Sweet potato carbon-nitrogen ratio detection method and system based on multispectral and fluorescence sensors

Abstract

This application relates to the technical field of carbon-nitrogen ratio detection, specifically to a detection method and system for the carbon-nitrogen ratio of sweet potatoes based on multi-spectral and fluorescence sensors, which specifically includes: constructing weight coefficients based on the correlation between the carbon-nitrogen ratio of leaves and the reflectance of each wavelength in each growth stage, combining the multi-spectral and fluorescence spectra of each leaf to obtain coupling eigenvalues; correcting the coupling eigenvalues according to soil conductivity; analyzing the multi-spectral characteristics and fluorescence spectral characteristics of sweet potatoes, constructing multi-spectral indices and fluorescence spectral indices; predicting the carbon-nitrogen ratio of the sweet potato leaves to be detected based on the above characteristics of the leaves, combining the differences between the eigenvectors of the sweet potato leaves to be detected and the leaves of all growth stages, and introducing the carbon-nitrogen ratio of the previous growth stage to calculate the carbon-nitrogen ratio of the sweet potato leaves to be detected; considering that the change of the carbon-nitrogen ratio has a certain continuity and inertia, so as to adapt to the changes in different growth stages of sweet potatoes and improve the detection accuracy of the carbon-nitrogen ratio.

Description

Technical Field

[0001] This application relates to the technical field of carbon-nitrogen ratio detection, and specifically relates to a detection method and system for the carbon-nitrogen ratio of sweet potatoes based on multi-spectral and fluorescence sensors. Background Technique

[0002] As an important food and cash crop, sweet potatoes are different from other crops. The harvested organ of sweet potatoes is the underground tuberous roots, and the carbon-nitrogen ratio directly affects their growth and quality. An excessively high carbon-nitrogen ratio may lead to excessive growth and affect tuberous root development, while an excessively low ratio may affect photosynthesis and disease resistance. Detecting the carbon-nitrogen ratio can help optimize fertilization strategies and improve yield and quality. Traditional laboratory detection has the defects of destructiveness and lag, while carbon-nitrogen ratio detection based on spectral technology can achieve in-situ monitoring in the field, providing a basis for precise regulation during the growth cycle.

[0003] In the related technology "CN103048276A A method for constructing a spectral index for detecting the carbon-nitrogen ratio of crop canopy leaves", relative reflectance is calculated through hyperspectral data, and the spectral slope is calculated in three sub-bands to construct a spectral index, which can eliminate the influence of light differences and reduce the correlation between adjacent wavelengths. However, this method only relies on single spectral data, and since the carbon-nitrogen ratio requirements of sweet potatoes are different at different growth stages. For example, at the seedling stage, the demand for nitrogen is relatively high, which helps to promote the growth and development of plants; during the tuberous root swelling stage and the mature stage, the demand for carbon by sweet potatoes gradually increases, which helps the swelling of tuberous roots and nutrient accumulation. And this method uses a fixed spectral index, without considering the spectral response differences at different growth stages of sweet potatoes and the continuity of carbon-nitrogen ratio changes, and cannot adapt to the spectral response differences at different growth stages of sweet potatoes, resulting in a decrease in detection accuracy. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide a detection method and system for the carbon-nitrogen ratio of sweet potatoes based on multi-spectral and fluorescence sensors. The specific technical solutions adopted are as follows:

[0005] In the first aspect, an embodiment of this application provides a detection method for the carbon-nitrogen ratio of sweet potatoes based on multi-spectral and fluorescence sensors. The method includes the following steps:

[0006] At each growth stage, collect the multi-spectral, fluorescence spectrum, carbon-nitrogen ratio, and environmental data of each leaf of the sweet potato; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity;

[0007] Based on the correlation between the carbon-nitrogen ratio of the leaves and the reflectance of each wavelength at each growth stage, construct the weight coefficient of each wavelength at each growth stage; based on the weight coefficient, and the multi-spectral and fluorescence spectra of each leaf, construct the coupled eigenvalue of the spectral data of each leaf;

[0008] Correct the coupling eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf;

[0009] Conduct feature combination based on the reflectance of characteristic wavelengths in the multispectral data of each leaf to construct the multispectral index of each leaf; conduct feature combination based on the fluorescence intensity and corresponding fluorescence lifetime of characteristic wavelengths in the fluorescence spectrum of each leaf to construct the fluorescence spectrum index of each leaf;

[0010] Construct the feature vector of each leaf based on the corrected value of the spectral data coupling eigenvalue of each leaf, the multispectral index, the fluorescence spectrum index and the environmental data; construct a prediction model according to the feature vectors of all leaf samples at each growth stage and the carbon-nitrogen ratio, and combine the feature vector of the sweet potato leaf to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected;

[0011] Based on the difference between the feature vector of the sweet potato leaf to be detected and the feature vectors of all leaves at all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combine the predicted value to construct the carbon-nitrogen ratio of the sweet potato leaf to be detected.

[0012] In one embodiment, the process of obtaining the weight coefficient of each wavelength in each growth stage is as follows:

[0013] Form a set of the reflectance of the i-th wavelength in the spectral data of all leaves at each growth stage, denoted as the i-th wavelength reflectance set of each growth stage; calculate the mutual information between the elements in the i-th wavelength reflectance set and the corresponding leaf carbon-nitrogen ratio, denoted as the first mutual information value;

[0014] Calculate the sum value of the first mutual information values of all wavelengths in each growth stage, denoted as the first sum value; take the ratio of the first mutual information value of the i-th wavelength in each growth stage to the first sum value as the weight coefficient of the i-th wavelength in each growth stage.

