Crop high-luminous-efficiency phenotype dynamic grading method fusing multi-environment response and vertical distribution analysis

Abstract

The invention provides a crop high-luminous-efficiency phenotype dynamic grading method fusing multi-environment response and vertical distribution analysis, and relates to the technical field of high-luminous-efficiency phenotype dynamic grading. According to the method, experimental groups and non-stress control groups of different single stress environments are set, and environment stress indexes are calculated; obtaining multi-source phenotypic data of crop canopies; vertically dividing the canopy into an upper layer, a middle layer and a lower layer based on the three-dimensional point cloud data, and extracting the spectral index and the canopy temperature of each layer; calculating a light energy utilization efficiency index of each experimental group in combination with the environmental stress index; and finally realizing dynamic grading of the light energy utilization efficiency based on global standardization. According to the invention, through multi-environment response analysis and canopy vertical analysis, accurate and dynamic evaluation of the high-photosynthetic-efficiency capability of the crops is realized, and effective technical support is provided for high-photosynthetic-efficiency breeding and cultivation management of the crops.

Description

Technical Field

[0001] This invention relates to the field of dynamic grading technology for high light efficiency phenotypes, specifically to a dynamic grading method for high light efficiency phenotypes of crops that integrates multiple environmental responses and vertical distribution analysis. Background Technology

[0002] With global climate change and population growth, agricultural production faces increasingly severe resource and environmental pressures. Improving crop light energy utilization efficiency has become a research hotspot in agriculture, especially in analyzing the physiological response mechanisms of crops under different environmental conditions through phenotyping techniques. Phenotypic analysis of high crop light efficiency involves not only the overall photosynthetic characteristics of the canopy but also the heterogeneity of its vertical structure and the impact of environmental stresses (such as water, temperature, nitrogen, and light) on light capture and conversion processes. Traditional methods often rely on single sensors or measurements at the overall canopy scale, making it difficult to capture the dynamic responses at different levels within the canopy. Furthermore, they lack quantitative assessment models for the synergistic effects of multiple environmental stress factors, limiting the development of high-efficiency breeding and precision cultivation.

[0003] Currently, the analysis of crop light efficiency phenotypes mainly relies on remote sensing technology and spectral index monitoring. However, most methods only focus on the overall canopy response, neglecting the regulatory role of vertical structure on light energy distribution. Furthermore, existing studies are often conducted under single environmental conditions, lacking the ability to synergistically analyze multiple stress factors, making it difficult for assessment results to comprehensively reflect crop adaptability in real, complex environments. For example, while the combination of canopy temperature and spectral index can indirectly reflect light energy use efficiency, it does not consider the differentiated responses of different canopy layers to stress, and the quantification of environmental stress intensity largely depends on subjective experience, lacking a unified index standard. These shortcomings limit the accuracy and universality of existing methods in dynamic grading and breeding applications.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a dynamic classification method for crop high light efficiency phenotypes that integrates multiple environmental responses and vertical distribution analysis, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A dynamic classification method for crop high photosynthetic efficiency phenotypes that integrates multiple environmental responses and vertical distribution analysis includes the following steps: Step 1: Select the target crop, set up different single stress environments to cultivate the target crop as experimental groups, and set up a non-stress environment to cultivate the crop as a control group. Introduce the environmental stress index to characterize the intensity of environmental stress in each experimental group. Step 2: During the same growth period, acquire multi-source phenotypic data of the target crop canopy in the control group and the experimental group. The multi-source phenotypic data includes hyperspectral data, thermal infrared data and three-dimensional point cloud data. Step 3: Based on the 3D point cloud data, the vertical layer division of the crop canopy is carried out. The canopy of each target crop is divided into three layers in the vertical direction: upper, middle and lower. The spectral index and canopy temperature corresponding to each layer are extracted from the hyperspectral data and thermal infrared data respectively. Step 4: Calculate the light energy utilization efficiency index based on the environmental stress index of the target crop under a single stress environment, as well as the spectral index and canopy temperature at each level, to characterize the light energy utilization efficiency of the target crop in each experimental group at each key growth stage. Step 5: For the same growth stage, based on the light energy utilization efficiency index of all experimental groups and the control group, perform global standardization and comprehensive classification to complete the dynamic classification of the high light efficiency phenotype of the target crop in each experimental group.

[0007] Furthermore, the control group and experimental group were specifically set up as follows: The control group, which is set up in the stress-free environment, has environmental parameters including at least: temperature, light intensity, soil volumetric water content, and nitrogen fertilizer application rate. The single stress environment includes a water stress group, a high temperature stress group, a nitrogen stress group, and a low light stress group. The water stress group is set with a soil volumetric water content that is half that of the control group, and the other environmental parameters are the same as the control group. The high temperature stress group is set with a temperature that is 10°C higher than the control group, and the other environmental parameters are the same as the control group. The nitrogen stress group is set with a nitrogen fertilizer application rate that is one-third lower than that of the control group, and the other environmental parameters are the same as the control group. The low light stress group is set with a light intensity that is half that of the control group, and the other environmental parameters are the same as the control group.

[0008] Furthermore, calculating the environmental stress index specifically includes: An environmental stress index is introduced to characterize the intensity of environmental stress in each experimental group. The specific calculation formula is as follows: ; in, Let i be the environmental stress index of the i-th experimental group. Let be the absolute difference between the temperature of the i-th experimental group and the temperature of the control group. The temperature of the control group Let be the absolute difference between the soil volumetric water content of the i-th experimental group and the soil volumetric water content of the control group. This represents the soil volumetric water content of the control group. Let be the absolute difference between the nitrogen fertilizer application rate of the i-th experimental group and the nitrogen fertilizer application rate of the control group. The temperature of the control group Let be the absolute difference between the light intensity of the i-th experimental group and the light intensity of the control group. The light intensity for the control group is , and i is the index of the experimental group. These are the weighting coefficients for each ratio; Based on the magnitude of the stress intensity index, the experimental groups were divided into stress levels, specifically as follows: when At that time, it was classified as mild stress. At that time, it was classified as moderate stress. At that time, it was classified as severe stress.

[0009] Furthermore, the collection of the multi-source phenotypic data specifically includes: The entire growth cycle of the target crop is divided into the seedling stage, the growth stage, and the fruiting stage. During the same growth stage, a UAV remote sensing platform integrating a hyperspectral camera, a thermal infrared camera, and a three-dimensional lidar is used to simultaneously collect multi-source phenotypic data of the target crop canopy above the canopy. One day is selected as the measurement day during each growth stage. Data is collected n times during the photosynthetically active period of the measurement day, with the time interval between two adjacent collections being the same, so as to obtain n collection times and their corresponding multi-source phenotypic data; the light saturation point of the target crop is determined. The photosynthetically active period is set as the time period from when the photosynthetically active radiation first reaches 80% of the light saturation point of the target crop after sunrise each day until the photosynthetically active radiation drops below that value before sunset. The hyperspectral data was acquired using a hyperspectral camera, the thermal infrared data was acquired using a thermal infrared camera, and the three-dimensional point cloud data was acquired using a three-dimensional lidar.

[0010] Furthermore, the vertical hierarchical division specifically includes: The three-dimensional point cloud data first collected during the effective photosynthetic period of the measurement day is used as the reference point cloud. All subsequent three-dimensional point cloud data are registered with the reference point cloud through the iterative nearest point algorithm, and the vertical hierarchy is divided based on the registered point cloud data. When acquiring 3D point cloud data using 3D LiDAR, the point cloud density should be no less than 100 points / square meter to reconstruct the 3D structure of the target crop canopy and perform hierarchical division. After point cloud registration is completed, the canopy hierarchy remains unchanged. Subsequent acquisitions of hyperspectral and thermal infrared data are all based on this fixed hierarchical mapping relationship to extract the spectral indices and canopy temperature of each level. The specific hierarchical division method is as follows: For the point cloud data of each target crop, calculate the height of all its points relative to the ground, and identify the highest and lowest point heights. The difference between the highest and lowest point heights represents the height range. Based on these height ranges, divide the target crop canopy into three layers. The point cloud data is divided into upper layers, and The point cloud data within the range is divided into a middle layer, and The point cloud data is divided into lower layers, where H is the height of the point cloud data. The height of the highest point. This is the height of the lowest point.

