Method for reducing noon break proportion of citrus stomata

Abstract

The invention discloses a method for reducing the noon break proportion of citrus stomata, and relates to the technical field of agriculture, and the method comprises the following steps: firstly, installing a multispectral camera in a citrus planting area, and obtaining the state information of citrus leaves in real time; through intelligent water spraying adjustment, the air hole state is accurately recognized through the thermal steady state deviation index and the fluorescence enhancement index, fine and smooth water mist is sprayed only when the air holes are closed, the water utilization efficiency is improved, and waste is avoided. When the air holes are opened, spraying is automatically stopped, and over-wetting and disease risks are prevented. Continuous monitoring and dynamic regulation ensure plant photosynthesis and transpiration optimization, gas exchange and nutrient absorption are improved, the yield and quality of citrus are remarkably improved, and economic and ecological win-win is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of agriculture, and in particular to a method for reducing the midday rest ratio of stomata in citrus. Background Art

[0002] Reducing the midday stomatal rest ratio of citrus means reducing the time and frequency of citrus plants closing their stomata during the high-temperature period at noon by improving environmental conditions or taking certain agricultural management measures. Stomatal rest is a phenomenon in which plants close their stomata during high-temperature periods in order to reduce water evaporation and avoid excessive transpiration. By reducing this ratio, the plant's stomatal opening time can be increased, thereby improving photosynthesis efficiency and water use efficiency, especially in high-temperature environments. Controlling this ratio can help citrus better adapt to climate change during the growing season, optimize water and nutrient absorption, and increase yield and quality.

[0003] The prior art has the following deficiencies: In the prior art, the water spraying device continuously sprays water particles on citrus leaves to help the stomata open. However, when the sprayed water particles are large, they may not be able to effectively adhere to the surface of the leaves, resulting in uneven distribution of water. Larger water particles may drip or flow away quickly and fail to form a uniform film on the leaves, which reduces the cooling and moisturizing effects and may cause the evaporation rate of water to decrease. In addition, this spraying method may waste a lot of water resources.

[0004] When the stomata are open, plants exchange gases through their stomata, especially transpiration of water vapor. Although spraying larger water particles will increase the moisture on the leaf surface, transpiration is not effectively regulated because the water particles fail to effectively adhere to the leaves. Water will evaporate quickly, resulting in excessive water loss. Plants may not be able to replenish lost water in time, which will affect water balance, especially in a continuous high temperature environment, which may aggravate water stress and affect plant growth and health.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The purpose of the present invention is to provide a method for reducing the proportion of citrus stomata in the afternoon. Through an intelligent water spraying adjustment mechanism, the scheme accurately judges the state of the stomata based on the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index, and starts fine water mist spraying only when the stomata are closed, thereby improving water utilization efficiency and avoiding loss and waste. When the stomata are open, the system automatically turns off the water spray device to prevent excess water and disease risks. Continuous monitoring and dynamic regulation ensure that plants always maintain the best physiological state under different environments, optimize photosynthesis and transpiration, enhance gas exchange and nutrient absorption capacity, significantly improve the yield and quality of citrus, and help the economic and ecological benefits of agriculture to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a method for reducing the midday rest ratio of citrus stomata, comprising the following steps: First, multispectral cameras were installed in citrus growing areas to obtain real-time information on the status of citrus leaves. After the multispectral camera collects the leaf status information, the data is stored and organized into a systematic data set, and the key features reflecting the stomata being in a closed state are extracted from the established data set; Within the detection window, the extracted key features are analyzed in detail, and then the analyzed key features are input into the pre-learned machine learning model, and the intelligent evaluation of the stomatal status of the leaves is performed through the machine learning model; Based on the evaluation results of the machine learning model, the stomatal states of leaves are divided into two categories: stomatal open state and stomatal closed state; According to the stomata closure state, based on the preset spray water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray. At the same time, the stomatal state of the leaves is paid attention to in real time. When the stoma gradually opens, the spray device is automatically closed. When the stomata are open, the water spray device does not work, reducing water waste. At the same time, it continuously pays attention to the stomatal status of the blades and maintains the ability of real-time monitoring and dynamic adjustment.

[0008] Preferably, key features reflecting that the stomata are in a closed state are extracted from the established data set, and the extracted features include the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface. Under the detection window, the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface are analyzed to generate a thermal steady-state deviation index and a stomatal edge fluorescence enhancement index, respectively. The thermal steady-state deviation index reflects the stomatal state by quantifying the uniformity of the thermal distribution on the leaf surface; the stomatal edge fluorescence enhancement index reflects the physiological state of the stoma by quantifying the change in fluorescence emission intensity in the stomatal edge area.

[0009] Preferably, after obtaining the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index generated after analyzing the extracted key features, the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index are input into a pre-learned machine learning model, and the stomatal variation coefficient is generated by the machine learning model, and the stomatal state in the leaf is intelligently evaluated by the stomatal variation coefficient.

[0010] Preferably, the generated stomatal variation coefficient is compared and analyzed with a preset stomatal variation coefficient reference threshold value, and the stomatal states of the leaves are divided, and the division steps are as follows: If the stomatal variation coefficient is greater than or equal to a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal closed state; If the stomatal variation coefficient is less than a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal open state.