[0015] In one embodiment, the expression of the coupling eigenvalue of the spectral data of each leaf is:

[0016] , where represents the coupling eigenvalue of the spectral data of the current leaf; n represents the number of wavelengths in any spectral data; is the weight coefficient of the i-th wavelength in the growth stage where the current leaf is located; represents the reflectance of the i-th wavelength in the multispectral data of the current leaf; represents the fluorescence lifetime corresponding to the i-th wavelength in the fluorescence spectrum data of the current leaf; represents the preset reference fluorescence lifetime; represents the exponential function with the natural constant e as the base.

[0017] In one of the embodiments, the correction eigenvalue of the spectral data of each blade is corrected, and the expression is:

[0018]

[0019]

[0020] In the formula, is the corrected value of the spectral data coupling eigenvalue of the current blade, represents the coupling eigenvalue of the spectral data of the current blade, is the compensation coefficient corresponding to the soil conductivity of the current blade, is the soil conductivity corresponding to the current blade, and A and B respectively represent the preset intercept and slope, is the tanh function, C represents the reference value of the soil conductivity, and E represents the preset response coefficient.

[0021] In one of the embodiments, the expression of the multispectral index of each blade is:

[0022]

[0023] In the formula, is the multispectral index of the current blade, , , , respectively represent the reflectances at wavelengths of 820 nm, 680 nm, 950 nm, and 760 nm in the multispectral data of the current blade, is the exponential function with the natural constant e as the base; where the wavelength of 680 nm is the wavelength corresponding to the strong absorption peak of chlorophyll a, the wavelength of 820 nm is the wavelength corresponding to the near-infrared plateau region, the wavelength of 760 nm is the wavelength corresponding to the photosystem II sensitive region, and the wavelength of 950 nm is the wavelength corresponding to the water absorption valley.

[0024] In one of the embodiments, the expression of the fluorescence spectral index of each blade is:

[0025]

[0026] In the formula, F is the fluorescence spectral index of the current blade, , are respectively the fluorescence intensities at wavelengths of 685 nm and 740 nm in the fluorescence spectral data of the current blade, , They are the fluorescence lifetimes corresponding to wavelengths of 685 nm and 740 nm in the current leaf fluorescence spectrum data respectively; among them, the wavelength of 685 nm is the wavelength corresponding to the emission peak of chlorophyll a, and the wavelength of 740 nm is the wavelength corresponding to the far-red light emission peak.

[0027] In one embodiment, the process of obtaining the eigenvector of each leaf is as follows:

[0028] Calculate the sum of the normalized values of temperature, humidity, and light intensity in the environmental data of each leaf as the environmental index of each leaf.

[0029] Take the vector composed of the correction value of the spectral data coupling eigenvalue, the multispectral index, the fluorescence spectral index, and the environmental index of each leaf as the eigenvector of each leaf.

[0030] In one embodiment, the process of obtaining the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected is as follows:

[0031] Take the eigenvector and carbon-nitrogen ratio of all leaf samples in each growth stage as the input of the regression prediction model, and train the regression prediction model to obtain the trained regression prediction model for each growth stage.

[0032] Take the eigenvector of the sweet potato leaf to be detected as the input of the regression prediction model corresponding to its growth stage, and the output is the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected.

[0033] In one embodiment, the process of obtaining the carbon-nitrogen ratio of the sweet potato leaf to be detected is as follows:

[0034] Randomly select the eigenvectors of a preset number of leaves in each growth stage, and take the set of the eigenvectors of the preset number of leaves in all growth stages as the growth stage data set.

[0035] Calculate the Euclidean distance between the eigenvector of the sweet potato leaf to be detected and each eigenvector in the growth stage data set, denoted as the first distance; take the normalized value of the sum of all the first distances of the sweet potato leaf to be detected as the dynamic growth stage weight of the sweet potato leaf to be detected.

[0036] Denote the carbon-nitrogen ratio of the sweet potato leaf to be detected as , The expression of

[0037]

[0038] In the formula, is the carbon-nitrogen ratio of the sweet potato leaf to be detected, is the dynamic growth stage weight of the sweet potato leaf to be detected, represents the eigenvector of the sweet potato leaf to be detected, represents the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected, represents the mean value of the carbon-nitrogen ratios of all leaf samples in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, where represents the sweet potato leaf to be detected.

[0039] In a second aspect, the embodiment of the present application further provides a sweet potato carbon-nitrogen ratio detection system based on a multispectral and fluorescence sensor to implement the method described in the first aspect. The system includes:

[0040] Data fusion layer: At each growth stage, collect the multispectral, fluorescence spectrum, carbon-nitrogen ratio, and environmental data of each sweet potato leaf; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity; construct the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of the leaf and the reflectance of each wavelength in each growth stage; based on the weight coefficient, and the multispectral and fluorescence spectra of each leaf, construct the coupled eigenvalue of the spectral data of each leaf; correct the coupled eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf.

[0041] Feature engineering layer: Perform feature combination according to the reflectance of the characteristic wavelengths in the multispectral of each leaf to construct the multispectral index of each leaf; perform feature combination according to the fluorescence intensity and the corresponding fluorescence lifetime of the characteristic wavelengths in the fluorescence spectrum of each leaf to construct the fluorescence spectrum index of each leaf; construct the feature vector of each leaf based on the corrected value of the coupled eigenvalue of the spectral data of each leaf, the multispectral index, the fluorescence spectrum index, and the environmental data.

