliminate environmental noise and outliers, and using multimodal data fusion technology to integrate environmental and physiological data into comprehensive data;

[0063] S3, leaf angle regulation of transpiration mechanism: Analyze the impact of leaf angle changes of corn and wheat on light absorption and transpiration rate, optimize leaf angle in real time to adapt to changes in light intensity, and dynamically adjust transpiration thresholds based on light conditions and water requirements of corn and wheat, and predict water consumption of corn and wheat under different light conditions, including:

[0064] S31, Analysis of the relationship between leaf angle and light intensity: By collecting leaf angle and corresponding transpiration rate data under different light conditions, the effect of leaf angle changes on light absorption efficiency and transpiration rate in the field environment is analyzed;

[0065] S32, dynamic adaptive light regulation: Based on the results of the analysis of the relationship between leaf angle and light intensity, the changes in ambient light intensity are monitored in real time, and the leaf angle is dynamically optimized to maximize the photosynthesis efficiency under different light conditions;

[0066] S33, Dynamic regulation of transpiration threshold and prediction of water consumption: According to the results of leaf angle and light adaptation regulation, combined with the real-time water demand of corn and wheat, the transpiration threshold is dynamically set to predict the water consumption under different light conditions, ensuring that the transpiration regulation mechanism optimizes the water demand of crops while ensuring photosynthesis;

[0067] S4, drought tolerance analysis: Based on the integrated comprehensive data and the analysis results of the leaf angle regulation transpiration mechanism, the drought tolerance of corn and wheat was evaluated, including water use efficiency, transpiration response index and stomatal regulation efficiency;

[0068] Through the above content, the accuracy and responsiveness of corn and wheat drought resistance monitoring have been effectively improved. It can not only dynamically adapt to environmental changes and optimize the water requirements of crops, but also realize the accurate assessment of water use efficiency and transpiration response, providing a scientific basis for intelligent irrigation management of farmland and improving the practicality and efficiency of crop drought resistance monitoring.

[0069] Data collection in S1 includes:

[0070] S11, air temperature and humidity collection: placing temperature sensors and humidity sensors in the field environment to detect air temperature and humidity in real time;

[0071] S12, soil moisture collection: soil moisture sensors were buried at different depths around the roots of corn and wheat to measure the moisture content of each soil layer;

[0072] S13, leaf angle monitoring: optical angle sensors are installed around crop plants to monitor the angle changes of leaves and record the real-time positions of leaves under different lighting conditions;

[0073] S14, blade temperature collection: monitor the surface temperature of the blade through an infrared temperature sensor to obtain the change of the blade temperature;

[0074] S15, stomatal conductance measurement: measuring the opening and closing state of crop stomata through a stomatal conductance sensor;

[0075] Through the above content, comprehensive and accurate collection of field environment and crop physiological data has been achieved, effectively improving the accuracy and timeliness of the data. This multi-level and multi-dimensional data collection provides a rich original data basis for drought resistance analysis, ensures the dynamic adaptability of the monitoring system to different growth conditions, and provides scientific support for precision irrigation and drought resistance management decisions, significantly improving the practicality and reliability of crop drought resistance monitoring.

[0076] Data preprocessing in S2 includes:

[0077] S21, data cleaning: outlier detection and noise elimination are performed on the collected environmental and physiological data, and the IQR (interquartile range) method is used to remove outliers, which is expressed as:

[0078] ;

[0079] Among them, if the data value or , it is judged as an outlier and removed;

[0080] S22, data standardization: The Z-score standardization method is used to standardize the environmental and physiological data after cleaning, and the data of different dimensions are converted into standardized data with a mean of 0 and a variance of 1, which is expressed as:

[0081] ;

[0082] in, is the original data, is the data mean, is the standard deviation;

[0083] S23, multimodal data fusion: The weighted average fusion algorithm is used to integrate the standardized environmental and physiological data into comprehensive data, which is expressed as:

[0084] ;

[0085] in, For the integrated data, is environmental data (air temperature, air humidity, soil moisture), For crop physiological data (leaf angle, leaf temperature, stomatal conductance), and are the corresponding fusion weights respectively, satisfying , The value is 0.4. The value is 0.6;

[0086] Through the above content, the accuracy and consistency of the data are ensured, the dimensional differences of different data features are eliminated, and the data have equal importance in the fusion analysis. Through multimodal data fusion technology, the environmental and physiological data are integrated into comprehensive data to fully reflect the drought resistance of crops, improve the quality and reliability of the data, and provide a high-precision and unified data basis for drought resistance analysis, which will help to achieve more accurate monitoring and evaluation.