[0011] Furthermore, obtaining the spectral index and canopy temperature specifically includes: After dividing the canopy into layers using 3D point cloud data, three corresponding mask regions were generated: an upper mask region, a middle mask region, and a lower mask region. Hyperspectral data was overlaid with the upper canopy mask region. For all pixels falling within the upper mask region, their reflectance values ​​at the 531nm and 570nm wavelengths were calculated. Based on these reflectance values, the photochemical reflectance index of each pixel in the upper mask region was calculated using the following formula: ; in, Photochemical reflectance index This indicates the reflectivity at the 531nm wavelength. This indicates the reflectivity at the 570nm wavelength. The average photochemical reflectance index of all pixels is calculated and used as the photochemical reflectance index of the upper canopy at the corresponding time. Based on n acquisition times, the acquisition times are connected to form a line graph showing the average photochemical reflectance index changing over time. The area enclosed by this line graph and the time axis is calculated to represent the integral value of the photochemical reflectance index of the upper canopy during the photosynthetically active period, and this integral value is used as the spectral index of the upper canopy during the midday photosynthetically active period. The area is calculated using the trapezoidal rule for numerical integration, and the calculation formula is as follows: ; in, The spectral index of the upper canopy, The photochemical reflectance index of the upper canopy at the k-th data acquisition time is given. The average photochemical reflectance index of the upper canopy at the (k+1)th data acquisition time is given. The time interval between two consecutive data collection times is k, where k is the index of the data collection time and n is the number of data collection times, and k∈[1,n]. Similarly, the spectral indices of the middle canopy and the lower canopy were obtained and measured during the effective photosynthetic period at midday. Radiometric calibration and atmospheric correction were performed on the acquired thermal infrared data to obtain a preliminarily corrected canopy brightness temperature image. From the canopy brightness temperature image, the pixel region that overlaps with the upper mask region in the point cloud data was extracted. The average temperature value of all pixels in this region was calculated, and the ambient temperature was obtained and corrected to obtain the average temperature used to characterize the actual physiological state of the canopy. The correction formula is as follows: ; in, The average temperature of the upper canopy. This is the average temperature value of all pixels in the upper canopy. These are empirical correction coefficients. The ambient temperature; Based on n data collection times, these times are connected to form a line graph showing the average temperature changing over time. The area enclosed by this line graph and the time axis is used to represent the integral value of the average temperature during the photosynthetically active period. This integral value is then used to measure the canopy temperature of the upper canopy during the midday photosynthetically active period. The area is also calculated using the trapezoidal rule for numerical integration, as shown in the following formula: ; in, The temperature of the upper canopy layer. The average temperature of the upper canopy at the k-th sampling time is [value missing]. The average temperature of the upper canopy at the (k+1)th sampling time; Similarly, the canopy temperatures of the middle layer and the lower layer were obtained and measured during the effective photosynthetic period at midday.

[0012] Furthermore, calculating the light energy utilization efficiency index specifically includes: Based on the canopy temperature of the target crops in different layers of each experimental group, the temperature difference ratio between each experimental group and the control group at the same layer was calculated using the following formula: ; in, This represents the ratio of the upper canopy temperature difference between the i-th experimental group and the control group. Let be the canopy temperature of the upper canopy in the i-th experimental group. The temperature of the upper canopy layer in the control group; Similarly, the temperature difference ratios of the middle canopy and the lower canopy were obtained for the i-th experimental group and the control group, respectively. The temperature difference ratio is normalized and mapped to a coefficient characterizing the degree of stress at that level. The calculation formula is as follows: ; in, This represents the stress response coefficient of the upper canopy in the i-th experimental group; Similarly, the stress response coefficients of the middle canopy and the lower canopy in the i-th experimental group are obtained, and are expressed as follows: , ; The light energy utilization efficiency index is calculated based on the spectral index, stress response coefficient, and environmental stress index at each level, using the following formula: ; in, This represents the light energy utilization efficiency index of the i-th experimental group. This represents the spectral index of the upper canopy within the i-th experimental group. This represents the spectral index of the middle canopy within the i-th experimental group. Spectral indices of the lower canopy in the i-th experimental group These are the weight coefficients for the corresponding items. This represents the environmental stress index of the i-th experimental group.

[0013] Furthermore, the comprehensive grading specifically includes: The light energy utilization efficiency index of all experimental and control groups at the same growth stage was collected to form several global datasets at the same growth stage. The mean and standardization of each global dataset were calculated to standardize the light energy utilization efficiency index of all experiments in each global dataset. The light energy utilization efficiency index of the target crop at all growth stages of each experimental group was weighted and summed to obtain the light energy utilization efficiency index of the target crop of the corresponding experimental group throughout the entire growth cycle, so as to characterize the comprehensive light energy utilization efficiency of the target crop of the corresponding experimental group throughout the entire growth cycle. The target crops in each experimental group were classified into five levels based on their high photosynthetic efficiency, from strongest to weakest. The specific criteria for classification were as follows: If... If it is classified as the optimal level, then it is considered as such. If it is classified as excellent, then it is classified as good. If it is classified as medium level, then it is considered medium level. If it is, it is classified as a poor grade. Then it is divided into range levels, among which, Let be the light energy utilization efficiency index of the target crop in the i-th experimental group throughout the entire growth cycle.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This method effectively overcomes the limitations of traditional crop light-efficiency phenotypic analysis by integrating multi-environmental response and vertical distribution analysis, achieving precise and dynamic assessment of crop light energy utilization efficiency. By introducing an environmental stress index to quantify the intensity of multiple stresses and combining it with a canopy vertical stratification strategy, this method can accurately capture the differentiated physiological responses of the upper, middle, and lower layers of the crop canopy under different environmental stresses, significantly improving the accuracy and reliability of the stratification results. Ultimately, this method provides a more comprehensive and scientific basis for the breeding of high-light-efficiency crop varieties and stress-resistant cultivation management, and has strong practical application value. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the overall method flow of the present invention; Figure 2 This is a schematic diagram of the environmental stress index and light energy utilization efficiency index in the first set of sample data of this invention; Figure 3 This is a schematic diagram of the upper canopy spectral index and light energy utilization efficiency index in the second set of sample data of this invention; Figure 4 This is a schematic diagram of the spectral index and light energy utilization efficiency index of the middle canopy in the second set of sample data of this invention; Figure 5 This is a schematic diagram of the lower canopy spectral index and light energy utilization efficiency index in the second set of sample data of this invention; Figure 6 This is a schematic diagram of the environmental stress index and light energy utilization efficiency index in the second set of sample data of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0017] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] Example: Please see Figures 1-6 The present invention provides a technical solution: A dynamic classification method for crop high photosynthetic efficiency phenotypes that integrates multiple environmental responses and vertical distribution analysis includes the following steps: Step 1: Select the target crop, set up different single stress environments to cultivate the target crop as experimental groups, and set up a non-stress environment to cultivate the crop as a control group. Introduce the environmental stress index to characterize the intensity of environmental stress in each experimental group. In this embodiment, setting up a control group and an experimental group specifically includes: This method is applicable to various crops or cash crops, such as, but not limited to, corn, rice, and soybeans. A control group with a stress-free environment is set up, and its environmental parameters include at least: temperature, light intensity, soil volumetric water content, and nitrogen fertilizer application rate. The single stress environment includes a water stress group, a high temperature stress group, a nitrogen stress group, and a low light stress group. The water stress group is set with a soil volumetric water content that is half that of the control group, and the other environmental parameters are the same as the control group. The high temperature stress group is set with a temperature that is 10°C higher than the control group, and the other environmental parameters are the same as the control group. The nitrogen stress group is set with a nitrogen fertilizer application rate that is one-third lower than that of the control group, and the other environmental parameters are the same as the control group. The low light stress group is set with a light intensity that is half that of the control group, and the other environmental parameters are the same as the control group.

[0019] The control group should have at least three biological replicates to eliminate individual differences and random errors. Each replicate should contain no fewer than 20 healthy, uniformly growing crop seedlings to ensure a sufficient statistical sample size for subsequent destructive sampling and measurements. Similarly, for each experimental group under a single environmental stress, at least three biological replicates should be set up for each stress group, with the same number of plants in each group as the control group.

[0020] When corn is used as the target crop, the environmental parameters for the control group corn are set as follows: temperature range of 25-28℃, light intensity range of 800-1000 ppm. The soil volumetric water content was set at 70%–80% of field capacity, and the nitrogen fertilizer application rate was set at 180–220 kg N / ha. Therefore, the environmental parameters for the experimental maize groups were set as follows: soil volumetric water content for the water stress group was set at 35%–40% of field capacity; temperature for the high temperature stress group was set at 35–38℃; nitrogen fertilizer application rate for the nitrogen stress group was set at 60–70 kg N / ha; and light intensity for the low light stress group was set at 400–500 kDa. .

[0021] When rice is used as the target crop, the environmental parameters for the control group rice are set as follows: temperature range of 25-30℃, light intensity range of 600-800 ppm. The soil volumetric water content was set at 80%–90% of field capacity, and the nitrogen fertilizer application rate was set at 120–150 kg N / ha. The environmental parameters for the experimental rice groups were as follows: Soil volumetric water content for the water stress group was set at 40%–45% of field capacity; temperature for the high-temperature stress group was set at 35–40℃; nitrogen fertilizer application rate for the nitrogen stress group was set at 40–50 kg N / ha; and light intensity for the low-light stress group was set at 300–400 kDa. .