[0042] Time series modeling layer: Construct a prediction model according to the feature vectors and carbon-nitrogen ratios of all leaf samples in each growth stage, and combine the feature vectors of the sweet potato leaf to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected; based on the difference between the feature vector of the sweet potato leaf to be detected and the feature vectors of all leaves in all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combine the predicted value to construct the carbon-nitrogen ratio of the sweet potato leaf to be detected.

[0043] The embodiment of the present application has at least the following beneficial effects:

[0044] This application constructs the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of leaves and the reflectance of each wavelength in each growth stage; based on the weight coefficient and the multi-spectral and fluorescence spectra of each leaf, the coupled eigenvalue of the spectral data of each leaf is obtained, avoiding the problem that single-modal data is difficult to comprehensively characterize the physiological state of crops, resulting in insufficient detection accuracy; the coupled eigenvalue is corrected according to the soil conductivity, making the calculation result of the coupled eigenvalue more in line with the actual growth characteristics of sweet potato leaves; by analyzing the multi-spectral characteristics and fluorescence spectral characteristics of sweet potatoes, multi-spectral indices and fluorescence spectral indices are constructed, which can more comprehensively characterize the physiological state of sweet potatoes and increase the detection accuracy; based on the above characteristics of the leaves, a regression prediction model for each growth stage is constructed to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaves to be detected, and by combining the difference between the eigenvector of the sweet potato leaves to be detected and the eigenvectors of all leaves in all growth stages, and introducing the carbon-nitrogen ratio of the previous growth stage, the carbon-nitrogen ratio of the sweet potato leaves to be detected is calculated, considering that the change of the carbon-nitrogen ratio has a certain continuity and inertia, so as to adapt to the changes in different growth stages of sweet potatoes and improve the detection accuracy of the carbon-nitrogen ratio. Description of the Drawings

[0045] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0046] Figure 1 It is a flowchart of the steps of the sweet potato carbon-nitrogen ratio detection method based on multi-spectral and fluorescence sensors provided by an embodiment of the present application;

[0047] Figure 2 It is a schematic diagram of the acquisition process of the weight coefficient of each wavelength;

[0048] Figure 3 It is a schematic diagram of the acquisition process of the dynamic growth stage weight. Detailed Embodiments

[0049] In order to further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following will, in conjunction with the drawings and preferred embodiments, describe in detail the specific embodiments, structures, features and effects of the sweet potato carbon-nitrogen ratio detection method and system based on multi-spectral and fluorescence sensors proposed by the present application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures or characteristics in one or more embodiments can be combined in any suitable form.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0051] The following specifically describes the specific solutions of the sweet potato carbon-nitrogen ratio detection method and system based on multi-spectral and fluorescence sensors provided by this application with reference to the accompanying drawings.

[0052] Please refer to Figure 1 , which shows the step flowchart of the sweet potato carbon-nitrogen ratio detection method based on multi-spectral and fluorescence sensors provided by an embodiment of this application. The method includes the following steps:

[0053] Step S1, at each growth stage, collect the multi-spectral, fluorescence spectrum, carbon-nitrogen ratio and environmental data of each sweet potato leaf; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity.

[0054] The growth stages of sweet potato include the seedling stage (0 - 30 days after planting), the branching and tuber formation stage (30 - 60 days), the vigorous growth stage of stems and leaves (60 - 90 days), the tuber swelling stage (90 - 120 days), and the mature harvesting stage (120 - 150 days). At different growth stages of sweet potato, various spectral data of sweet potato leaves are collected. Specifically:

[0055] In the seedling stage, the sweet potato begins to develop roots and form stems and leaves. In this stage, the spectral data of sweet potato leaves are collected every three days; in the branching and tuber formation stage, the tubers begin to swell, and the spectral data of sweet potato leaves are collected every two days; in the vigorous growth stage of stems and leaves, the photosynthesis is the strongest and the nutrient competition is fierce, so the spectral data of sweet potato leaves are collected every day; in the tuber swelling stage, the volume of the tubers grows rapidly, and the spectral data of sweet potato leaves are also collected every day; in the mature harvesting stage, the starch accumulation is completed, and the spectral data of sweet potato leaves are collected once a week.

[0056] Among them, each time the spectral data of sweet potato leaves are collected, N sweet potato leaves are randomly selected, the multi-spectral data of each sweet potato leaf are collected by a spectrometer, and the fluorescence spectral data of each sweet potato leaf are collected by a fluorescence sensor. In the embodiment of this application, the value of N is set to 100. As other embodiments of this application, the implementer can set the value of N according to the actual situation.

[0057] It should be noted that when collecting two kinds of spectral data, ensure that the two kinds of spectral data are collected synchronously in the same leaf area;

[0058] When using a spectrometer to collect multi-spectral data, in the embodiment of this application, the spectral range is set to 500 - 1000 nm to cover the carbon-nitrogen sensitive band, and the field of view angle is set to , the resolution is set to 5 nm, and the value of each wavelength in the obtained multi-spectral data is the reflectance of the leaf to the light of each wavelength;

[0059] When using a fluorescence sensor to collect fluorescence spectral data, in the embodiments of the present application, an LED blue light with a wavelength of 470 nm is used as the excitation light, and the fluorescence spectral data within the wavelength range of 500 - 1000 nm is collected. The values of each wavelength in the fluorescence spectral data are the fluorescence emission intensities of each wavelength in the fluorescence excited by the leaves, and the fluorescence lifetime of each wavelength is obtained. Among them, the fluorescence lifetime refers to the time required for the fluorescence intensity of a fluorescent substance to decay to 1 / e of its initial value after the excitation light is removed, and the acquisition method is a well-known technology, and the specific process will not be elaborated here.