[0087] The analysis of the relationship between blade angle and light intensity in S31 includes:

[0088] S311, light intensity and transpiration rate collection: under different light conditions (morning, noon and evening), the light intensity is collected in real time through transpiration sensors and light sensors and the corresponding transpiration rate ;

[0089] S312, calculate light absorption efficiency: calculate the light absorption efficiency of the blade based on the collected blade angle and light intensity , expressed as:

[0090] ;

[0091] in, is the incident light intensity, is the angle between the blade and the incident direction of the light;

[0092] S313, transpiration rate response: Combined with leaf angle and light absorption efficiency, the effect of leaf angle on transpiration rate is calculated using a regression analysis model, expressed as:

[0093] ;

[0094] in, is the transpiration rate, and is the regression coefficient, obtained by fitting multiple sets of experimental data and The value of , forms the transpiration rate response model;

[0095] Through the above content, the effect of leaf angle on light absorption and transpiration rate was quantified, and a response model of leaf angle, light absorption efficiency and transpiration rate was established, which is helpful to dynamically predict the water demand of crops under different light conditions, and can more accurately manage and optimize water utilization under drought conditions, providing a scientific basis for drought resistance monitoring, thereby improving the accuracy and adaptability of field management.

[0096] The dynamic adaptation of lighting in S32 includes:

[0097] S321, real-time monitoring of light intensity: real-time monitoring of changes in light intensity in the field, obtaining the current light intensity value (unit: );

[0098] S322, blade angle optimization calculation: based on time The light intensity value at , combined with the target transpiration rate upper limit , calculate the time The optimal blade angle , and dynamically adjust the leaf angle according to the calculation results to maximize the photosynthesis efficiency, expressed as:

[0099] ;

[0100] in, For the moment The optimal blade angle is is the light absorption efficiency coefficient, The upper limit of the target transpiration rate indicates the maximum transpiration rate allowed under the current conditions. By adjusting the leaf angle, the leaves can obtain sufficient photosynthesis under real-time lighting conditions without excessively increasing the transpiration rate, thereby optimizing water use efficiency.

[0101] Through the above content, the light intensity is monitored in real time and the leaf angle is dynamically optimized, so that the leaves can obtain the maximum photosynthesis efficiency under different light conditions, while avoiding excessive transpiration rate and reducing water loss. By balancing the light absorption efficiency coefficient and the upper limit of the target transpiration rate, the water use efficiency of the crop is effectively optimized, providing a more intelligent and precise control scheme for drought resistance monitoring, so that crops can better adapt to environmental changes under drought conditions.

[0102] Dynamic regulation of transpiration threshold and water consumption prediction in S33 include:

[0103] S331, Dynamic setting of transpiration threshold: based on optimal leaf angle and light intensity , combined with the real-time water demand of corn and wheat, dynamically calculate the transpiration threshold , expressed as:

[0104] ;

[0105] in, is the dynamically set transpiration threshold, is the adjustment coefficient of light and water demand, which is used to balance the relationship between transpiration rate and light. For the current water requirements of corn and wheat, It indicates the effect of leaf angle on light absorption efficiency;

[0106] Current Water Requirements for Corn and Wheat Calculated based on a variety of environmental and physiological factors, including:

[0107] Soil moisture: The soil moisture sensor monitors the soil moisture content in real time. When the soil moisture is insufficient, increase To reflect the higher water demand of crops, a preliminary estimate of soil moisture for water demand is expressed as:

[0108] ;in, is the current soil moisture, Optimal soil moisture for crops, is the adjustment factor;

[0109] Air temperature and humidity: Air temperature and air humidity affect the transpiration rate of crops, thereby affecting their water demand. The higher the temperature and the lower the humidity, the higher the transpiration rate and the higher the water demand of crops, expressed as:

[0110] ;

[0111] in, and are the current temperature and humidity, and To provide the optimal temperature and humidity for crops, is the adjustment coefficient;