[0022] When soybeans are used as the target crop, the environmental parameters for the control group soybeans are set as follows: temperature range of 22-25℃, light intensity range of 500-700 ppm. The soil volumetric water content was set at 60%–70% of field capacity, and the nitrogen fertilizer application rate was set at 50–80 kg N / ha. Therefore, the environmental parameters for the soybean experimental groups were set as follows: For the water stress group, the soil volumetric water content was set at 30%–35% of field capacity; for the high temperature stress group, the temperature was set at 32–35℃; for the nitrogen stress group, the nitrogen fertilizer application rate was set at 15–25 kg N / ha; and for the low light stress group, the light intensity was set at 250–350 nm. .

[0023] In addition, the control group and the experimental group must be the same in setting other environmental parameters. For example, daytime / nighttime temperature can be set to a constant temperature or a temperature variation cycle that conforms to natural laws (such as 25℃ / 20℃ at night); field water holding capacity needs to be measured in the original growing soil of each target crop using the ring cutter method before the experiment; nitrogen fertilizer application rate refers to the amount of pure nitrogen applied, and the fertilizer should be applied as a base fertilizer in one application or applied in multiple applications according to the growth stage; relative humidity is set to 60% to 80%; carbon dioxide concentration is set to 400-450 ppm, etc.

[0024] In the implementation of this method, significant differences were found in the environmental parameters set for maize, rice, and soybean. These differences were not arbitrary but rather based on the distinct biological characteristics, physiological needs, and unique environmental adaptation strategies developed during their long evolution in their native habitats. Specifically, these differences mainly stem from the following aspects: First, the differences in crop physiological types and photosynthetic pathways are the core reason for the differences in environmental parameter settings. Maize is a C4 plant, and its photosynthesis has… The concentration mechanism allows C4 plants to exhibit higher photosynthetic and water use efficiency under high temperature and high light intensity conditions. This determines that the optimal temperature (25-28°C) and light intensity (800-1000 μmol / m² / s) for the maize control group are higher than those for C3 plants rice and soybean. C4 plants have a stronger ability to utilize strong light, so their threshold for low light stress is correspondingly higher. Conversely, soybean, as a C3 plant, can reach its light saturation point under relatively mild light and temperature conditions. Excessive light and temperature may cause photoinhibition, so its optimal parameters are set relatively low.

[0025] Secondly, the fundamental differences in water requirements and growth environment directly determine the method of setting water stress. Rice is a typical semi-aquatic crop with well-developed root aeration tissue, making it suitable for growth in flooded environments. Therefore, the control group is designed to maintain a water layer, and its water stress is usually simulated through drought treatment (soil moisture content reduced to 40%–45% of field capacity). Corn and soybean, on the other hand, are dryland crops, and their optimal soil moisture content is measured as a percentage of field capacity. Soybeans, in particular, are extremely sensitive to water balance during the flowering and pod-setting stages. Too much water can easily lead to root rot, while too little water can cause flower and pod drop. Therefore, their optimal water content and stress settings reflect their higher requirements for soil aeration.

[0026] Finally, the difference in nutrient requirements and nitrogen fixation capacity is the key to the different nitrogen stress settings. As a high-yielding cereal crop, maize has a large demand for nitrogen fertilizer, and its high yield is highly dependent on external nitrogen fertilizer supplementation. Therefore, the control group received the highest nitrogen application rate, and the corresponding nitrogen stress setting was the most stringent. Soybeans, as a legume, can fix nitrogen through symbiosis with rhizobia and can obtain most of their required nitrogen from the air. Therefore, its control group required the lowest amount of external nitrogen fertilizer. After applying the same proportion of nitrogen stress, although its absolute nitrogen deficiency was not as severe as that of maize, it better simulated its actual growth state.

[0027] In this embodiment, calculating the environmental stress index specifically includes: An environmental stress index is introduced to characterize the intensity of environmental stress in each experimental group. The specific calculation formula is as follows: ; in, Let i be the environmental stress index of the i-th experimental group. Let be the absolute difference between the temperature of the i-th experimental group and the temperature of the control group. The temperature of the control group Let be the absolute difference between the soil volumetric water content of the i-th experimental group and the soil volumetric water content of the control group. This represents the soil volumetric water content of the control group. Let be the absolute difference between the nitrogen fertilizer application rate of the i-th experimental group and the nitrogen fertilizer application rate of the control group. The temperature of the control group Let be the absolute difference between the light intensity of the i-th experimental group and the light intensity of the control group. The light intensity for the control group is , and i is the index of the experimental group. These are the weighting coefficients for each ratio; Different environmental stressors have significantly different effects on crop physiological and biochemical processes, and therefore their corresponding weighting relationships also differ. For example, when maize is the target crop, the weighting coefficients can be related as follows: As a C4 crop, maize is heat-tolerant, but its reproductive growth (especially during the tasseling and silking stages) is extremely sensitive to high temperatures. Short-term high temperatures can lead to pollen inactivation and poor fertilization, causing severe yield reduction; therefore, temperature is the primary stress factor. Maize plants are tall with high biomass and high water consumption, especially during the "large trumpet stage" to the grain-filling stage, which is a critical period for water demand. Drought at this time can cause leaf wilting, a sharp drop in photosynthesis, and insufficient grain filling, significantly impacting yield. Therefore, water stress is considered the second most important factor. Maize is a nitrogen-loving crop and is sensitive to nitrogen fertilizer. Nitrogen deficiency significantly affects leaf area, chlorophyll synthesis, and ear differentiation, but its effects are relatively slow compared to water and heat stress. As a C4 plant, maize has a high light saturation point and high efficiency in utilizing strong light, but its tolerance to low light is poor in the lower canopy or during cloudy / rainy weather, affecting overall biomass, but usually not the primary cause of crop failure.

[0028] When rice is the target crop, the weighting coefficients can be ranked as follows: Rice is a semi-aquatic crop, and its root system, aerenchyma, and metabolism are all adapted to flooded environments. Water is its most critical ecological factor. Drought during both the tillering and booting stages is devastating. Simultaneously, deep flooding also constitutes stress. Therefore, water's weight is significantly higher than other factors. High temperatures during the heading and flowering stages severely affect pollen fertility and seed setting rate, leading to an increase in empty grains, and have become a significant meteorological disaster in major rice-growing areas in recent years. Rice has a high nitrogen requirement, and nitrogen fertilizer management directly affects the number of tillers and yield. However, excessive nitrogen application can easily lead to excessive vegetative growth, lodging, and exacerbated pests and diseases; therefore, its stress relationships are complex, and its weight should not be too high. Rice has a certain degree of shade tolerance, but continuous cloudy and rainy weather during the reproductive growth period affects the accumulation of photosynthetic products, leading to a decrease in thousand-grain weight. Its impact is usually less than other stresses.

[0029] When soybeans are the target crop, the weighting coefficients can be ranked as follows: Soybeans are sensitive to water, especially during the flowering and pod-setting stage, which is a critical period for water demand. Drought at this time ("neck-killing drought") will cause a large number of flowers and pods to fall off, leading to severe yield reduction; while excessive water will easily cause root rot and soil hypoxia. Therefore, water has the highest weight. Soybeans are short-day C3 crops with low photosynthetic efficiency. The lower leaves of the canopy are often in a state of light limitation. Insufficient light during the flowering and grain-filling stages will significantly reduce the synthesis of photosynthetic products, directly affecting the number of pods and the weight of 100 grains. Therefore, the weight of light needs to be significantly increased. High temperature and dryness during the flowering period will affect pollen viability and increase transpiration, leading to flower drop. However, compared with rice and corn, its absolute threshold for heat stress is higher, and the impact is relatively less. As a legume crop, soybeans can fix nitrogen in symbiosis with rhizobia and can obtain about 50% to 60% of their nitrogen needs from the atmosphere. Therefore, its sensitivity to soil nitrogen deficit is relatively low, and the stress effect of exogenous nitrogen fertilizer should be set to the lowest weight.

[0030] Based on the magnitude of the stress intensity index, the experimental groups were divided into stress levels, specifically as follows: when At that time, it was classified as mild stress. At that time, it was classified as moderate stress. At that time, it was classified as severe stress.