[0060] As other embodiments of the present application, the implementer can set various parameters of the spectrometer and fluorescence sensor according to the actual situation. Perform standard normal variate transformation (SNV) on the collected spectral data to eliminate the influence of leaf curvature.

[0061] Furthermore, after collecting the spectral data of each leaf, the carbon-nitrogen ratio of each leaf is detected. The present application detects the carbon-nitrogen ratio of each leaf through elemental analysis. Among them, elemental analysis is a well-known technology, and the specific process will not be elaborated here.

[0062] It should be noted that for the detection of the carbon-nitrogen ratio of leaves, the present application only provides a method for detecting the carbon-nitrogen ratio. There are many existing methods for detecting the carbon-nitrogen ratio, and the implementer can also use other methods for detecting the carbon-nitrogen ratio to detect the carbon-nitrogen ratio of leaves, and the present application does not make specific restrictions.

[0063] Furthermore, each time spectral data is collected, the growth environment data of sweet potatoes is also collected. Among them, the temperature and humidity of the air are collected through a temperature and humidity sensor, the light intensity is collected through a light intensity sensor, and the soil electrical conductivity (EC) is collected through a four-electrode sensor. The values of temperature, humidity, and light intensity obtained each time spectral data is collected are normalized and then summed, and the result obtained is used as the environmental index of all leaves for that time.

[0064] All the above-collected leaves are used as samples for the training of subsequent regression prediction models.

[0065] Traditional fluorescence intensity is easily interfered by ambient light, leaf thickness, etc., while fluorescence lifetime is more sensitive to microenvironments such as polarity and viscosity, and has a higher correlation with the carbon-nitrogen ratio. Changes in the carbon-nitrogen ratio will directly affect the chlorophyll molecular structure and electron transfer efficiency. When nitrogen is deficient, chlorophyll synthesis is blocked, and the intermolecular energy transfer efficiency decreases, resulting in an extended fluorescence lifetime. In the detection of the carbon-nitrogen ratio of sweet potatoes, by measuring the change in fluorescence lifetime, the dynamic change of the carbon-nitrogen ratio can be indirectly quantified.

[0066] Step S2: Based on the correlation between the leaf carbon-nitrogen ratio and the reflectance at each wavelength in each growth stage, construct the weight coefficient for each wavelength in each growth stage; based on the weight coefficient and the multi-spectral and fluorescence spectra of each leaf, construct the coupled eigenvalue of the spectral data of each leaf.

[0067] The multi-spectral and fluorescence sensors are complementary in the detection of the carbon-nitrogen ratio of sweet potatoes, and it is difficult for single-modal data to comprehensively characterize the physiological state of crops. The multi-spectral reflectance can effectively reflect the pigment content and structural characteristics of leaves, but it is easily affected by environmental interferences such as soil background and light intensity; although the fluorescence lifetime parameter is sensitive to changes in the carbon-nitrogen ratio, the information content is limited when used alone, and the fluorescence intensity is easily affected by factors such as leaf thickness and moisture. The multi-spectral data provides macroscopic structural information, and the fluorescence lifetime reveals the carbon-nitrogen metabolic differences at the molecular level. The combination of the two can construct a more complete carbon-nitrogen ratio characterization system.

[0068] Therefore, couple the multi-spectral data and fluorescence spectral data of each leaf to obtain the coupled eigenvalue of the spectral data of each leaf. The expression is:

[0069]

[0070] In the formula, represents the coupled eigenvalue of the spectral data of the current leaf; n represents the number of wavelengths in any kind of spectral data; is the weight coefficient of the i-th wavelength in the growth stage where the current leaf is located; represents the reflectance of the i-th wavelength in the multi-spectral data of the current leaf; represents the fluorescence lifetime corresponding to the i-th wavelength in the fluorescence spectral data of the current leaf; represents the preset reference fluorescence lifetime. In this embodiment, is set to the average value of the fluorescence lifetimes of all wavelengths of healthy sweet potato leaves; represents the exponential function with the natural constant e as the base. The purpose is to convert the linear change of the fluorescence lifetime into a non-linear modulation factor to enhance the sensitive response to the carbon-nitrogen ratio.

[0071] Among them, the process of obtaining the weight coefficient of the i-th wavelength in the growth stage where the current leaf is located is as follows:

[0072] Obtain the reflectance of the i-th wavelength in all the leaf spectral data collected in this growth stage, and denote the set composed of them as the reflectance set of the i-th wavelength in this growth stage; calculate the mutual information between the elements in this reflectance set and the corresponding leaf carbon-nitrogen ratio, and denote it as the first mutual information value of the i-th wavelength in this growth stage; among them, the calculation of mutual information is a well-known technology, and the specific process will not be elaborated here;

[0073] Calculate the sum of the first mutual information values of all wavelengths in this growth stage, denoted as the first sum value; use the ratio of the first mutual information value of the \(i\)-th wavelength in this growth stage to the first sum value as the weight coefficient of the \(i\)-th wavelength in this growth stage.