[0011] Preferably, for the stomata closed state, based on the preset spraying water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray, and the stomata state of the leaves is paid attention to in real time. When the stomata gradually open, the specific steps of automatically closing the spray device are as follows: The porosity variation coefficient The stomatal status of the leaves is divided by comparing with the preset stomatal variation coefficient reference threshold. The judgment rules are as follows: , where is the reference threshold of the stomatal variation coefficient; When the leaf pores are judged to be in a closed state, the water spray device is triggered to start spraying water particles. The spraying effect is automatically adjusted according to the current leaf state, and fine water mist particles are sprayed. The calculation expression is as follows: , where is the diameter of the sprayed water particles, It is the basic particle size parameter of the spraying system. is the adjustment factor; During the spraying process, the stomata status of the leaves is monitored in real time to obtain the latest stomata change coefficient value, and continue to be consistent with the reference threshold value of the stomatal variation coefficient By comparison, if the pore state tends to be open during the spraying process, the spraying parameters are adjusted dynamically to adjust the spraying flow rate. The calculation expression is as follows: , where is the water spray flow rate, is the initial spraying flow parameter, is the attenuation coefficient; When real-time monitoring results show When the blade pores are judged to be in an open state, the water spray device is automatically closed. The control rules for closing the water spray are as follows: , where It is the operating status parameter of the water spraying device. The value of 1 means the water spraying is turned on, and 0 means the water spraying is stopped.

[0012] Preferably, the specific steps of analyzing the thermal distribution on the blade surface and generating the thermal steady state deviation index are as follows: In the detection window, the thermal radiation data of the leaves in multiple bands is obtained through a multispectral camera, and the thermal radiation intensity of different areas on the leaf surface is recorded. The thermal radiation intensity of each pixel point is calculated to obtain the corresponding surface temperature, and the surface temperatures of all pixels are combined into a two-dimensional temperature distribution matrix. , the surface temperature calculation expression for each pixel is as follows: , where is the temperature of the pixel point on the leaf surface, is the thermal radiation intensity, is the ability of the blade surface to emit thermal radiation. is the Stefan-Boltzmann constant; On the blade surface, the temperature difference between each pixel and its surrounding pixels reflects the local thermal gradient. By calculating the change rate of the local thermal gradient of the pixel, the distribution dynamics of heat on the blade surface can be quantified. The calculation expression is as follows: , where is the rate of change of the local thermal gradient of the pixel, is the temperature of the pixel on the leaf surface For The temperature change rate of the neighboring points in the horizontal direction is is the temperature of the pixel on the leaf surface For The temperature change rate of the neighboring points in the vertical direction is is the total number of pixel neighbors; According to the local thermal gradient change rate , the energy dissipation distribution index of each pixel is further calculated to quantify the uneven diffusion degree of heat on the blade surface in the detection window. The calculation expression is as follows: , where is the energy dissipation distribution index, is the attenuation coefficient, is the central horizontal coordinate, is the central longitudinal coordinate; The energy dissipation distribution index of all pixels Normalized accumulation, the overall thermal steady-state deviation index is obtained, and the calculation is expressed as follows: , where is the thermal steady-state deviation index, is the thermal gradient amplification factor.

[0013] Preferably, the specific steps of analyzing the fluorescence emission characteristics of the leaf surface and generating the stomatal edge fluorescence enhancement index are as follows: In the detection window, a multispectral camera is used to monitor citrus leaves in real time to obtain the fluorescence emission characteristics of the leaf surface at different wavelengths. The collected fluorescence data records its spectral reflectance characteristics in pixels. In order to improve the data accuracy, the collected data is preprocessed to correct the influence of light intensity and equipment errors. The calculation is expressed as follows: , where is the raw fluorescence emission intensity, is the spectral correction factor, is the wavelength; In the leaf image, the stomatal edge feature recognition algorithm is used to lock the stomatal edge area, compare the fluorescence intensity changes of different pixels, and extract the characteristic value of the fluorescence emission characteristic change of the stomatal edge area. The calculation expression is as follows: , where is the difference in the fluorescence emission gradient at the stomatal edge area, is the pixel at wavelength The fluorescence intensity change rate under is the pixel coordinate set of the pore edge area, is the pixel coordinate set of the non-edge area; In order to further amplify the fluorescence difference between the stomatal edge area and the non-edge area, the enhanced fluorescence difference feature is generated to quantify the significance of the change in the fluorescence emission characteristics of the stomatal edge. The calculation expression is as follows: , where is the enhanced fluorescence difference feature, is the wavelength weight function, is the second-order rate of change of the raw fluorescence emission intensity, is the exponential amplification term of the second-order fluorescence change; Enhanced fluorescence difference characteristics , generate the stomatal edge fluorescence enhancement index, the calculation expression is as follows: , where is the stomatal edge fluorescence enhancement index, is the local weight distribution function, is a nonlinear amplification function.

[0014] In the above technical solution, the technical effects and advantages provided by the present invention are: The present invention effectively optimizes the water spraying efficiency through an intelligent water spraying adjustment mechanism. When the leaves are in a stomatal closed state, the system generates a stomatal variation coefficient through a machine learning model based on precise indicators such as the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index, accurately judges the stomatal state, and starts the water spray device only when the stomatal state is closed. In addition, the water spray device adopts a delicate mist spraying method to further enhance the adhesion and uniformity of water and prevent the loss problem caused by excessively large water particles. At the same time, when it is detected that the stomata are gradually opening, the system will automatically turn off the water spray device to avoid unnecessary water spraying. This method of dynamic adjustment and precise control not only reduces the waste of water resources, but also improves the utilization efficiency of every drop of water, providing growers with significant advantages in water resource management and cost savings.