[0031] when When the overall deviation of various environmental factors is relatively small, crops can usually compensate effectively by activating their own protective physiological mechanisms. Although there are measurable changes in phenotypic indicators such as photosynthetic rate and growth rate, the decline is limited, and no irreversible damage occurs; therefore, this is defined as mild stress. When the stress level reaches 0.3, it indicates that the environmental conditions have deviated significantly from optimal conditions, the crop's protective mechanisms have been destroyed, irreversible and severe damage has begun to occur in the cell structure, photosynthetic organs have collapsed, growth has almost stopped, and even death has occurred. Therefore, it is clearly classified as severe stress. The range of 0.3 to 0.7 is the critical stage in which crops transition from reversible compensation to irreversible damage. Physiological metabolic disorders intensify, phenotypic indicators decline significantly, and this range is defined as moderate stress, which can accurately capture this dynamic process.

[0032] The environmental stress index, as the dependent variable, quantifies the overall intensity of abiotic stress environments experienced by crops. Its value directly reflects the overall degree to which the target crop's growth environment deviates from its optimal conditions. The technical advantage of this index lies in integrating physical stress factors of different natures and units into a unified, comparable quantitative indicator through mathematical methods, thus providing a precise data foundation for the subsequent objective and standardized classification of stress levels. The independent variable is the relative deviation of each environmental factor from the control group's environmental factors, rather than the absolute difference. This is because the intensity of a crop's physiological response to stress depends primarily on the proportion of change rather than the absolute value. For example, for a crop with an optimum temperature of 25℃, an increase of 5℃ will produce drastically different heat stress effects compared to a crop with an optimum temperature of 15℃, but the relative changes of 20% for the former and 33% for the latter are more comparable.

[0033] In the formula for calculating the environmental stress index, the index exhibits a clear positive correlation with each independent variable. An increase in the relative deviation of each independent variable, i.e., each environmental factor, directly reflects that the specific environmental conditions are further deviating from the optimal state for crop growth, and the stress intensity is intensifying individually. For example, an increase in temperature-related deviation indicates an increase in the intensity of heat or cold stress; an increase in the relative deviation of soil volumetric water content represents a deepening of drought or waterlogging stress. An increase in the relative deviation of any environmental factor from its optimal value will lead to an increase in the value of the corresponding term within the square root, thereby causing a corresponding increase in the environmental stress index. This means that the total environmental load on crop physiological functions is increasing, and its growth, photosynthesis, and other metabolic processes are more significantly inhibited and disturbed overall. Conversely, the closer an environmental factor is to its optimal state, the smaller its relative deviation, and the smaller its contribution to the environmental stress index value.

[0034] Step 2: During the same growth period, acquire multi-source phenotypic data of the target crop canopy in the control group and the experimental group. The multi-source phenotypic data includes hyperspectral data, thermal infrared data and three-dimensional point cloud data. In this embodiment, collecting the multi-source phenotypic data specifically includes: The entire growth cycle of the target crop is roughly divided into the seedling stage, the growth stage, and the fruiting stage. However, the division of the entire growth cycle varies slightly for different target crops. For example, the entire growth cycle of corn includes germination, seedling, leaf growth, seedling stage, flower bud differentiation, flowering, and maturity; the entire growth cycle of rice includes germination, seedling, tillering, jointing, flower bud differentiation, flowering, grain filling, and maturity; and the entire growth cycle of soybean includes germination, seedling, budding, flowering, pod formation, and maturity. This method, for the purpose of standardizing the growth cycle, roughly divides the entire growth cycle of the target crop into three stages: the seedling stage, the growth stage, and the fruiting stage. For example, when corn is the target crop, the germination period and seedling period are classified as the seedling period, the leaf growth period, seedling period and flower bud differentiation period are classified as the growth period, and the flowering period and maturity period are classified as the grain-filling period. When rice is the target crop, the germination period and seedling period are classified as the seedling period, the tillering period, jointing period and flower bud differentiation period are classified as the growth period, and the flowering period, grain-filling period and maturity period are classified as the grain-filling period. When soybean is the target crop, the germination period and seedling period are classified as the seedling period, the budding period and flowering period are classified as the growth period, and the pod-filling period and maturity period are classified as the grain-filling period.

[0035] During the same growth period, a drone remote sensing platform integrating a hyperspectral camera, a thermal infrared camera, and a three-dimensional lidar was used to simultaneously collect multi-source phenotypic data of the target crop canopy above the canopy.

[0036] Hyperspectral cameras are used to acquire hyperspectral image data of the target crop canopy. Their spectral range should cover 400-2500 nm, including visible, infrared, near-infrared, and short-wave infrared bands, with a spectral resolution better than 10 nm. This data is used to extract fine spectral features related to photosynthetic pigment content, water content, nitrogen status, and light use efficiency. Thermal infrared cameras are used to acquire thermal infrared image data of the target crop canopy. Their operating band is typically 8-14 nm. Temperature measurement accuracy should be better than ±1℃, and spatial resolution should meet the requirements for canopy temperature variation detection. This data is used to retrieve canopy temperature or leaf temperature and is a key indicator for characterizing crop water and heat stress. Three-dimensional lidar is used to acquire three-dimensional point cloud data of the target crop canopy. A multi-echo or full-waveform lidar system should be used, capable of penetrating the canopy to obtain multi-layered structural information.

[0037] One day is selected as the measurement day during each growth stage. Data is collected n times during the photosynthetically active period of the measurement day, with the time interval between two adjacent collections being the same, so as to obtain n collection times and their corresponding multi-source phenotypic data; the light saturation point of the target crop is determined. The photosynthetically active period is set as the time period from when the photosynthetically active radiation first reaches 80% of the light saturation point of the target crop after sunrise each day until the photosynthetically active radiation drops below that value before sunset. Measurement dates can be randomly selected during any growth stage, but should be avoided immediately after periods of severe environmental stress (such as sudden heavy rain or high temperatures), as well as the first and last days of a corresponding growth stage, to eliminate errors caused by extreme conditions. This will better reflect the conditions throughout the entire growth period. Based on the identified target crop, relevant literature should be consulted to obtain the light saturation point for that crop; for example, the light saturation point for corn is 1800-2200 nm. The light saturation point of rice is 1000-1500. The light saturation point of soybeans is 800-1300. The specific light saturation point varies depending on the actual growth stage and variety of the target crop. Measurements should be taken at least three times during the photosynthetically active period of the measurement day. The interval between events should take into account the drone's endurance, data volume, and the temporal variation of the crop's physiological response. Each interval can be 1 hour or 2 hours.

[0038] The light saturation point is the point at which light and effective radiation reach a sufficient level to maximize the rate of photosynthesis. Generally, at this point, plant photosynthesis is no longer limited by light intensity. Choosing 80% of the light saturation point as the starting point means that under these light conditions, plants can still effectively photosynthesize. Although the photosynthetic rate reaches its maximum at the light saturation point, plants can still maintain high photosynthetic efficiency under 80% light intensity. Therefore, 80% of the light saturation point is chosen as the threshold for the start and end of the effective photosynthetic period.

[0039] Step 3: Based on the 3D point cloud data, the vertical layer division of the crop canopy is carried out. The canopy of each target crop is divided into three layers in the vertical direction: upper, middle and lower. The spectral index and canopy temperature corresponding to each layer are extracted from the hyperspectral data and thermal infrared data respectively. In this embodiment, the vertical hierarchical division specifically includes: The first 3D point cloud data acquired during the effective photosynthetic period of the measurement day was used as the reference point cloud. Subsequent acquisitions of 3D point cloud data were registered with the reference point cloud using an iterative nearest-point algorithm and its variants. This algorithm iteratively calculates and finds the rigid body transformation matrix (including rotation matrix and translation vector) that minimizes the sum of distances between corresponding points in the two point clouds, thus achieving spatial alignment of the point clouds. Vertical hierarchical division was then performed based on the registered point cloud data.

[0040] To accurately reconstruct the complex three-dimensional structure of the crop canopy and perform fine-grained hierarchical division, the point cloud density acquired using a three-dimensional lidar is no less than 100 points / square meter. This density ensures that the leaf structures at the top and middle of the canopy are clearly presented, avoiding hierarchical division errors caused by sparse point clouds. After point cloud registration, a one-time vertical hierarchical division is performed based on the registered reference point cloud. This division result serves as a fixed spatial framework for data extraction at all times throughout the day. Subsequent acquisitions of hyperspectral and thermal infrared data are all based on this fixed hierarchical division mapping relationship to extract spectral indices and canopy temperatures for each level. The specific hierarchical division method is as follows: First, ground points are separated from the point cloud using ground point filtering algorithms (such as Fabric Simulation Filtering (CSF) and Random Sample Consensus (RANSAC), and a digital elevation model is fitted. The Z-coordinate value of each point is subtracted from its corresponding ground elevation value to obtain the absolute height of each point relative to the ground. All point clouds belonging to crops are traversed to find the highest and lowest point heights. The highest point is typically the tip of the ear or leaf at the top of the canopy, and the lowest point is typically an older leaf or the base of the stem close to the ground. The difference between the highest and lowest point heights represents the height range. Based on these height ranges, the target crop canopy is divided into three layers. The point cloud data is divided into upper layers, and The point cloud data within the range is divided into a middle layer, and The point cloud data is divided into lower layers, where H is the height of the point cloud data. The height of the highest point. This is the height of the lowest point.