[0112] Crop growth stage: Crops at different growth stages have different water requirements. A basic demand coefficient is set according to the crop growth stage. , indicating the water demand intensity at the current growth stage;

[0113] ;

[0114] in, Indicates the water requirement due to the current growth stage, It is the basic demand coefficient of the growth stage, reflecting the overall demand level for water at different stages. It is a weighted coefficient set according to the growth stage (such as the growing period, flowering period, fruiting period, etc.);

[0115] Leaf temperature and stomatal conductance: Leaf temperature and stomatal conductance reflect the transpiration of crops. When the leaf temperature is high and the stomatal conductance is large, the transpiration rate of crops is high and the water demand is also higher. The immediate water demand adjustment value of crops is calculated based on leaf temperature and stomatal conductance. , expressed as:

[0116] ;

[0117] in, is the adjustment for the immediate water demand due to leaf temperature and stomatal conductance, is the adjustment coefficient, is the stomatal conductance, is the leaf temperature;

[0118] Calculation of current water requirements for corn and wheat: Calculation of current water requirements for corn and wheat by weighted combination , expressed as:

[0119] ;

[0120] in, , , , are the weight coefficients of each factor respectively;

[0121] S332, water consumption prediction: Based on the set transpiration threshold, the water consumption of corn and wheat under different light conditions is predicted, expressed as:

[0122] ;

[0123] in, For the moment The predicted transpiration rate (i.e., water consumption rate) under is the current light intensity, is the maximum light intensity of the environment;

[0124] Through the above content, it is possible to respond to environmental changes in real time, optimize water management, ensure that crops obtain sufficient photosynthesis while avoiding excessive transpiration, effectively improve water use efficiency, and the dynamic control mechanism makes the system more adaptable under different growth conditions, providing scientific decision-making support for farmland drought resistance management.

[0125] Drought tolerance analysis in S4 includes:

[0126] S41, Water use efficiency assessment: By analyzing the integrated comprehensive data, the photosynthesis efficiency of crops under current water conditions is assessed. By calculating the water use efficiency of corn and wheat at different growth stages and light conditions, the adaptability and water use capacity of crops under drought stress are judged.

[0127] S42, Calculation of transpiration response index: Based on the analysis results of the leaf angle regulation transpiration mechanism, the transpiration response index of corn and wheat was calculated to quantify the sensitivity of transpiration rate to light changes;

[0128] S43, stomatal regulation efficiency assessment: by analyzing stomatal conductance data, the stomatal regulation efficiency is assessed, that is, the crop's ability to regulate the opening and closing of stomata under water stress, and the crop's self-protection mechanism under water-limited conditions is determined;

[0129] Through the above content, the adaptability and self-protection mechanism of crops under different water and light conditions can be comprehensively measured. Through the systematic analysis of water use, transpiration sensitivity and stomatal regulation ability, the drought resistance and water requirements of crops can be accurately judged, which helps to optimize irrigation strategies, improve water resource utilization efficiency, ensure the healthy growth of crops under drought stress, and provide strong support for scientific farmland management and drought resistance decision-making.

[0130] The water use efficiency assessment in S41 includes:

[0131] S411, Water use efficiency calculation: Based on the integrated comprehensive data, water use efficiency is calculated Evaluate the photosynthesis efficiency of crops under current water conditions, expressed as:

[0132] ;

[0133] in, Represents the photosynthetic rate, which is obtained by measuring the net photosynthetic intensity of the leaves. represents the transpiration rate;

[0134] S412, Application of growth stage adjustment coefficient: Combine the growth stages of corn and wheat and set the water use efficiency adjustment coefficient for each stage. , to reflect the water demand characteristics of different growth stages, the water use efficiency at different growth stages is expressed as:

[0135] ;

[0136] in, is the water use efficiency adjusted based on the crop growth stage, The value of is adjusted according to the growth stage of the crop (seedling stage, heading stage, maturity stage) to reflect the different requirements for water efficiency in each stage;

[0137] Adjustment factor for water use efficiency The settings include:

[0138] Seedling stage: In the seedling stage, the water demand and photosynthesis efficiency of crops are relatively low, so the adjustment coefficient of water use efficiency Set it to a smaller value, such as 0.8;