[0074] The traditional fixed-weight method assumes that the contributions of all wavelengths to the carbon-nitrogen ratio (CNR) are constant. However, in actual detection, the spectral responses in different growth stages are significantly different, and the spectral sensitive bands in different growth stages are also different. For example, the importance of the nitrogen-sensitive band (680 nm) in the seedling stage is 2-3 times that in the mature stage, while the weight of the sensitive band (950 nm) needs to be increased during the tuber swelling stage. In addition, environmental interferences, such as changes in leaf water content, will cause the information content of some wavelengths to decrease, and fixed weights cannot compensate dynamically. Therefore, the non-linear correlation between the reflectance of each wavelength in the spectrum and the carbon-nitrogen ratio is quantified by mutual information, and the adaptive weight coefficient of each wavelength is constructed.

[0075] Furthermore, the weighted sum of the coupling results of the corresponding wavelength data in the two spectral data is performed through the weight coefficient, deeply combining the macroscopic structural information of the multispectrum and the microscopic metabolic information of the fluorescence, constructing a composite feature that is sensitive to the carbon-nitrogen ratio and resistant to environmental interference, solving the problem of insufficient detection accuracy of traditional single-modal methods, and providing higher-quality input data for subsequent modeling.

[0076] Because when nitrogen is deficient, the molecular structure of chlorophyll changes and the fluorescence lifetime is prolonged. When When, the exponential term decays rapidly, suppressing the contribution of high When, the exponential term approaches 1, retaining the low When, the exponential term approaches 1, retaining the low value reflectance signal. Compared with linear weighting, the exponential function can significantly suppress extreme values and improve the correlation.

[0077] Step S3, correct the coupling eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf.

[0078] Soil conductivity (EC) is an important environmental interference factor affecting the spectral response of plants. An excessively high EC value will cause the stomata of plant leaves to close, change the arrangement of chlorophyll molecules, and shift the reflectance curve. For example, the absorption valley at 760 nm becomes shallower.

[0079] Therefore, a compensation coefficient is constructed according to the magnitude of EC, and the coupling feature is compensated according to the compensation coefficient. First, the expression of the compensation coefficient is:

[0080]

[0081] In the formula, is the compensation coefficient corresponding to the soil conductivity of the current leaf; A and B respectively represent the preset intercept and slope, where the value range of A is 0.4 to 0.6, and the value range of B is 0.2 to 0.4. Preferably, in the embodiments of the present application, the values of A and B are respectively set to 0.5 and 0.3; is the tanh function; is the soil conductivity corresponding to the current leaf; C represents the reference value of the soil conductivity. In the embodiments of the present application, the value of C is set to , which is the optimal EC value for sweet potato growth. When it is lower than this value, the soil fertility is insufficient, and when it is higher than this value, there is a risk of salt stress; E represents the preset response coefficient, and the value in this embodiment is , controlling the slope of the tanh function, so that when the soil conductivity is greater, the change trend of the tanh function is slower, and when the soil conductivity is closer to 0, the change trend of the tanh function is faster, which conforms to the physiological characteristics of plants' response to salt. The sensitivity is high at low concentrations, and the adaptability decreases at high concentrations.

[0082] Thus within range, it adjusts quickly, and tends to be saturated outside this interval. That is, when , (no compensation); for every deviation of EC from the reference value , changes , ensuring that the compensation intensity is positively correlated with the interference degree. When , the weight of the multispectral reflectance is reduced to avoid overestimating the carbon-nitrogen ratio due to soil infertility. When , the weight of the fluorescence lifetime parameter is enhanced, and the anti-interference ability of the fluorescence characteristics is utilized, such as the response of the fluorescence lifetime to salt stress lags behind the spectrum.

[0083] Furthermore, based on the soil conductivity obtained when collecting spectral data for the current leaf, and the compensation coefficient corresponding to the soil conductivity, the coupling eigenvalue of the spectral data of the current leaf is corrected, and the expression is:

[0084]

[0085] In the formula, is the corrected value of the coupling eigenvalue of the spectral data of the current leaf, represents the coupling eigenvalue of the spectral data of the current leaf, is the compensation coefficient corresponding to the soil conductivity of the current leaf, is the soil conductivity corresponding to the current leaf.

[0086] Step S4: Perform feature combination based on the reflectance of characteristic wavelengths in the multispectral data of each leaf to construct the multispectral index of each leaf; perform feature combination based on the fluorescence intensity and corresponding fluorescence lifetime of characteristic wavelengths in the fluorescence spectrum of each leaf to construct the fluorescence spectrum index of each leaf.

[0087] Through dynamic weight allocation and environmental interference compensation, the multispectral reflectance and fluorescence lifetime parameters are efficiently coupled. However, the exponential form is difficult to describe the high-order non-linear relationship between the spectrum and the carbon-nitrogen ratio, such as the exponential growth of starch accumulation during the tuber bulking period. The dynamic weight may fail in extreme environments, such as under drought stress, resulting in abnormal contribution of the fluorescence lifetime parameter. Moreover, a single index cannot retain the detailed information in the original data, such as the subtle fluctuations of the spectral curve. Therefore, it is necessary to extract the features of multispectral data and fluorescence spectral data as a supplement.