[0015] The present invention monitors the stomatal state of citrus leaves in real time. The solution provides an appropriate amount of water spray when the stoma is closed to reduce the leaf temperature, improve the water balance, and promote the reopening of the stoma, thereby optimizing the photosynthesis and transpiration of the leaves. When the stoma is in an open state, the plant can effectively exchange gases and regulate temperature through its own transpiration. At this time, closing the water spray device not only prevents excess water, but also avoids the risk of disease caused by the over-humid environment of the leaves. At the same time, the solution has the ability of continuous monitoring and dynamic adjustment, can quickly respond to changes in the external environment, and provide water supplement in time when the stoma is re-closed, so as to ensure that the plant is always in the best physiological state under different environmental conditions. Through this intelligent control mechanism, the healthy growth of plants is significantly improved, and the gas exchange efficiency and nutrient absorption capacity of the leaves are also optimized, which ultimately improves the yield and quality of citrus, providing significant economic and ecological benefits for agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 The present invention is a flow chart of the method for reducing the midday rest ratio of stomata in citrus fruits. DETAILED DESCRIPTION

[0018] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0019] The present invention provides Figure 1 The method for reducing the midday break ratio of citrus stomata comprises the following steps: First, multispectral cameras were installed in citrus growing areas to obtain real-time information on the status of citrus leaves. First, multispectral cameras are installed in citrus planting areas to obtain real-time status information of citrus leaves. Multispectral cameras can capture the reflection characteristics of leaves in different bands of spectrum (such as visible light, near infrared and short-wave infrared). These characteristics directly reflect the physiological state of leaves, including the degree of opening and closing of stomata, water content and photosynthetic activity. At the same time, multispectral cameras can also sense the light-receiving area of ​​the leaves, and evaluate whether the leaves are in a high-light or low-light environment by analyzing the intensity and angle of sunlight, which is crucial for judging the dynamic response of stomata. For example, at high light intensity, stomata may be closed due to overheating, while under moderate light, stomata remain open. By comprehensively analyzing the spectral reflection characteristics and lighting conditions, the system can accurately judge the state of leaf stomata and provide key data support for subsequent water spraying adjustments.

[0020] After the multispectral camera collects the leaf status information, the data is stored and organized into a systematic data set, and the key features reflecting the stomata being in a closed state are extracted from the established data set; After the multispectral camera collects the leaf status information, the data first needs to be stored and organized into a systematic data set. This process involves the unified storage and management of leaf status data at different time and space locations, including spectral reflectance data, leaf surface temperature, humidity and other related characteristics. These data will be annotated according to the timestamp and spatial location information of the collection, and stored in a database or data warehouse to form a data set that can be continuously updated and queried. In this way, a large amount of raw data can be effectively managed to provide clear and structured input for subsequent analysis and model training.

[0021] The steps and functions of establishing an analysis set are to provide data support for subsequent intelligent analysis by extracting key features that reflect the closed state of stomata from a systematic data set. These key features may include changes in reflectance values ​​at specific wavelengths, leaf temperature fluctuations, and other physiological indicators related to stomatal opening and closing. Through the extraction and analysis of these features, the specific manifestations of the stomatal closure state can be identified, and high-quality input data can be provided for the training and optimization of machine learning models, thereby improving the accuracy of intelligent assessment of stomatal status and providing a scientific basis for automated spraying adjustments.

[0022] Within the detection window, the extracted key features are analyzed in detail, and then the analyzed key features are input into the pre-learned machine learning model, and the intelligent evaluation of the stomatal status of the leaves is performed through the machine learning model; The key features reflecting that the stomata are in a closed state are extracted from the established data set. The extracted features include the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface. Under the detection window, the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface are analyzed to generate a thermal steady-state deviation index and a stomatal edge fluorescence enhancement index, respectively. The thermal steady-state deviation index reflects the stomatal state by quantifying the uniformity of the thermal distribution on the leaf surface; the stomatal edge fluorescence enhancement index reflects the physiological state of the stoma by quantifying the changes in the fluorescence emission intensity in the stomatal edge area.

[0023] After obtaining the thermal steady-state deviation index and stomatal edge fluorescence enhancement index generated by analyzing the extracted key features, the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index are input into a pre-learned machine learning model, and the stomatal variation coefficient is generated by the machine learning model. The stomatal status in the leaves is intelligently evaluated through the stomatal variation coefficient.

[0024] A pre-learned machine learning model refers to a model that has been fully trained and optimized and can accurately predict or classify based on the input data features. In this application, the main task of the model is to generate the stomatal variation coefficient by inputting the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index, so as to intelligently evaluate the state of stomata in citrus leaves. Training this model requires the use of a large amount of annotated leaf data, which contains known stomatal states (such as open or closed) and corresponding characteristic parameter values ​​(such as thermal distribution and fluorescence characteristics). Through training on these data, the model can learn to capture the potential laws of stomatal state changes from the thermal steady-state deviation index and the fluorescence enhancement index, and establish a mapping relationship between features and stomatal states. The model usually selects algorithms suitable for nonlinear problems, such as support vector machines (SVM), random forests (RF), or deep learning models (such as neural networks) to ensure accurate evaluation of complex stomatal states.

[0025] The core advantages of pre-learned machine learning models are their versatility and real-time responsiveness. Once the model is trained, it can quickly evaluate real-time input data without reanalyzing the entire data set. This "pre-learning" process also includes model validation and optimization, such as cross-validation, test set evaluation, and parameter tuning to ensure that the model has high predictive accuracy and robustness under different environmental conditions and leaf characteristics. By using high-value features such as the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index, the model can accurately capture subtle changes in stomata and quantify the degree of their state transition (i.e., the stomatal variation coefficient), thereby providing a scientific basis for intelligent spraying regulation. In addition, the pre-learned model can be continuously updated and retrained by introducing new data, so that it can maintain high adaptability and reliability when facing different climatic conditions or plant varieties.