[0041] In this embodiment, obtaining the spectral index and canopy temperature specifically includes: Based on the above partitioning rules, three binary mask images are generated in the projection coordinate system of the reference point cloud, representing the projection areas of the upper, middle, and lower canopies on the two-dimensional plane. These masks will be used to extract hyperspectral and thermal infrared information for the corresponding layers. They are the upper mask region, the middle mask region, and the lower mask region, respectively. The hyperspectral data is superimposed on the upper canopy mask region. For all pixels falling within the upper mask region, their reflectance values ​​at the 531nm and 570nm bands are calculated. Based on the reflectance values ​​in these two bands, the photochemical reflectance index of each pixel in the upper mask region is calculated using the following formula: ; in, Photochemical reflectance index This indicates the reflectivity at the 531nm wavelength. This indicates the reflectivity at the 570nm wavelength. The average photochemical reflectance index (PRI) of all pixels is calculated and used as the PRI of the upper canopy at the corresponding time. The PRI is an indicator sensitive to subtle physiological changes in leaves. The PRI value of a single pixel is easily affected by various factors, such as leaf angle reflecting solar flares, the possibility of a single pixel being mixed with leaf and soil background, and sensor random noise. By calculating the statistical average of a large number of pixels, these random errors and outliers can be effectively smoothed out, making the final index value more stable and reliable in reflecting the overall average photosynthetic physiological state of the entire upper canopy at that time, rather than the accidental situation of individual leaves. Furthermore, the study focuses more on population performance than on individual leaves. For example, the average yield and average leaf area index of a field are measured, rather than a single plant. Similarly, the purpose of this invention is to classify the high photosynthetic efficiency phenotype of crop populations. Therefore, using the average value of the entire canopy layer as the unit of analysis ensures that the conclusions are macroscopically representative.

[0042] Based on n data acquisition times, these times are connected to form a line graph showing the average photochemical reflectance index as a function of time. The area enclosed by this line graph and the time axis is calculated to represent the integral value of the photochemical reflectance index of the upper canopy during the photosynthetically active period. This integral value is then used as the spectral index of the upper canopy during the midday photosynthetically active period. The area is calculated using the trapezoidal rule for numerical integration, and the formula is as follows: ; in, The spectral index of the upper canopy, The photochemical reflectance index of the upper canopy at the k-th data acquisition time is given. The average photochemical reflectance index of the upper canopy at the (k+1)th data acquisition time is given. The time interval between two consecutive data collection times is k, where k is the index of the data collection time and n is the number of data collection times, and k∈[1,n]. Similarly, the spectral indices of the middle canopy and the lower canopy were obtained and measured during the effective photosynthetic period at midday.

[0043] This method goes beyond single-moment observations, employing multiple data collections and using the daily integral value as the final spectral index. Crop photosynthesis is not a static process but rather dynamically changes throughout the day. For example, around midday, strong light and high temperatures may cause partial stomata to close, resulting in a photosynthetic depression, at which point the PRI value decreases. Selecting only a single midday point might misjudge that the genotype is in a state of low light energy utilization efficiency throughout the day. Multiple data collections can comprehensively record the dynamic change curve of PRI from morning to night, thus more fully reflecting the crop's comprehensive physiological response strategies and capabilities to diurnal environmental fluctuations. Furthermore, the final biomass accumulation of a crop depends on the total amount of net photosynthetic products throughout the day. The daily integral value (i.e., the area under the curve) quantifies the cumulative effect of light energy utilization efficiency of the crop throughout the entire photosynthetically active period. One genotype may have high efficiency in the morning but decline rapidly in the afternoon, while another may remain stable throughout the day but with a low peak. Their instantaneous values ​​may be the same at a certain moment, but their daily integral values ​​will differ. As a comprehensive indicator, the daily integral value is more representative of the crop's overall performance on the measurement day than any single instantaneous value.

[0044] Furthermore, in numerical computation, there are various integration methods to choose from, such as the rectangular method and Simpson's method. This method chooses the trapezoidal rule based on several considerations. Compared to the simple rectangular method, the trapezoidal rule uses a trapezoid to approximate the area under the curve within each time interval, resulting in significantly higher accuracy and a closer approximation of the true integral value. Compared to higher-order methods (such as Simpson's method), its calculation process is very simple, stable, less prone to errors, and computationally efficient. Moreover, this invention explicitly requires that the time interval between two adjacent data collections be the same (i.e., ∆t is a constant). For data sampled at such equal time intervals, the application of the trapezoidal rule becomes extremely direct and natural, and the calculation formula can be highly simplified, thereby reducing computational complexity and error rate.

[0045] The acquired thermal infrared data was radiometrically calibrated, converting grayscale values ​​into absolute radiance values. Atmospheric correction was then performed to eliminate the effects of atmospheric absorption and emission, resulting in a preliminarily corrected canopy brightness temperature image. This image was then overlaid with the upper mask region, and the temperature values ​​of pixels falling within that region were extracted. The average temperature of all pixels within that region was calculated, and the ambient temperature was obtained and corrected to obtain the average temperature characterizing the actual physiological state of the canopy. The correction formula is as follows: ; in, The average temperature of the upper canopy. This is the average temperature value of all pixels in the upper canopy. These are empirical correction coefficients. The ambient temperature.

[0046] Canopy temperature directly obtained through thermal infrared cameras is affected by the actual physical temperature of the crop canopy and the thermal radiation from the surrounding atmosphere. Although radiometric calibration and atmospheric correction have eliminated most of the influence of atmospheric paths, ambient temperature, as a macro-meteorological background value, remains the best comprehensive indicator for measuring the current atmospheric thermodynamic state. The temperature of the crop canopy is constantly in dynamic equilibrium with the surrounding ambient air, exchanging heat continuously. Therefore, directly comparing the canopy brightness temperature with the brightness temperatures of other samples at different ambient temperatures is unscientific and misleading. The core purpose of correction is to eliminate measurement bias caused by fluctuations in ambient temperature, thereby more fairly and accurately revealing the differences in canopy temperature caused by the crop's own physiological activities (mainly transpiration).

[0047] Temperature sensors were placed at the four corners and the center of the planting area where the control group and the experimental group were located, and the average value was taken as the ambient temperature of the planting area. This reflects the canopy temperature difference. For a healthy, well-watered crop, due to the cooling effect of transpiration, the canopy temperature is typically lower than the ambient temperature, meaning this difference is less than 0. However, when a crop is under water stress, stomata close, transpiration weakens, this cooling effect decreases, and the canopy temperature rises to near or even above the ambient temperature, meaning this difference is greater than or equal to 0. Therefore, the canopy temperature difference itself is a highly valuable stress indicator. The correction logic here is to weight and adjust the original canopy temperature based on its temperature difference with the environment. If the canopy temperature is lower than the ambient temperature, the corrected temperature is lower than the original temperature, amplifying the cooling effect of healthy crops. If the canopy temperature is higher than the ambient temperature, the corrected temperature is higher than the original temperature, amplifying the warming effect of stressed crops. Through correction, the extracted temperature characteristics become more sensitive and specific to changes in crop physiological states, revealing their intrinsic physiological state rather than simply reflecting how hot it was at the time. The empirical correction coefficient needs to be determined by obtaining experimental data and establishing a regression model. The larger the k value, the greater the correction magnitude and the more obvious the amplification effect on physiological differences; the smaller the k value, the closer the result is to the original temperature.

[0048] Based on n data collection times, these times are connected to form a line graph showing the average temperature changing over time. The area enclosed by this line graph and the time axis is used to represent the integral value of the average temperature during the photosynthetically active period. This integral value is then used to measure the canopy temperature of the upper canopy during the midday photosynthetically active period. The area is also calculated using the trapezoidal rule for numerical integration, as shown in the following formula: ; in, The temperature of the upper canopy layer. The average temperature of the upper canopy at the k-th sampling time is [value missing]. The average temperature of the upper canopy at the (k+1)th sampling time; Similarly, the canopy temperatures of the middle layer and the lower layer were obtained and measured during the effective photosynthetic period at midday.

[0049] The physiological response of crops is not determined by a single instantaneous temperature, but rather by the cumulative effect of sustained exposure to heat. Brief high-temperature peaks may be buffered by the crop's short-term regulatory mechanisms, but prolonged exposure to high temperatures, even if not exceeding biological limits, can lead to: significantly increased respiration rates at high temperatures, consuming large amounts of photosynthetic products that could otherwise be used for growth; sustained high temperatures inhibiting photosynthetic enzyme activity, accelerating cell membrane permeability deterioration, and potentially causing protein denaturation; and insufficient time for repair at night, resulting in the gradual accumulation of heat damage. Therefore, a single instantaneous temperature value cannot comprehensively measure the heat load experienced by crops throughout the day. The daily integral value, however, quantifies the total heat load of the entire day, providing a more realistic and accurate reflection of the cumulative inhibitory effect of heat stress on crop growth. It also eliminates the interference of instantaneous fluctuations, providing a stable and reliable phenotypic indicator.