[0088] In this embodiment, the characteristic wavelengths of the multispectral data are selected as follows: the wavelength corresponding to the strong absorption peak of chlorophyll a (680 nm), the reflectance at this wavelength reflects the chlorophyll concentration and is positively correlated with the nitrogen content; the wavelength corresponding to the near-infrared plateau region (820 nm), the reflectance at this wavelength characterizes the leaf structure complexity and is positively correlated with carbon accumulation; the wavelength corresponding to the photosystem II sensitive region (760 nm), the reflectance at this wavelength is affected by photochemical quenching and indirectly reflects the light utilization efficiency; the wavelength corresponding to the water absorption valley (950 nm), the reflectance at this wavelength indicates the leaf water content and is negatively correlated with the carbon-nitrogen ratio. The characteristic wavelengths of the fluorescence spectral data are selected as follows: the wavelength corresponding to the emission peak of chlorophyll a (685 nm), the fluorescence intensity at this wavelength reflects the total amount of chlorophyll and is positively correlated with the nitrogen content; the wavelength corresponding to the far-red emission peak (740 nm), the fluorescence intensity at this wavelength is affected by the size of the photosystem II antenna and is related to the light energy distribution efficiency; the fluorescence lifetime at the wavelength of 685 nm, when the carbon-nitrogen ratio decreases, the intermolecular energy transfer efficiency decreases and the lifetime prolongs; the fluorescence lifetime at the wavelength of 740 nm, which reflects the redox state of the photosystem II reaction center and is negatively correlated with the electron transfer rate.

[0089] Furthermore, perform feature combination based on the selected characteristic wavelengths of the multispectral data to construct the multispectral index. In the embodiment of the present application, the expression of the multispectral index can be:

[0090]

[0091] In the formula, is the multispectral index of the current leaf, , , , respectively represent the reflectances at the wavelengths of 820 nm, 680 nm, 950 nm, and 760 nm in the multispectral data of the current leaf, is the exponential function with the natural constant e as the base. In the first part, 820 nm (carbon-sensitive) and 680 nm (nitrogen-sensitive) are selected to enhance the carbon-nitrogen ratio response, and the exponential function part enhances the non-linear relationship between moisture (950 nm) and the photosystem (760 nm). The role of the exponential function is to enhance the spectral data characteristics through non-linear transformation, amplify small differences or compress high-value intervals. When the moisture stress intensifies, the ratio increases, and the exponential term amplifies this change, enhancing the sensitivity to drought-induced abnormal carbon-nitrogen ratios, and capturing the complex interaction between the carbon-nitrogen ratio and environmental factors through exponential transformation;

[0092] According to the characteristic wavelengths of the selected fluorescence spectral data, characteristic combinations are made to construct a fluorescence spectral index. In the embodiments of the present application, the expression of the fluorescence spectral index can be:

[0093]

[0094] In the formula, F is the fluorescence spectral index of the current leaf, 、 are the fluorescence intensities at wavelengths 685 nm and 740 nm in the fluorescence spectral data of the current leaf respectively, 、 are the fluorescence lifetimes corresponding to wavelengths 685 nm and 740 nm in the fluorescence spectral data of the current leaf respectively. The fluorescence intensity ratio is a traditional carbon-nitrogen ratio index, but it is susceptible to environmental light interference. In this scheme, through the time-resolved characteristic, that is, the fluorescence lifetime, the carbon-nitrogen ratio specificity is enhanced, and then through the geometric mean lifetime parameter, the influence of single-wavelength lifetime fluctuations is eliminated.

[0095] As other embodiments of the present application, the implementer can set the characteristic combination method of the characteristic wavelengths according to the actual situation to construct a multi-spectral index and a fluorescence spectral index.

[0096] The construction of the above two spectral indices combines the physiological mechanism of sweet potato carbon-nitrogen metabolism. Through key wavelength combinations and non-linear transformation, the signals related to the carbon-nitrogen ratio are strengthened and environmental interference is suppressed.

[0097] Step S5, construct the characteristic vector of each leaf based on the corrected value of the spectral data coupling eigenvalue of each leaf, the multi-spectral index, the fluorescence spectral index and the environmental data; construct a prediction model according to the characteristic vectors of all leaf samples at each growth stage and the carbon-nitrogen ratio, and combine the characteristic vectors of the sweet potato leaves to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaves to be detected.

[0098] Since the carbon-nitrogen ratio ranges vary significantly at different growth stages of sweet potato, the growth stage of sweet potato is of great significance for carbon-nitrogen ratio detection. For example, there may be significant differences in the carbon-nitrogen ratio between the tuber formation stage and the maturity stage. Therefore, regression prediction models for each growth stage are constructed respectively according to the leaf data of each growth stage. Specifically:

[0099] The vector composed of the correction value of the spectral data coupling eigenvalue, the multispectral index, the fluorescence spectral index, and the environmental index of each leaf is used as the eigenvector of each leaf. Among them, the eigenvector of the t-th leaf is denoted as , where is the correction value of the spectral data coupling eigenvalue of the t-th leaf, is the multispectral index of the t-th leaf, is the fluorescence spectral index of the t-th leaf, is the vector composed of the environmental index of the t-th leaf;

[0100] In each growth stage of sweet potato, the eigenvectors and carbon-nitrogen ratios of all leaf samples in this growth stage are used as the input of the LightGBM regression prediction model for training the LightGBM regression prediction model. Among them, the maximum number of leaves of each tree is set to 25, the maximum depth of the tree is set to 6, the number of trees is set to 1000, the feature ratio used by each tree is 0.8, and the learning rate is set to 0.1. The trained LightGBM regression prediction model for each growth stage is obtained. The training process of the LightGBM regression prediction model is a well-known technology, and the specific steps will not be elaborated.