[0026] The heat distribution on the leaf surface tends to be uneven, and the thermal stability of local areas is affected, which usually indicates that the stomata in the citrus leaves may be in a closed state. This is because after the stomata are closed, the transpiration of the leaves is significantly reduced, the evaporation of water slows down, and the leaf surface loses the ability to take away heat through transpiration, resulting in inconsistent heat accumulation and dissipation rates in different areas, forming uneven heat distribution. In addition, when the stomata are closed, the leaves' ability to regulate the external environment decreases, making the temperature of local areas more susceptible to direct sunlight or other environmental factors, further exacerbating thermal unevenness. Therefore, abnormal heat distribution on the leaf surface and changes in local thermal stability are important indirect characterization features of the stomatal closure state. By detecting these thermal characteristics, the current opening and closing state of the stomata on citrus leaves can be effectively evaluated.

[0027] Under the detection window, the specific steps of analyzing the thermal distribution on the blade surface and generating the thermal steady-state deviation index are as follows: In the detection window, the thermal radiation data of the leaves in multiple bands is obtained through a multispectral camera. The acquired data is recorded in units of each pixel to record the thermal radiation intensity of different areas on the leaf surface. The thermal radiation intensity of each pixel point is calculated to obtain the corresponding surface temperature, and the surface temperatures of all pixels are combined into a two-dimensional temperature distribution matrix. , which is used to represent the overall heat distribution within the detection window. The surface temperature calculation expression for each pixel is as follows: , where is the temperature of the pixel point on the leaf surface, , is the coordinate of the pixel point, is the thermal radiation intensity, which indicates the radiation intensity of each pixel point on the leaf surface in the thermal infrared band. is the ability of the blade surface to emit thermal radiation. is the Stefan-Boltzmann constant; The thermal radiation emission capacity of the blade surface refers to the ability of the blade surface to release its own heat energy in the form of infrared radiation, which is expressed in emissivity. It is quantified by , and its value ranges from 0 to 1. Emissivity represents the ratio of the actual radiation energy of the leaf to the radiation energy of an ideal black body (perfect radiator). The emissivity of leaves is usually high, about 0.95, which is related to their complex surface structure and ability to efficiently absorb infrared radiation. In temperature calculations, emissivity is used to correct the relationship between radiation intensity and temperature, and its accuracy directly affects the accuracy of temperature calculations. The value of emissivity is usually determined experimentally or found in a botanical database, and may vary depending on the leaf type, health status, and surface characteristics (such as whether it is covered with a water film). It plays a vital role in temperature inversion and stomatal status assessment to ensure that the calculation results are consistent with the thermal radiation characteristics of actual leaves.

[0028] Stefan-Boltzmann constant The function of the formula is to convert the thermal radiation intensity captured by the multispectral camera Converted to the temperature of the pixel point on the leaf surface According to the Stefan-Boltzmann law, the radiation intensity of an object is proportional to the fourth power of its surface temperature, and the constant As a scaling factor, it quantifies this relationship. Specifically, it ensures the physical accuracy of the calculation from thermal radiation intensity to temperature and is the key bridge in the formula that links energy (radiation intensity) to thermal properties (temperature). Based on the thermal radiation data acquired by the multispectral camera, The actual temperature of the blade surface can be accurately calculated, thus providing a scientific basis for subsequent thermal distribution analysis.

[0029] On the blade surface, the temperature difference between each pixel and its surrounding pixels reflects the local thermal gradient. The distribution dynamics of heat on the blade surface is quantified by calculating the change rate of the local thermal gradient of the pixel. The change rate of the local thermal gradient is defined as the cumulative value of the change rate of the temperature of each pixel relative to its four neighbors. The calculation expression is as follows: , where It is the rate of change of the local thermal gradient of a pixel, which is used to quantify the intensity of the temperature change of a pixel on the leaf surface relative to its neighborhood. is the temperature of the pixel on the leaf surface For The neighboring points are in the horizontal direction ( The temperature change rate in the horizontal direction indicates the speed at which the temperature changes with the horizontal direction. is the temperature of the pixel on the leaf surface For The neighboring points are in the vertical direction ( The temperature change rate in the vertical direction indicates the speed at which the temperature changes with the vertical direction. is the total number of pixel neighbors; Four neighborhoods is a commonly used concept in image processing and computer vision, which refers to the four directly adjacent pixels of a pixel in a two-dimensional grid. Specifically, for the pixel at position The pixel whose four neighbors include the pixel above , the pixels below , the pixel on the left and the pixel on the right These adjacent pixels are directly connected to the current pixel in the horizontal and vertical directions, excluding pixels in the diagonal direction. The concept of four neighborhoods is widely used in image processing, especially in algorithms such as image edge detection, morphological operations, and region growing. It is used to describe the spatial relationship between pixels and help achieve local analysis and feature extraction.

[0030] According to the local thermal gradient change rate , the energy dissipation distribution index of each pixel is further calculated to quantify the uneven diffusion degree of heat on the blade surface in the detection window. The calculation expression is as follows: , where is the energy dissipation distribution index, which quantifies the local heat dissipation characteristics of a pixel while taking into account its spatial position in the detection window. is the attenuation coefficient, which controls the weight of the spatial position on the energy dissipation distribution index. is the central transverse coordinate, representing the heat distribution matrix on the blade surface The center point of The coordinate position of the axis (horizontal), is the central longitudinal coordinate, representing the heat distribution matrix on the blade surface The center point of Axis (vertical) coordinate position; The energy dissipation distribution index of all pixels Normalized accumulation, the overall thermal steady-state deviation index is obtained, and the calculation is expressed as follows: , where is the thermal steady-state deviation index, is the thermal gradient amplification factor, which is calculated by the local thermal gradient change rate The logarithmic transformation is introduced to enhance the sensitivity to high thermal gradient regions while weakening the impact on low thermal gradient regions.