[0050] Step 4: Calculate the light energy utilization efficiency index based on the environmental stress index of the target crop under a single stress environment, as well as the spectral index and canopy temperature at each level, to characterize the light energy utilization efficiency of the target crop in each experimental group at each key growth stage. In this embodiment, calculating the light energy utilization efficiency index specifically includes: Based on the canopy temperature of the target crops in different layers of each experimental group, the temperature difference ratio between each experimental group and the control group at the same layer was calculated using the following formula: ; in, This represents the ratio of the upper canopy temperature difference between the i-th experimental group and the control group. Let be the canopy temperature of the upper canopy in the i-th experimental group. The temperature of the upper canopy layer in the control group; Similarly, the temperature difference ratios of the middle canopy and the lower canopy were obtained for the i-th experimental group and the control group, respectively.

[0051] This ratio reflects the degree to which the overall temperature regulation capacity of each canopy layer in the i-th experimental group deviates from that of the ideal control group due to environmental stress. This value is non-negative; a larger value indicates a more severe degree of thermal stress (significantly increased temperature) or a greater loss of transpiration cooling capacity (failure to effectively reduce temperature). It is a dimensionless relative value reflecting the intensity of interference caused by the stress environment on the crop canopy temperature regulation system. Its technical advantage lies in transforming absolute temperature values ​​into a comparable stress response index, eliminating baseline temperature differences caused by different crop species, different growth stages, or different daytime climatic conditions. This makes it possible to assess the effects of thermal stress between different experimental groups and even between different experimental days, providing standardized input for subsequent calculations of stress response coefficients.

[0052] The daily integral canopy temperature of the experimental and control groups was chosen to characterize the canopy temperature difference ratio because the canopy temperature of the target crop is a comprehensive result of the interaction between its physiological state and environmental factors: under ideal conditions without stress, the crop's transpiration cooling reaches a balance with the energy input of the environment, resulting in a relatively stable and optimal canopy temperature baseline; however, when subjected to environmental stress, the crop's physiological functions (especially transpiration) are inhibited, leading to a disruption of its energy balance, manifested as a deviation of the canopy temperature from the baseline value. Therefore, the absolute value of the difference between these two independent variables directly reflects the magnitude of the physical quantity of physiological imbalance caused by stress, while dividing by the control baseline value achieves normalization, transforming this physical quantity into a comparable relative deviation. The greater the deviation of the canopy temperature between the experimental and control groups, the larger their difference ratio becomes, showing a positive correlation. This positive correlation is because a larger temperature deviation indicates a more severe impairment of the crop's ability to maintain homeostasis, a greater intensity of thermal stress, or a more severe inhibition of transpiration.

[0053] The temperature difference ratio is normalized and mapped to a coefficient characterizing the degree of stress at that level. The calculation formula is as follows: ; in, This represents the stress response coefficient of the upper canopy in the i-th experimental group; Similarly, the stress response coefficients of the middle canopy and the lower canopy in the i-th experimental group are obtained, and are expressed as follows: , .

[0054] The formula for calculating the upper canopy temperature difference ratio shows that since this ratio is non-negative, the stress response coefficient is taken from the right side of the y-axis in its graph. Converting the temperature difference ratio into a stress response coefficient achieves a non-linear mapping from physical deviation to physiological function inhibition. The temperature difference ratio objectively quantifies the relative difference in canopy temperature between the stressed group and the control group. However, it cannot directly answer how much this temperature difference affects crop physiological functions. Furthermore, the physiological response of the target crop to temperature stress is highly non-linear, not a simple linear relationship. There is usually a threshold effect: when the temperature deviation is small, the crop can compensate through regulatory mechanisms (such as transpiration), and the decline in physiological function is slow; but when the deviation exceeds a certain limit, physiological function begins to decline sharply. Therefore, an exponential decay function is used here to simulate this nonlinear response relationship. The temperature difference ratio is normalized and dimensionless to limit it to the range of (0,1). When the temperature difference ratio is very small (close to 0), the stress response coefficient is close to 1, indicating that physiological functions are almost completely maintained. As the temperature difference ratio increases, the stress response coefficient begins to decrease rapidly, indicating that physiological functions are entering a rapid decline zone. When the temperature difference ratio is very large, the stress response coefficient approaches 0, indicating that physiological functions are almost collapsing. Such a decay curve is also more consistent with known plant biological laws.

[0055] The light energy utilization efficiency index is calculated based on the spectral index, stress response coefficient, and environmental stress index at each level, using the following formula: ; in, This represents the light energy utilization efficiency index of the i-th experimental group. This represents the spectral index of the upper canopy within the i-th experimental group. This represents the spectral index of the middle canopy within the i-th experimental group. Spectral indices of the lower canopy in the i-th experimental group These are the weight coefficients for the corresponding items. This represents the environmental stress index of the i-th experimental group.

[0056] The core meaning of the light energy use efficiency index (eLUE) is to quantify the actual light energy use efficiency that crops can maintain under a unit of environmental stress intensity. It is a comprehensive indicator of stress resistance, and its value directly reflects the ability of crop genotypes or cultivation management practices to maintain photosynthetic productivity under adverse conditions. A higher eLUE value indicates that the crop can still efficiently convert captured light energy into chemical energy per unit of environmental stress, demonstrating stronger stress resistance and photosynthetic stability; conversely, a lower eLUE value indicates that the crop is more sensitive to environmental stress and its photosynthetic function is easily inhibited. The technical advantage of this index lies in its coupling of complex environmental stress inputs with the multidimensional physiological response outputs of the crop canopy into a single, comparable, and biologically meaningful scalar value. This provides breeders with a crucial decision-making basis for screening high-light-efficiency, stress-resistant varieties and agronomists for evaluating the effectiveness of cultivation practices.

[0057] The choice of independent variables in the formula is based on the fact that the final light use efficiency (eLUE) of a crop is the result of the balance between the photosynthetic potential and heat stress loss of different canopy layers under specific stress conditions. The spectral index in the numerator represents the potential photosynthetic efficiency of each layer under conditions without heat dissipation interference, while the stress response coefficient introduces the inhibition coefficient of heat stress on this potential efficiency. The product of the two reflects the actual light use efficiency of each layer after heat stress, and the weighted summation through weighting coefficients reflects the differences in the contribution of different layers to the overall canopy. The environmental stress index in the denominator characterizes the intensity of the applied adversity. The light use efficiency index is positively correlated with the independent variables in the numerator and negatively correlated with the independent variables in the denominator. When the spectral index or stress response coefficient of each canopy layer increases, it means that the crop has a higher photosynthetic potential or less heat stress damage, and its overall actual light use efficiency is improved. Therefore, under the same environmental stress, the eLUE value increases accordingly. Conversely, when the environmental stress index increases, it indicates that the intensity of the adversity experienced by the crop increases. In response to increased external pressure, the crop's physiological functions are usually suppressed to a greater extent, leading to a relative decrease in its photosynthetic efficiency, and thus a decrease in the eLUE value. A high eLUE value means that the crop can cope with environmental stress at a lower physiological cost; that is, under the same environmental stress, the crop's light energy utilization efficiency is higher. In the formula for calculating the light energy utilization efficiency index, the weights are related as follows: This indicates that the upper canopy layer has the highest weight, followed by the middle layer, and the lower layer has the lowest. This designation is based on the uneven distribution of light in the canopy and the phototropic nature of photosynthesis. In a typical crop canopy, the upper leaves are directly exposed to full sunlight, receiving the strongest photosynthetically active radiation and being the primary source of photosynthetic products, thus contributing most significantly to the overall light energy utilization efficiency. While the middle leaves receive some shade, they still contribute considerably to photosynthesis, especially under diffused light conditions or when the upper leaves experience photoinhibition. The lower leaves, however, are severely shaded, experiencing a harsh light environment, resulting in a lower photosynthetic rate. Their respiration consumption often approaches or even exceeds their photosynthetic output, therefore their net contribution to overall light energy utilization is usually the smallest. This general rule of decreasing photosynthetic contribution from top to bottom is the fundamental basis for this weighting coefficient designation.