[0101] Based on the multispectral, fluorescence spectral, carbon-nitrogen ratio, and environmental data of the sweet potato leaf to be detected, the eigenvector of the sweet potato leaf to be detected is obtained by using the same acquisition method as the eigenvector of the above-mentioned leaf samples. The eigenvector of the sweet potato leaf to be detected is used as the input of the LightGBM regression prediction model of the corresponding growth stage of this leaf, and the output is the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected.

[0102] Step S6, based on the difference between the eigenvector of the sweet potato leaf to be detected and the eigenvectors of all leaves in all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combined with the predicted value, construct the carbon-nitrogen ratio of the sweet potato leaf to be detected.

[0103] Randomly select the eigenvectors of x leaves in each growth stage, and use the set composed of the eigenvectors of the x leaves in all growth stages as the growth stage data set; preferably, in the embodiment of the present application, the value of x is set to 10. As other embodiments of the present application, the implementer can set the value of x according to the actual situation.

[0104] Calculate the Euclidean distance between the eigenvector of the sweet potato leaf to be detected and each eigenvector in the growth stage data set, denoted as the first distance; use the normalized value of the sum of all the first distances of the sweet potato leaf to be detected as the dynamic growth stage weight of the sweet potato leaf to be detected.

[0105] Calculate the carbon-nitrogen ratio of the sweet potato leaf to be detected based on the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combined with the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected. The expression is:

[0106]

[0107] In the formula, is the carbon-nitrogen ratio of the sweet potato leaf to be detected, is the dynamic growth stage weight of the sweet potato leaf to be detected, represents the feature vector of the sweet potato leaf to be detected, represents the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected, represents the average value of the carbon-nitrogen ratios of all leaf samples in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located. Among them, represents the sweet potato leaf to be detected. Because the change of the carbon-nitrogen ratio has a certain continuity and inertia, introducing the carbon-nitrogen ratio of the previous growth stage makes the result smoother and more accurate.

[0108] It should be noted that if the growth stage where the sweet potato leaf to be detected is located is the first growth stage, then .

[0109] Table 1 Comparison table of the effects of the solution of this application and the prior art

[0110]

[0111] Among them, the content corresponding to the dynamic mutual information weight in Table 1 is to construct the weight coefficient of each wavelength in each growth stage through the mutual information between the carbon-nitrogen ratio of the leaves and the reflectance of each wavelength in each growth stage, and the content corresponding to the EC compensation function is to construct the compensation coefficient through the magnitude of EC.

[0112] The schematic diagram of the acquisition process of the weight coefficient of each wavelength is as Figure 2 shown, and the schematic diagram of the acquisition process of the dynamic growth stage weight is as Figure 3 shown.

[0113] Based on the same inventive concept as the above method, the embodiment of this application also provides a sweet potato carbon-nitrogen ratio detection system based on a multi-spectral and fluorescence sensor. The system includes:

[0114] Data fusion layer: At each growth stage, collect the multispectral, fluorescence spectrum, carbon-nitrogen ratio, and environmental data of each sweet potato leaf; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity; construct the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of the leaf and the reflectivity of each wavelength in each growth stage; based on the weight coefficient, and the multispectral and fluorescence spectra of each leaf, construct the coupling eigenvalue of the spectral data of each leaf; correct the coupling eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf.

[0115] Feature engineering layer: Perform feature combination according to the reflectivity of the characteristic wavelengths in the multispectral of each leaf to construct the multispectral index of each leaf; perform feature combination according to the fluorescence intensity and corresponding fluorescence lifetime of the characteristic wavelengths in the fluorescence spectrum of each leaf to construct the fluorescence spectrum index of each leaf; construct the feature vector of each leaf based on the corrected value of the coupling eigenvalue of the spectral data of each leaf, the multispectral index, the fluorescence spectrum index, and the environmental data.

[0116] Time series modeling layer: Construct a prediction model according to the feature vectors and carbon-nitrogen ratios of all leaf samples at each growth stage, and combine the feature vectors of the sweet potato leaves to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaves to be detected; based on the difference between the feature vectors of the sweet potato leaves to be detected and the feature vectors of all leaves at all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaves to be detected are located, combine the predicted value to construct the carbon-nitrogen ratio of the sweet potato leaves to be detected.

[0117] In summary, the embodiment of the present application provides a method for detecting the carbon-nitrogen ratio of sweet potatoes based on multispectral and fluorescence sensors, constructs the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of the leaf and the reflectivity of each wavelength in each growth stage; based on the weight coefficient, and the multispectral and fluorescence spectra of each leaf, obtain the coupling eigenvalue of the spectral data of each leaf, avoiding the problem that single-modal data is difficult to comprehensively characterize the physiological state of crops, resulting in insufficient detection accuracy; correct the coupling eigenvalue according to the soil conductivity, making the calculation result of the coupling eigenvalue more in line with the actual growth characteristics of sweet potato leaves; analyze the multispectral characteristics and fluorescence spectrum characteristics of sweet potatoes, construct multispectral indexes and fluorescence spectrum indexes, which can more comprehensively characterize the physiological state of sweet potatoes and increase the detection accuracy; construct a regression prediction model for each growth stage based on the above characteristics of the leaves, obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaves to be detected, combine the difference between the feature vectors of the sweet potato leaves to be detected and the feature vectors of all leaves at all growth stages, and introduce the carbon-nitrogen ratio of the previous growth stage to calculate the carbon-nitrogen ratio of the sweet potato leaves to be detected, considering that the change of the carbon-nitrogen ratio has a certain continuity and inertia, so as to adapt to the changes in different growth stages of sweet potatoes and improve the detection accuracy of the carbon-nitrogen ratio.