[0031] Under the detection window, the larger the value of the thermal steady-state deviation index generated after analyzing the thermal distribution on the leaf surface, the more uneven the thermal distribution on the leaf surface is, and the greater the impact on the thermal stability of the local area. This is usually a sign that the stomata are in a closed state, because the transpiration is reduced and the leaf surface loses the ability to dissipate heat evenly through water evaporation. Conversely, the smaller the value of the thermal steady-state deviation index, the more even the thermal distribution on the leaf surface is, and the higher the thermal stability of the local area is, which is a characteristic of the open state of the stomata, because the transpiration is active, and the excess heat can be taken away through water evaporation, thereby maintaining the thermal balance on the leaf surface.

[0032] Changes in the fluorescence emission characteristics of the leaf surface and a slight increase in the fluorescence intensity at the edge of the stoma usually indicate that the stomata in the current citrus leaves are in a closed state. This is because when the stomata are closed, the transpiration on the leaf surface is significantly reduced, and water and energy metabolism in the stomatal edge area accumulate locally, which may trigger local enhancement of photosynthesis-related fluorescence signals. In addition, after the stomata are closed, the carbon dioxide concentration inside the leaves may increase, leading to changes in the efficiency of electron transfer during photosynthesis, thereby affecting the fluorescence emission characteristics. In particular, metabolic activity may be more concentrated in the stomatal edge area, leading to an increase in fluorescence intensity. This change is an indirect manifestation of the adjustment of the physiological state of the leaves after the stomata are closed. Therefore, by detecting the characteristics of the enhanced fluorescence at the stomatal edge, it can be used as one of the important characteristic indicators of the stomatal closure state to judge the current state of the stomata.

[0033] Under the detection window, the specific steps for analyzing the fluorescence emission characteristics of the leaf surface and generating the stomatal edge fluorescence enhancement index are as follows: In the detection window, a multispectral camera is used to monitor citrus leaves in real time to obtain the fluorescence emission characteristics of the leaf surface at different wavelengths, focusing on the changes in fluorescence intensity in the stomatal edge area. The collected fluorescence data records its spectral reflectance characteristics in pixels. To improve data accuracy, the collected data is preprocessed, including noise removal and spectral correction, to correct the influence of light intensity and equipment errors, so that the fluorescence data can more accurately reflect the local characteristics of the stomatal edge. The calculation is expressed as follows: , where is the raw fluorescence emission intensity, which is the fluorescence emission intensity data of the leaf surface collected by the multispectral camera, specifically for the pixel coordinates In wavelength The fluorescence reflectance value under is the spectral correction factor, is the wavelength, which indicates the specific wavelength of light used by the multispectral camera in the analysis of fluorescence emission characteristics; Spectral correction factor It is a coefficient used to correct the difference in response sensitivity of a multispectral camera at different wavelengths. It reflects the deviation of the device in each spectral band and is obtained through pre-calibration. Since a multispectral camera may be more or less sensitive to light signals at a specific wavelength, coupled with the influence of ambient lighting conditions (such as light source intensity or uniformity), the collected fluorescence intensity data may not fully and truly reflect the actual emission characteristics of the leaf surface. The role of the spectral correction factor is to unify the spectral data at different wavelengths so that they can be compared and analyzed on the same basis, eliminating data deviations caused by equipment characteristics or external environment, thereby ensuring that the fluorescence intensity data is accurate and stable, and providing a reliable basis for subsequent leaf feature extraction and index calculation.

[0034] In the leaf image, the stomatal edge feature recognition algorithm is used to lock the stomatal edge area, compare the fluorescence intensity changes of different pixels, and extract the characteristic value of the fluorescence emission characteristic change of the stomatal edge area. The calculation expression is as follows: , where It is the difference in the change of fluorescence emission gradient in the stomatal edge area, which is used to quantify the difference in the change rate of fluorescence intensity between the stomatal edge area and the non-edge area. is the pixel at wavelength The fluorescence intensity change rate under is a set of pixel coordinates of the stomatal edge area, representing the stomatal edge pixels identified by the image processing algorithm. The gradient changes of these pixels will serve as important features of the stomatal closure state. It is a set of pixel coordinates in the non-edge area, indicating the background area that is not at the edge of the stoma. Its gradient change can be used as a contrast to highlight the features of the stomatal edge. In order to further amplify the fluorescence difference between the stomatal edge area and the non-edge area, the enhanced fluorescence difference feature is generated to quantify the significance of the change in the fluorescence emission characteristics of the stomatal edge. The enhanced fluorescence difference feature is calculated by combining the gradient change, wavelength sensitivity and second-order fluorescence change trend. The calculation expression is as follows: , where It is the enhanced fluorescence difference feature used to describe the pixels at the edge of the stoma The difference between the fluorescence emission characteristics of the is the wavelength weight function, which is a weight function that varies with wavelength and is used to enhance the sensitivity of a specific band to fluorescence changes. is the second-order rate of change of the original fluorescence emission intensity, indicating the original fluorescence emission intensity The second derivative with wavelength, quantifies the acceleration of fluorescence change, It is the exponential amplification term of the second-order fluorescence change, which exponentially processes the second-order change rate to enhance its amplification effect on the fluorescence change; Enhanced fluorescence difference characteristics , generate the stomatal edge fluorescence enhancement index, the calculation expression is as follows: , where is the stomatal edge fluorescence enhancement index, is a local weight distribution function, which is used to quantify the contribution of different pixels in the stomatal edge area to the fluorescence enhancement index. It is a nonlinear amplification function. By taking the logarithm of the enhancement feature, the range of larger values ​​can be compressed while the changes of small values ​​can be amplified, thereby increasing the sensitivity of the index to the weak enhancement characteristics of fluorescence; Under the detection window, the larger the value of the fluorescence enhancement index at the edge of the stoma, which is generated after analyzing the fluorescence emission characteristics of the leaf surface, the more the stomata in the current citrus tree leaf are in a closed state; conversely, if the value of the fluorescence enhancement index at the edge of the stoma is smaller, it indicates that the stomata are in an open state. This is because when the stomata are closed, the transpiration of the leaves is weakened, and the water and metabolic activity around the stomata may accumulate locally, resulting in a relative enhancement of the fluorescence emission intensity in the stomatal edge area. When the stomata are open, transpiration is active, the metabolic state around the stomata tends to be balanced, and the fluorescence intensity enhancement effect is weakened.