[0139] Heading stage: During the heading stage, crops enter a rapid growth stage with higher demands for water and photosynthesis efficiency. is a larger value, taking the value as 1.2;

[0140] Maturity: During the maturity period, crop growth slows down and water demand decreases. For medium value, the value is 1;

[0141] S413, Effect of light conditions on water use efficiency: Water use efficiency will vary under different light conditions, based on light intensity. , by setting the light adjustment factor To correct the water use efficiency, it is expressed as:

[0142] ;

[0143] in, is the water use efficiency after adjusting for light conditions, is the light adjustment factor, which is based on the light intensity Adjust the height of

[0144] Through the above content, water use efficiency is dynamically adjusted to more comprehensively reflect the water demand and utilization capacity of crops in different environments and growth conditions. Through the application of growth stage adjustment coefficients and light regulation factors, it can flexibly adapt to the different water demands of crops in critical growth periods, while accurately evaluating the impact of light intensity on water use. This multi-level adjustment and precise calculation not only improves the accuracy of drought resistance analysis, but also provides strong data support for scientific irrigation and water management, which helps to improve the utilization efficiency of water resources and the drought resistance of crops.

[0145] The transpiration response index calculation in S42 includes:

[0146] S421, leaf angle and light change data collection: data collected under different light intensities The lower blade angle and the corresponding transpiration rate , forming a correlation data set of leaf angle, light intensity and transpiration rate;

[0147] S422, Calculation of sensitivity of transpiration rate to light changes: Calculate the transpiration response index based on the formed correlation data set , to quantify the sensitivity of transpiration rate to changes in light intensity, expressed as:

[0148] ;

[0149] in, Indicates the change in transpiration rate, that is, the difference in transpiration rate before and after the change in light intensity. Indicates the change in light intensity, that is, the difference in light intensity under different lighting conditions;

[0150] S423, Dynamic adjustment of transpiration response index: In order to reflect the effect of leaf angle regulation, the leaf angle in the transpiration mechanism is regulated according to the leaf angle The impact on light absorption efficiency dynamically adjusts the transpiration response index, which is expressed as:

[0151] ;

[0152] in, is the adjusted transpiration response index, is the effect of leaf angle on light absorption efficiency. When the leaf angle is larger, light absorption decreases and the response index of transpiration rate also decreases.

[0153] Through the above content, the sensitivity of crop transpiration rate to light changes was quantified, and dynamically adjusted through the adjustment effect of leaf angle on the basis of the basic index. It can not only accurately reflect the water demand of crops under different light conditions, but also dynamically adapt to changes in leaf angles and improve the accuracy of drought resistance analysis. The adjusted transpiration response index provides a more reliable reference basis for optimizing crop water management and drought resistance strategies, which helps to use water resources scientifically and efficiently when environmental conditions change.

[0154] The stomatal regulation efficiency assessment in S43 includes:

[0155] S431, stomatal conductance data collection: real-time collection of stomatal conductance of corn and wheat at different air humidity and soil moisture levels , forming a dynamic data set of stomatal conductance changes with the environment;

[0156] S432, Stomatal Regulation Efficiency Calculation: Calculate stomatal regulation efficiency based on the dynamic data set formed , used to quantify the opening and closing regulation ability of stomata under water stress, expressed as:

[0157] ;

[0158] in, is the change in stomatal conductance, is the change in air humidity or soil moisture;

[0159] S433, Dynamic Regulation Analysis: Under the condition of limited water, combined with the analysis results of stomatal regulation efficiency, the self-protection mechanism of corn and wheat, that is, the ability of corn and wheat to reduce water loss under water stress, is determined. When the preset regulation threshold is exceeded, it indicates that the stomata have the ability to regulate, thereby reducing transpiration losses under water-limited conditions, protecting crop water balance, and improving crop drought tolerance;

[0160] The setting of the adjustment threshold specifically includes:

[0161] Baseline values ​​of ambient humidity and soil moisture: The adjustment threshold is set based on the moisture parameters (ambient humidity and soil moisture) in the crop's growth environment, and the base humidity is set. and baseline soil moisture , respectively represent the humidity and soil moisture levels of crops under normal growth environment;