[0058] In this embodiment, 30 sets of sample data of the target crop were collected, and the spectral index of the sample data in each canopy and the environmental stress index of the experimental group were calculated. Here, the weights are... The values ​​are 0.5, 0.3, and 0.2, respectively. The specific sample data is shown in the table below: Table 1: Schematic Table of Relevant Data for Light Energy Utilization Efficiency Index

[0059] Combining the data in the table above, and Figure 2 It can be seen that as the spectral indices of each canopy increase, the numerator of the light energy utilization efficiency index (eLUE) increases, but at the same time, the denominator, the environmental stress index, also increases. Influenced by the denominator, the eLUE decreases. This reflects that the eLUE is a cost-effectiveness or efficiency indicator. The numerator represents the potential photosynthetic productivity of a crop under ideal conditions. A higher PRI value indicates better crop hardware and more efficient photosynthetic processes. The denominator represents the operating costs or resistance imposed by the environment. A higher SII value indicates a harsher environment for crop growth, such as high temperature, drought, or poor soil. To survive, the crop needs to consume more energy to cope with stress (e.g., synthesizing osmotic regulators, activating antioxidant systems), which greatly increases its metabolic burden. Therefore, even if a crop variety has extremely high photosynthetic potential (large numerator), if it is placed in an extremely harsh environment (larger denominator), its final net light energy utilization efficiency (eLUE) will be lower. Most of its energy is not used for growth and dry matter accumulation, but is consumed in resisting adversity. Conversely, a variety with moderate photosynthetic potential but extremely strong resistance may exhibit a higher eLUE in harsh environments because it can maintain physiological functions at a lower cost.

[0060] Therefore, to reflect the positive correlation between the spectral indices of each canopy and the light energy utilization efficiency index, an experimental group with a decreasing environmental stress index was set up. Sample data on the spectral indices of each canopy and the environmental stress index of each experimental group were collected again, as shown in the table below: Table 2: Schematic Table of Relevant Data for Light Energy Utilization Efficiency Index

[0061] Combining the data in the table above, and Figures 3-6 It can be seen that as the numerator (i.e., the spectral index of each canopy) increases and the environmental stress index decreases, indicating that the growth environment becomes more suitable, the light energy utilization efficiency index also increases. This further reflects a positive correlation between the numerator and the light energy utilization efficiency index, while the denominator and the light energy utilization efficiency index are inversely correlated.

[0062] Step 5: For the same growth stage, based on the light energy utilization efficiency index of all experimental groups and the control group, perform global standardization and comprehensive classification to complete the dynamic classification of the high light efficiency phenotype of the target crop in each experimental group.

[0063] In this embodiment, the comprehensive level classification specifically includes: The light energy utilization efficiency indices of all experimental and control groups at the same growth stage were collected to form several global datasets at the same growth stage. The mean and standardization of each global dataset were calculated to standardize the light energy utilization efficiency indices of all experiments in each global dataset. The standardized light energy utilization efficiency indices of the target crops at all growth stages of each experimental group were weighted and summed to obtain the light energy utilization efficiency index of the target crops of the corresponding experimental group throughout the entire growth cycle, so as to characterize the comprehensive light energy utilization efficiency of the target crops of the corresponding experimental group throughout the entire growth cycle.

[0064] Here, the light energy utilization efficiency index of the target crops in each experimental group during the seedling, growth, and fruiting stages is weighted and summed. The weight of fruiting is the highest, followed by the growth stage, and the weight of seedling is the lowest. The ultimate goal of crop photosynthetic production is to generate economic yield (such as grains and fruits), and the contribution rate of photosynthetic products to yield varies greatly at different growth stages. The fruiting stage is the period when yield components are directly formed and determined. During this stage, photosynthetic products (i.e., photosynthesis at this time) are preferentially and directly transported to grains or fruits, and are the primary source of grain filling substances. The level of light energy utilization efficiency during this period directly determines the level of the harvest index and the final yield. Therefore, the fruiting stage is given the highest weight. The growth stage is the critical period for the formation of crop vegetative organs and the accumulation of maximum biomass. During this stage, a large canopy structure and photosynthetic area are formed, laying the material foundation for later yield formation. Although the photosynthetic products at this stage are mainly used to construct roots, stems, and leaves, the biomass formed is the material basis for later yield formation, and the level of light energy utilization efficiency directly affects the sufficiency of the source. Therefore, the weight of the growth stage should be significantly higher than that of the seedling stage, but lower than that of the fruiting stage, which directly contributes to yield. The seedling stage is the initial stage of crop growth; the plants are small, and photosynthetic products are mainly used for their own morphogenesis. Although stress resistance is important at this stage, its absolute biomass accumulation and direct contribution to final yield are the smallest among the three stages. Therefore, assigning it the lowest weight is in line with agronomic common sense and can avoid excessive influence of early excessive growth on the final evaluation results.

[0065] The target crops in each experimental group were classified into five levels based on their high photosynthetic efficiency, from strongest to weakest. The specific criteria for classification were as follows: If... If it is classified as the optimal level, then it is considered as such. If it is classified as excellent, then it is classified as good. If it is classified as medium level, then it is considered medium level. If it is, it is classified as a poor grade. Then it is divided into range levels, among which, Let be the light energy utilization efficiency index of the target crop in the i-th experimental group throughout the entire growth cycle.

[0066] First, by aggregating the light energy utilization efficiency indices of all experimental and control groups at the same growth stage to form a global dataset and standardizing it, the different experimental groups are essentially placed under a unified and comparable reference system. Standardization eliminates the systematic differences in absolute eLUE values ​​caused by environmental background (such as seasonal variations in natural light and temperature) and different physiological centers of gravity at different growth stages, allowing index values ​​from the seedling, growth, and fruiting stages to be fairly compared and integrated. Subsequently, the standardized eLUE values ​​over the entire growth cycle are weighted and summed. The core logic is to acknowledge that the contribution of different crop growth stages to the final yield is not equal. For example, the direct contribution of photosynthetic efficiency during the fruiting stage to yield is usually much greater than that during the seedling stage. Therefore, in practical applications, this difference can be reflected by assigning it a higher weight, thus ensuring that the final global index... It can more accurately characterize the overall performance of the genotype or treatment throughout the growing season.

[0067] The standardized data has a mean of 0. Therefore, This means that the overall light energy utilization efficiency of this experimental group is at least one standard deviation higher than the average level under the same conditions and in the same period, demonstrating extremely outstanding resistance to adverse conditions and high light efficiency, and is therefore classified as the best level. A score between 0.5 and 1 indicates performance significantly better than average, but not exceptionally outstanding, thus classified as good. A score between -0.5 and 0.5 indicates performance comparable to average, with no significant strengths or weaknesses, thus classified as average. A score below -0.5 indicates that the performance is significantly below average. A score between -1 and -0.5 is considered poor, meaning that the performance is relatively sensitive to stress. A score below -1 indicates that the performance is far below average and the resilience is extremely poor, hence it is considered very poor.

[0068] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0069] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0070] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0071] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A dynamic classification method for crop high photosynthetic efficiency phenotypes that integrates multi-environmental response and vertical distribution analysis, characterized in that, The specific steps include: Step 1: Select the target crop, set up different single stress environments to cultivate the target crop as experimental groups, and set up a non-stress environment to cultivate the crop as a control group. Introduce the environmental stress index to characterize the intensity of environmental stress in each experimental group. Step 2: During the same growth period, acquire multi-source phenotypic data of the target crop canopy in the control group and the experimental group. The multi-source phenotypic data includes hyperspectral data, thermal infrared data and three-dimensional point cloud data. Step 3: Based on the 3D point cloud data, the vertical layer division of the crop canopy is carried out. The canopy of each target crop is divided into three layers in the vertical direction: upper, middle and lower. The spectral index and canopy temperature corresponding to each layer are extracted from the hyperspectral data and thermal infrared data respectively. Step 4: Calculate the light energy utilization efficiency index based on the environmental stress index of the target crop under a single stress environment, as well as the spectral index and canopy temperature at each level, to characterize the light energy utilization efficiency of the target crop in each experimental group at each key growth stage. Step 5: For the same growth stage, based on the light energy utilization efficiency index of all experimental groups and the control group, perform global standardization and comprehensive classification to complete the dynamic classification of the high light efficiency phenotype of the target crop in each experimental group.

2. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 1, characterized in that, Setting up control and experimental groups specifically includes: The control group, which is set up in the stress-free environment, has environmental parameters including at least: temperature, light intensity, soil volumetric water content, and nitrogen fertilizer application rate. The single stress environment includes a water stress group, a high temperature stress group, a nitrogen stress group, and a low light stress group. The water stress group is set with a soil volumetric water content that is half that of the control group, and the other environmental parameters are the same as the control group. The high temperature stress group is set with a temperature that is 10°C higher than the control group, and the other environmental parameters are the same as the control group. The nitrogen stress group is set with a nitrogen fertilizer application rate that is one-third lower than that of the control group, and the other environmental parameters are the same as the control group. The low light stress group is set with a light intensity that is half that of the control group, and the other environmental parameters are the same as the control group.

3. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 2, characterized in that, The calculation of the environmental stress index specifically includes: An environmental stress index is introduced to characterize the intensity of environmental stress in each experimental group. The specific calculation formula is as follows: ； in, Let i be the environmental stress index of the i-th experimental group. Let be the absolute difference between the temperature of the i-th experimental group and the temperature of the control group. The temperature of the control group Let be the absolute difference between the soil volumetric water content of the i-th experimental group and the soil volumetric water content of the control group. This represents the soil volumetric water content of the control group. Let be the absolute difference between the nitrogen fertilizer application rate of the i-th experimental group and the nitrogen fertilizer application rate of the control group. The temperature of the control group Let be the absolute difference between the light intensity of the i-th experimental group and the light intensity of the control group. The light intensity for the control group is , and i is the index of the experimental group. These are the weighting coefficients for each ratio; Based on the magnitude of the stress intensity index, the experimental groups were divided into stress levels, specifically as follows: when At that time, it was classified as mild stress. At that time, it was classified as moderate stress. At that time, it was classified as severe stress.

4. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 1, characterized in that, The collection of the multi-source phenotypic data specifically includes: The entire growth cycle of the target crop is divided into the seedling stage, the growth stage, and the fruiting stage. During the same growth stage, a UAV remote sensing platform integrating a hyperspectral camera, a thermal infrared camera, and a three-dimensional lidar is used to simultaneously collect multi-source phenotypic data of the target crop canopy above the canopy. One day is selected as the measurement day during each growth stage. Data is collected n times during the photosynthetically active period of the measurement day, with the time interval between two adjacent collections being the same, so as to obtain n collection times and their corresponding multi-source phenotypic data; the light saturation point of the target crop is determined. The photosynthetically active period is set as the time period from when the photosynthetically active radiation first reaches 80% of the light saturation point of the target crop after sunrise each day until the photosynthetically active radiation drops below that value before sunset. The hyperspectral data was acquired using a hyperspectral camera, the thermal infrared data was acquired using a thermal infrared camera, and the three-dimensional point cloud data was acquired using a three-dimensional lidar.

5. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 4, characterized in that, The vertical hierarchical division specifically includes: The three-dimensional point cloud data first collected during the effective photosynthetic period of the measurement day is used as the reference point cloud. All subsequent three-dimensional point cloud data are registered with the reference point cloud through the iterative nearest point algorithm, and the vertical hierarchy is divided based on the registered point cloud data. When acquiring 3D point cloud data using 3D LiDAR, the point cloud density should be no less than 100 points / square meter to reconstruct the 3D structure of the target crop canopy and perform hierarchical division. After point cloud registration is completed, the canopy hierarchy remains unchanged. Subsequent acquisitions of hyperspectral and thermal infrared data are all based on this fixed hierarchical mapping relationship to extract the spectral indices and canopy temperature of each level. The specific hierarchical division method is as follows: For the point cloud data of each target crop, calculate the height of all its points relative to the ground, and identify the highest and lowest point heights. The difference between the highest and lowest point heights represents the height range. Based on these height ranges, divide the target crop canopy into three layers. The point cloud data is divided into upper layers, and The point cloud data within the range is divided into a middle layer, and The point cloud data is divided into lower layers, where H is the height of the point cloud data. The height of the highest point. This is the height of the lowest point.

6. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 5, characterized in that, Obtaining the spectral index and canopy temperature specifically includes: After dividing the canopy into layers using 3D point cloud data, three corresponding mask regions were generated: an upper mask region, a middle mask region, and a lower mask region. Hyperspectral data was overlaid with the upper canopy mask region. For all pixels falling within the upper mask region, their reflectance values ​​at the 531nm and 570nm wavelengths were calculated. Based on these reflectance values, the photochemical reflectance index of each pixel in the upper mask region was calculated using the following formula: ； in, Photochemical reflectance index This indicates the reflectivity at the 531nm wavelength. This indicates the reflectivity at the 570nm wavelength. The average photochemical reflectance index of all pixels is calculated and used as the photochemical reflectance index of the upper canopy at the corresponding time. Based on n acquisition times, the acquisition times are connected to form a line graph showing the average photochemical reflectance index changing over time. The area enclosed by this line graph and the time axis is calculated to represent the integral value of the photochemical reflectance index of the upper canopy during the photosynthetically active period, and this integral value is used as the spectral index of the upper canopy during the midday photosynthetically active period. The area is calculated using the trapezoidal rule for numerical integration, and the calculation formula is as follows: ； in, The spectral index of the upper canopy, The photochemical reflectance index of the upper canopy at the k-th data acquisition time is given. The average photochemical reflectance index of the upper canopy at the (k+1)th data acquisition time is given. The time interval between two consecutive data collection times is k, where k is the index of the data collection time and n is the number of data collection times, and k∈[1,n]. Similarly, the spectral indices of the middle canopy and the lower canopy were obtained and measured during the effective photosynthetic period at midday. Radiometric calibration and atmospheric correction were performed on the acquired thermal infrared data to obtain a preliminarily corrected canopy brightness temperature image. From the canopy brightness temperature image, the pixel region that overlaps with the upper mask region in the point cloud data was extracted. The average temperature value of all pixels in this region was calculated, and the ambient temperature was obtained and corrected to obtain the average temperature used to characterize the actual physiological state of the canopy. The correction formula is as follows: ； in, The average temperature of the upper canopy. This is the average temperature value of all pixels in the upper canopy. These are empirical correction coefficients. The ambient temperature; Based on n data collection times, these times are connected to form a line graph showing the average temperature changing over time. The area enclosed by this line graph and the time axis is used to represent the integral value of the average temperature during the photosynthetically active period. This integral value is then used to measure the canopy temperature of the upper canopy during the midday photosynthetically active period. The area is also calculated using the trapezoidal rule for numerical integration, as shown in the following formula: ； in, The temperature of the upper canopy layer. The average temperature of the upper canopy at the k-th sampling time is [value missing]. The average temperature of the upper canopy at the (k+1)th sampling time; Similarly, the canopy temperatures of the middle layer and the lower layer were obtained and measured during the effective photosynthetic period at midday.

7. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 3, characterized in that, The calculation of the light energy utilization efficiency index specifically includes: Based on the canopy temperature of the target crops in different layers of each experimental group, the temperature difference ratio between each experimental group and the control group at the same layer was calculated using the following formula: ； in, This represents the ratio of the upper canopy temperature difference between the i-th experimental group and the control group. Let be the canopy temperature of the upper canopy in the i-th experimental group. The temperature of the upper canopy layer in the control group; Similarly, the temperature difference ratios of the middle canopy and the lower canopy were obtained for the i-th experimental group and the control group, respectively. The temperature difference ratio is normalized and mapped to a coefficient characterizing the degree of stress at that level. The calculation formula is as follows: ； in, This represents the stress response coefficient of the upper canopy in the i-th experimental group; Similarly, the stress response coefficients of the middle canopy and the lower canopy in the i-th experimental group are obtained, and are expressed as follows: , ; The light energy utilization efficiency index is calculated based on the spectral index, stress response coefficient, and environmental stress index at each level, using the following formula: ； in, This represents the light energy utilization efficiency index of the i-th experimental group. This represents the spectral index of the upper canopy within the i-th experimental group. This represents the spectral index of the middle canopy within the i-th experimental group. Spectral indices of the lower canopy in the i-th experimental group These are the weight coefficients for the corresponding items. denoted as the environmental stress index of the i-th experimental group.

8. The dynamic classification method for crop high light efficiency phenotypes based on multi-environmental response and vertical distribution analysis according to claim 1, characterized in that, The comprehensive grading specifically includes: The light energy utilization efficiency indices of all experimental and control groups at the same growth stage were collected to form several global datasets at the same growth stage. The mean and standardization of each global dataset were calculated to standardize the light energy utilization efficiency indices of all experiments in each global dataset. The standardized light energy utilization efficiency indices of the target crops at all growth stages of each experimental group were weighted and summed to obtain the light energy utilization efficiency index of the target crops of the corresponding experimental group throughout the entire growth cycle, so as to characterize the comprehensive light energy utilization efficiency of the target crops of the corresponding experimental group throughout the entire growth cycle. The target crops in each experimental group were classified into five levels based on their high photosynthetic efficiency, from strongest to weakest. The specific criteria for classification were as follows: If... If it is classified as the optimal level, then it is considered as such. If it is classified as excellent, then it is classified as good. If it is classified as medium level, then it is classified as medium level. If it is, it is classified as a poor grade. Then it is divided into range levels, among which, Let be the light energy utilization efficiency index of the target crop in the i-th experimental group throughout the entire growth cycle.

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