[0035] The machine learning model is not limited here and can achieve the thermal steady state deviation index and stomatal edge fluorescence enhancement index Comprehensive analysis to generate the porosity variation coefficient In order to realize the technical solution of the present invention, the present invention provides a specific implementation method; Porosity variation coefficient The generation formula is as follows: , where , Thermal Steady State Deviation Index and stomatal edge fluorescence enhancement index The preset scaling factor of , Both are greater than 0.

[0036] The preset proportionality factor is used to weight the thermal steady-state deviation index and stomatal edge fluorescence enhancement index The final calculation result (i.e., the porosity variation coefficient ) is the coefficient of the influence weight. In the formula, and The deviation index from thermal steady state is and stomatal edge fluorescence enhancement index The corresponding weights are pre-set through experiments or theoretical analysis to reflect the relative importance of the two indices to the change of stomatal state. If the change of the pore state is more affected, you can set The value is higher than These coefficients are greater than 0, and their specific values ​​need to be verified through experiments to ensure that the combined results can accurately reflect the changes in the actual stomatal state, thereby providing a reliable reference for the evaluation of the machine learning model.

[0037] It can be seen from the calculation formula of the stomatal variation coefficient that, under the detection window, the larger the thermal steady-state deviation index expression value generated after analyzing the thermal distribution of the leaf surface, and the larger the stomatal edge fluorescence enhancement index expression value generated after analyzing the fluorescence emission characteristics of the leaf surface, the larger the stomatal variation coefficient expression value generated under the detection window, indicating that the stomata of the current citrus leaf are in a closed state, otherwise, it indicates that the stomata of the current citrus leaf are in an open state.

[0038] Based on the evaluation results of the machine learning model, the stomatal states of leaves are divided into two categories: stomatal open state and stomatal closed state; The generated stomatal variation coefficient is compared and analyzed with the preset stomatal variation coefficient reference threshold, and the stomatal status of the leaves is divided. The division steps are as follows: If the stomatal variation coefficient is greater than or equal to a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal closed state; If the stomatal variation coefficient is less than a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal open state.

[0039] The stomatal open state refers to the physiological state in which the stomata on the surface of plant leaves are open. The stomatal pores allow gas exchange and water transpiration to occur. In this state, plants absorb carbon dioxide through their stomata. Used for photosynthesis, releasing oxygen at the same time to the external environment; the stomatal closure state refers to the partial or complete closure of the stomatal pores on the surface of plant leaves, thereby limiting the occurrence of gas exchange and water transpiration. In this state, stomatal closure is a self-protection mechanism adopted by plants to reduce water transpiration loss, which usually occurs under high temperature, drought or other environmental stress conditions.

[0040] According to the stomata closure state, based on the preset spray water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray. At the same time, the stomatal state of the leaves is paid attention to in real time. When the stoma gradually opens, the spray device is automatically closed. In view of the stomata closure state, based on the preset spray water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray. At the same time, the stomata state of the leaves is paid attention to in real time. When the stomata gradually open, the specific steps of automatically closing the spray device are as follows: The porosity variation coefficient The stomatal status of the leaves is divided by comparing with the preset stomatal variation coefficient reference threshold. The judgment rules are as follows: , where is the reference threshold of the stomatal variation coefficient; When the leaf pores are judged to be in a closed state, the water spray device is triggered to start spraying water particles. The spraying effect is automatically adjusted according to the current leaf state, and fine water mist particles are sprayed. The calculation expression is as follows: , where The smaller the diameter of the sprayed water particles, the finer the water mist will be and the more evenly it will adhere to the surface of the leaves. It is the basic particle size parameter of the spraying system. It is the adjustment coefficient, which is used to dynamically adjust the particle size and pore change coefficient The relationship between the porosity variation coefficient and the particle size indicates the sensitivity of the porosity variation coefficient to the particle size when it deviates from the reference value. This step ensures that when the stomata are closed, the system sprays a finer mist of water to quickly promote the opening of the stomata.

[0041] During the spraying process, the stomata status of the leaves is monitored in real time to obtain the latest stomata change coefficient value, and continue to be consistent with the reference threshold value of the stomatal variation coefficient By comparison, if the pore state tends to be open during the spraying process, the spraying parameters are adjusted dynamically to adjust the spraying flow rate. The calculation expression is as follows: , where is the water spray flow rate, reflecting the spraying intensity, is the initial spraying flow parameter, defining the default flow upper limit of the spraying device. is the attenuation coefficient, which controls the rate of decrease of flow rate when the pore state approaches the threshold; This step ensures that the water flow rate decreases gradually as the pores gradually open, avoiding waste of resources due to excessive spraying.

[0042] When real-time monitoring results show When the blade pores are judged to be in an open state, the water spray device is automatically closed. The control rules for closing the water spray are as follows: , where It is the operating status parameter of the water spraying device. The value of 1 means the water spraying is turned on, and 0 means the water spraying is stopped.

[0043] Through this logic, the system can respond to changes in the stomata state of the blades in real time, ensuring that water is sprayed only when needed, optimizing water resource utilization efficiency.