[0162] Sensitivity of stomatal conductance change: According to the response degree of stomatal conductance to water changes under different environmental conditions, the average change rate (sensitivity) is calculated to determine the adjustment threshold and sensitivity. It is expressed as the average change of stomatal conductance with water content change, expressed as:

[0163] ;

[0164] in, Indicates The change in stomatal conductance during the measurement, Indicates The change in air humidity or soil moisture during the first measurement. is the number of measurements;

[0165] Adjustment threshold calculation: Combine the baseline value and sensitivity to set the adjustment threshold , expressed as:

[0166] ;

[0167] in, is the current ambient humidity, is the current soil moisture;

[0168] Through the above content, the opening and closing regulation ability of stomata under water stress conditions can be accurately measured. The SRE value quantifies the response degree of stomata to water changes, which can effectively judge the self-protection mechanism of crops under limited water conditions. It can dynamically analyze the water utilization regulation ability of crops in arid environments, help identify crops with stronger drought resistance, and provide decision-making support for scientific irrigation, thereby reducing water loss, maintaining the water balance of crops and improving the drought resistance of crops.

[0169] 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.

[0170] 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. A method for analyzing and monitoring drought tolerance data of corn and wheat in the field, characterized in that: The following steps are involved: S1, data collection: Collect environmental and physiological data of corn and wheat in the field based on multi-source sensors, including air temperature, air humidity, soil moisture, leaf angle, leaf temperature and stomatal conductance; S2, data preprocessing: preprocess the collected environmental and physiological data, including cleaning and standardization, and use multimodal data fusion technology to integrate environmental and physiological data into comprehensive data; S3, leaf angle regulation of transpiration mechanism: Analyze the impact of leaf angle changes of corn and wheat on light absorption and transpiration rate, optimize leaf angle in real time to adapt to changes in light intensity, and dynamically adjust transpiration thresholds based on light conditions and water requirements of corn and wheat, and predict water consumption of corn and wheat under different light conditions, including: S31, Analysis of the relationship between leaf angle and light intensity: By collecting leaf angle and corresponding transpiration rate data under different light conditions, the effect of leaf angle changes on light absorption efficiency and transpiration rate in the field environment is analyzed; S32, dynamic adaptive light regulation: Based on the results of the analysis of the relationship between leaf angle and light intensity, the changes in ambient light intensity are monitored in real time, and the leaf angle is dynamically optimized to maximize the photosynthesis efficiency under different light conditions; S33, Dynamic Control of Transpiration Threshold and Water Consumption Prediction: Based on the results of leaf angle and light adaptation, combined with the real-time water demand of corn and wheat, the transpiration threshold is dynamically set to predict water consumption under different light conditions. and light intensity , combined with the real-time water demand of corn and wheat, dynamically calculate the transpiration threshold , expressed as: ； in, is the dynamically set transpiration threshold, is the adjustment coefficient of light and water demand, which is used to balance the relationship between transpiration rate and light. For the current water requirements of corn and wheat, It indicates the effect of leaf angle on light absorption efficiency; S4, Drought tolerance analysis: Based on the integrated comprehensive data and combined with the analysis results of the leaf angle regulation transpiration mechanism, the drought tolerance of corn and wheat was evaluated, including water use efficiency, transpiration response index and stomatal regulation efficiency.

2. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 1, characterized in that: The data collection in S1 includes: S11, air temperature and humidity collection: placing temperature sensors and humidity sensors in the field environment to detect air temperature and humidity in real time; S12, soil moisture collection: soil moisture sensors were buried at different depths around the roots of corn and wheat to measure the moisture content of each soil layer; S13, leaf angle monitoring: optical angle sensors are installed around crop plants to monitor the angle changes of leaves and record the real-time positions of leaves under different lighting conditions; S14, blade temperature collection: monitor the surface temperature of the blade through an infrared temperature sensor to obtain the change of the blade temperature; S15, stomatal conductance measurement: the opening and closing status of crop stomata is measured by a stomatal conductance sensor.

3. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 1, characterized in that: The data preprocessing in S2 includes: S21, data cleaning: outlier detection and noise elimination are performed on the collected environmental and physiological data, and the IQR method is used to eliminate outliers; S22, data standardization: The Z-score standardization method was used to standardize the environmental and physiological data after cleaning; S23, multimodal data fusion: A weighted average fusion algorithm is used to integrate the standardized environmental and physiological data into comprehensive data.

4. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 1, characterized in that: The analysis of the relationship between the blade angle and the light intensity in S31 includes: S311, light intensity and transpiration rate collection: Under different light conditions, the light intensity is collected in real time through the transpiration sensor and light sensor. and the corresponding transpiration rate ; S312, calculate light absorption efficiency: calculate the light absorption efficiency of the blade based on the collected blade angle and light intensity ; S313, transpiration rate response analysis: Combine leaf angle and light absorption efficiency, and use a regression analysis model to calculate the effect of leaf angle on transpiration rate.

5. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 4, characterized in that: The dynamic adaptive adjustment of illumination in S32 includes: S321, real-time monitoring of light intensity: real-time monitoring of changes in light intensity in the field, obtaining the current light intensity value ; S322, blade angle optimization calculation: based on time The light intensity value at , combined with the target transpiration rate upper limit , calculate the time The optimal blade angle , and dynamically adjust the leaf angle based on the calculation results to maximize photosynthesis efficiency.

6. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 5, characterized in that: The dynamic regulation of transpiration threshold and water consumption prediction in S33 also includes water consumption prediction: based on the set transpiration threshold, predicting the water consumption of corn and wheat under different lighting conditions.

7. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 1, characterized in that: The drought tolerance analysis in S4 includes: S41, Water use efficiency assessment: By analyzing the integrated comprehensive data, the photosynthesis efficiency of crops under current water conditions is assessed. By calculating the water use efficiency of corn and wheat at different growth stages and light conditions, the adaptability and water use capacity of crops under drought stress are judged. S42, Calculation of transpiration response index: Based on the analysis results of the leaf angle regulation mechanism of transpiration, the transpiration response index of corn and wheat was calculated to quantify the sensitivity of transpiration rate to light changes; S43, stomatal regulation efficiency evaluation: By analyzing the stomatal conductance data, the stomatal regulation efficiency, that is, the ability of crops to regulate the opening and closing of stomata under water stress, is evaluated to determine the self-protection mechanism of crops under water-limited conditions.

8. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 7, characterized in that: The water use efficiency assessment in S41 includes: S411, Water use efficiency calculation: Based on the integrated comprehensive data, water use efficiency is calculated Assess crop photosynthesis efficiency under current water conditions; S412, Application of growth stage adjustment coefficient: Combine the growth stages of corn and wheat and set the water use efficiency adjustment coefficient for each stage. , to reflect the water demand characteristics of different growth stages; S413, Effect of light conditions on water use efficiency: Based on light intensity under different light conditions, , by setting the light adjustment factor To correct water use efficiency.

9. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 8, characterized in that: The transpiration response index calculation in S42 includes: S421, leaf angle and light change data collection: data collected under different light intensities The lower blade angle and the corresponding transpiration rate , forming a correlation data set of leaf angle, light intensity and transpiration rate; S422, Calculation of sensitivity of transpiration rate to light changes: Calculate the transpiration response index based on the formed correlation data set , to quantify the sensitivity of transpiration rate to changes in light; S423, Dynamic adjustment of transpiration response index: In order to reflect the effect of leaf angle regulation, the leaf angle in the transpiration mechanism is regulated according to the leaf angle The impact on light absorption efficiency and dynamic adjustment of the transpiration response index.

10. The method for analyzing and monitoring drought tolerance data of corn and wheat in the field according to claim 9, characterized in that: The stomatal regulation efficiency evaluation in S43 includes: S431, stomatal conductance data collection: real-time collection of stomatal conductance of corn and wheat at different air humidity and soil moisture levels , forming a dynamic data set of stomatal conductance changes with the environment; S432, Stomatal Regulation Efficiency Calculation: Calculate stomatal regulation efficiency based on the dynamic data set formed , used to quantify the opening and closing regulation ability of stomata under water stress; S433, Dynamic Regulation Analysis: Under the condition of limited water, combined with the analysis results of stomatal regulation efficiency, the self-protection mechanism of corn and wheat, that is, the ability of corn and wheat to reduce water loss under water stress, is determined. When the preset adjustment threshold is exceeded, it means that the pores have the ability to adjust.

Images