[0044] By precisely adjusting the spraying effect of the water spray device, the stomata of citrus leaves in a closed state can be quickly opened, while ensuring the efficient use of water resources and the optimization of the plant growth environment. When the stomata of the leaves are in a closed state, the transpiration effect is weakened, resulting in an increase in the temperature of the leaves, an imbalance in water metabolism, and even affecting photosynthesis and nutrient absorption. Through fine mist spraying, water particles can be evenly attached to the surface of the leaves to form a thin and stable water film, which improves the efficiency of water absorption and evaporation, thereby reducing the surface temperature of the leaves, restoring transpiration, and promoting the reopening of stomata. In addition, by paying attention to the stomatal state of the leaves in real time, the changes in the stomata can be dynamically monitored, and the start and stop of the water spray device can be intelligently adjusted according to the monitoring results to ensure that the water spray device is automatically closed after the stomata are gradually opened. This not only prevents the waste of water resources caused by excessive spraying, but also avoids the adverse reactions that may be caused by excessive water accumulation on the leaf surface (such as excessive closure of stomata or excessive burden on leaves). Therefore, this step plays a vital role in achieving stomatal state regulation, optimizing the plant growth environment, and improving the efficiency of water resource utilization.

[0045] When the stomata are open, the water spray device does not work, which reduces the waste of water resources. At the same time, it continuously pays attention to the stomata status of the leaves and maintains the ability of real-time monitoring and dynamic adjustment. The purpose is to achieve efficient use of water resources while maintaining the optimal physiological state of plant leaves. When the stomata are open, the plant is already able to carry out normal gas exchange and temperature regulation through transpiration. At this time, turning off the sprinkler can effectively prevent unnecessary water spraying and avoid waste of resources. At the same time, continuous attention to the state of the stomata and the ability to maintain real-time monitoring and dynamic adjustment can ensure that the system responds quickly when the state of the stomata changes. For example, when the stomata are closed due to changes in the external environment, the sprinkler can be restarted in time to replenish water. This dynamic adjustment mechanism not only optimizes the efficiency of water spraying, but also balances the water demand of plants and the rational use of resources to the greatest extent through intelligent regulation, further promoting the healthy growth of plants and increasing yields.

[0046] The present invention effectively optimizes the water spraying efficiency through an intelligent water spraying adjustment mechanism. When the leaves are in a stomatal closed state, the system generates a stomatal variation coefficient through a machine learning model based on precise indicators such as the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index, accurately judges the stomatal state, and starts the water spray device only when the stomatal state is closed. In addition, the water spray device adopts a delicate mist spraying method to further enhance the adhesion and uniformity of water and prevent the loss problem caused by excessively large water particles. At the same time, when it is detected that the stomata are gradually opening, the system will automatically turn off the water spray device to avoid unnecessary water spraying. This method of dynamic adjustment and precise control not only reduces the waste of water resources, but also improves the utilization efficiency of every drop of water, providing growers with significant advantages in water resource management and cost savings.

[0047] The present invention monitors the stomatal state of citrus leaves in real time. The solution provides an appropriate amount of water spray when the stoma is closed to reduce the leaf temperature, improve the water balance, and promote the reopening of the stoma, thereby optimizing the photosynthesis and transpiration of the leaves. When the stoma is in an open state, the plant can effectively exchange gases and regulate temperature through its own transpiration. At this time, closing the water spray device not only prevents excess water, but also avoids the risk of disease caused by the over-humid environment of the leaves. At the same time, the solution has the ability of continuous monitoring and dynamic adjustment, can quickly respond to changes in the external environment, and provide water supplement in time when the stoma is re-closed, so as to ensure that the plant is always in the best physiological state under different environmental conditions. Through this intelligent control mechanism, the healthy growth of plants is significantly improved, and the gas exchange efficiency and nutrient absorption capacity of the leaves are also optimized, which ultimately improves the yield and quality of citrus, providing significant economic and ecological benefits for agricultural production.

[0048] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for reducing the midday rest ratio of stomata in citrus fruits, characterized in that: The following steps are involved: First, multispectral cameras were installed in citrus growing areas to obtain real-time information on the status of citrus leaves. After the multispectral camera collects the leaf status information, the data is stored and organized into a systematic data set, and the key features reflecting the stomata being in a closed state are extracted from the established data set; Within the detection window, the extracted key features are analyzed in detail, and the stomatal status of the leaves is intelligently evaluated based on the analyzed key features; Based on the evaluation results, the stomatal states of leaves were divided into two categories: stomatal open state and stomatal closed state; According to the stomata closure state, based on the preset spray water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray. At the same time, the stomatal state of the leaves is paid attention to in real time. When the stoma gradually opens, the spray device is automatically closed. When the stomata are open, the water spray device does not work, reducing water waste. At the same time, it continuously pays attention to the stomatal status of the blades and maintains the ability of real-time monitoring and dynamic adjustment.

2. The method for reducing the midday rest ratio of citrus stomata according to claim 1, characterized in that: Extract key features reflecting the closed state of stomata from the established data set. The extracted features include the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface. Under the detection window, analyze the thermal distribution on the leaf surface and the fluorescence emission characteristics on the leaf surface to generate a thermal steady-state deviation index and a stomatal edge fluorescence enhancement index, respectively. The thermal steady-state deviation index reflects the stomatal state by quantifying the uniformity of the thermal distribution on the leaf surface. The stomatal edge fluorescence enhancement index reflects the physiological state of stomata by quantifying the changes in fluorescence emission intensity in the stomatal edge area.

3. The method for reducing the midday rest ratio of citrus stomata according to claim 2, characterized in that: After obtaining the thermal steady-state deviation index and stomatal edge fluorescence enhancement index generated by analyzing the extracted key features, a comprehensive analysis is performed on the thermal steady-state deviation index and the stomatal edge fluorescence enhancement index to generate a stomatal variation coefficient, and the stomatal state in the leaf is intelligently evaluated through the stomatal variation coefficient.

4. The method for reducing the midday rest ratio of citrus stomata according to claim 3, characterized in that: The generated stomatal variation coefficient is compared and analyzed with the preset stomatal variation coefficient reference threshold, and the stomatal status of the leaves is divided. The division steps are as follows: If the stomatal variation coefficient is greater than or equal to a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal closed state; If the stomatal variation coefficient is less than a preset stomatal variation coefficient reference threshold, the current citrus tree leaf state is classified as a stomatal open state.

5. The method for reducing the midday rest ratio of citrus stomata according to claim 1, characterized in that: In view of the stomata closure state, based on the preset spray water particle parameters, the spraying effect of the water spray device is adjusted to form a fine mist spray. At the same time, the stomata state of the leaves is paid attention to in real time. When the stomata gradually open, the specific steps of automatically closing the spray device are as follows: The porosity variation coefficient The stomatal status of the leaves is divided by comparing with the preset stomatal variation coefficient reference threshold. The judgment rules are as follows: , where is the reference threshold of the stomatal variation coefficient; When the leaf pores are judged to be in a closed state, the water spray device is triggered to start spraying water particles. The spraying effect is automatically adjusted according to the current leaf state, and fine water mist particles are sprayed. The calculation expression is as follows: , where is the diameter of the sprayed water particles, It is the basic particle size parameter of the spraying system. is the adjustment factor; During the spraying process, the stomata status of the leaves is monitored in real time to obtain the latest stomata change coefficient value, and continue to be consistent with the reference threshold value of the stomatal variation coefficient By comparison, if the pore state tends to be open during the spraying process, the spraying parameters are adjusted dynamically to adjust the spraying flow rate. The calculation expression is as follows: , where is the water spray flow rate, is the initial spraying flow parameter, is the attenuation coefficient; When real-time monitoring results show When the blade pores are judged to be in an open state, the water spray device is automatically closed. The control rules for closing the water spray are as follows: , where It is the operating status parameter of the water spraying device. The value of 1 means the water spraying is turned on, and 0 means the water spraying is stopped.

6. The method for reducing the midday rest ratio of citrus stomata according to claim 2, characterized in that: Under the detection window, the specific steps of analyzing the thermal distribution on the blade surface and generating the thermal steady-state deviation index are as follows: In the detection window, the thermal radiation data of the leaves in multiple bands is obtained through a multispectral camera, and the thermal radiation intensity of different areas on the leaf surface is recorded. The thermal radiation intensity of each pixel point is calculated to obtain the corresponding surface temperature, and the surface temperatures of all pixels are combined into a two-dimensional temperature distribution matrix. , the surface temperature calculation expression for each pixel is as follows: , where is the temperature of the pixel point on the leaf surface, is the thermal radiation intensity, is the ability of the blade surface to emit thermal radiation. is the Stefan-Boltzmann constant; On the blade surface, the temperature difference between each pixel and its surrounding pixels reflects the local thermal gradient. By calculating the change rate of the local thermal gradient of the pixel, the distribution dynamics of heat on the blade surface can be quantified. The calculation expression is as follows: , where is the rate of change of the local thermal gradient of the pixel, is the temperature of the pixel on the leaf surface For The temperature change rate of the neighboring points in the horizontal direction is is the temperature of the pixel on the leaf surface For The temperature change rate of the neighboring points in the vertical direction is is the total number of pixel neighbors; According to the local thermal gradient change rate , further calculate the energy dissipation distribution index of each pixel point to quantify the uneven diffusion degree of heat on the blade surface in the detection window. The calculation expression is as follows: , where is the energy dissipation distribution index, is the attenuation coefficient, is the central horizontal coordinate, is the central longitudinal coordinate; The energy dissipation distribution index of all pixels Normalized accumulation, the overall thermal steady-state deviation index is obtained, and the calculation is expressed as follows: , where is the thermal steady-state deviation index, is the thermal gradient amplification factor.

7. The method for reducing the midday rest ratio of citrus stomata according to claim 2, characterized in that: Under the detection window, the specific steps for analyzing the fluorescence emission characteristics of the leaf surface and generating the stomatal edge fluorescence enhancement index are as follows: In the detection window, a multispectral camera is used to monitor citrus leaves in real time to obtain the fluorescence emission characteristics of the leaf surface at different wavelengths. The collected fluorescence data records its spectral reflectance characteristics in pixels. In order to improve the data accuracy, the collected data is preprocessed to correct the influence of light intensity and equipment errors. The calculation is expressed as follows: , where is the raw fluorescence emission intensity, is the spectral correction factor, is the wavelength; In the leaf image, the stomatal edge feature recognition algorithm is used to lock the stomatal edge area, compare the fluorescence intensity changes of different pixels, and extract the characteristic value of the fluorescence emission characteristic change of the stomatal edge area. The calculation expression is as follows: , where is the difference in the fluorescence emission gradient at the stomatal edge area, is the pixel at wavelength The fluorescence intensity change rate under is the pixel coordinate set of the pore edge area, is the pixel coordinate set of the non-edge area; In order to further amplify the fluorescence difference between the stomatal edge area and the non-edge area, the enhanced fluorescence difference feature is generated to quantify the significance of the change in the fluorescence emission characteristics of the stomatal edge. The calculation expression is as follows: , where is the enhanced fluorescence difference feature, is the wavelength weight function, is the second-order rate of change of the raw fluorescence emission intensity, is the exponential amplification term of the second-order fluorescence change; Enhanced fluorescence difference characteristics , generate the stomatal edge fluorescence enhancement index, the calculation expression is as follows: , where is the stomatal edge fluorescence enhancement index, is the local weight distribution function, is a nonlinear amplification function.

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