[0118] It should be noted that: the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. And the specific embodiments of the present application have been described above. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0119] Each embodiment in the present application is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key points of each embodiment are the differences from other embodiments.

[0120] The above are only the preferred embodiments of the present application and are not intended to limit the present application. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present application shall be included in the protection scope of the present application.

Claims

1. Detection method of carbon-nitrogen ratio of sweet potato based on multispectral and fluorescence sensors, characterized in that The method comprises the following steps: At each growth stage, collect the multispectral, fluorescence spectrum, carbon-nitrogen ratio and environmental data of each sweet potato leaf; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity; Construct the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of the leaf and the reflectance of each wavelength in each growth stage; based on the weight coefficient and the multispectral and fluorescence spectra of each leaf, construct the coupling eigenvalue of the spectral data of each leaf; Correct the coupling eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf; Conduct feature combination according to the reflectance of the characteristic wavelength in the multispectral of each leaf to construct the multispectral index of each leaf; conduct feature combination according to the fluorescence intensity and the corresponding fluorescence lifetime of the characteristic wavelength in the fluorescence spectrum of each leaf to construct the fluorescence spectrum index of each leaf; Construct the feature vector of each leaf based on the corrected value of the coupling eigenvalue of the spectral data of each leaf, the multispectral index, the fluorescence spectrum index and the environmental data; construct a prediction model according to the feature vectors and the carbon-nitrogen ratio of all leaf samples at each growth stage, and combine the feature vector of the sweet potato leaf to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected; Based on the difference between the feature vector of the sweet potato leaf to be detected and the feature vectors of all leaves at all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combine the predicted value to construct the carbon-nitrogen ratio of the sweet potato leaf to be detected; The expression of the coupling eigenvalue of the spectral data of each leaf is: , where represents the coupling eigenvalue of the spectral data of the current leaf; n represents the number of wavelengths in any spectral data; is the weight coefficient of the i-th wavelength in the growth stage where the current leaf is located; represents the reflectance of the i-th wavelength in the multispectral data of the current leaf; represents the fluorescence lifetime corresponding to the i-th wavelength in the fluorescence spectral data of the current leaf; represents the preset reference fluorescence lifetime; represents the exponential function with the natural constant e as the base; The expression for correcting the coupling eigenvalue of the spectral data of each leaf is: In the formula, is the correction value of the spectral data coupling eigenvalue of the current blade, represents the coupling eigenvalue of the spectral data of the current blade, is the compensation coefficient corresponding to the soil conductivity of the current blade, is the soil conductivity corresponding to the current blade, A and B respectively represent the preset intercept and slope, is the tanh function, C represents the reference value of the soil conductivity, and E represents the preset response coefficient; The process of obtaining the carbon-nitrogen ratio of the sweet potato leaf to be detected is: Randomly select the feature vectors of a preset number of leaves in each growth stage, and use the set composed of the feature vectors of the preset number of leaves in all growth stages as the growth stage data set; Calculate the Euclidean distance between the feature vector of the sweet potato leaf to be detected and each feature vector in the growth stage data set, denoted as the first distance; use the normalized value of the sum of all the first distances of the sweet potato leaf to be detected as the dynamic growth stage weight of the sweet potato leaf to be detected; Denote the carbon-nitrogen ratio of the sweet potato leaves to be detected as , , and its expression is: Wherein, is the carbon-nitrogen ratio of the sweet potato leaf to be detected, is the weight of the dynamic growth stage of the sweet potato leaf to be detected, represents the feature vector of the sweet potato leaf to be detected, represents the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected, represents the mean value of the carbon-nitrogen ratios of all leaf samples in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located. Among them, represents the sweet potato leaf to be detected.

2. The sweet potato carbon-nitrogen ratio detection method based on a multi-spectral and fluorescence sensor according to claim 1, wherein, The process of obtaining the weight coefficient of each wavelength in each growth stage is: Form a set of the reflectances of the i-th wavelength in the spectral data of all leaves in each growth stage, denoted as the i-th wavelength reflectance set of each growth stage; calculate the mutual information between the elements in the i-th wavelength reflectance set and the corresponding leaf carbon-nitrogen ratio, denoted as the first mutual information value; Calculate the sum of the first mutual information values of all wavelengths in each growth stage, denoted as the first sum value; use the ratio of the first mutual information value of the i-th wavelength in each growth stage to the first sum value as the weight coefficient of the i-th wavelength in each growth stage.

3. The sweet potato carbon-nitrogen ratio detection method based on a multi-spectral and fluorescence sensor according to claim 1, characterized in that, The expression of the multispectral index of each leaf is: Wherein, is the multispectral index of the current leaf, , , , respectively represent the reflectance at wavelengths of 820 nm, 680 nm, 950 nm, and 760 nm in the multispectral data of the current leaf, is the exponential function with the natural constant e as the base; wherein the wavelength of 680 nm is the wavelength corresponding to the strong absorption peak of chlorophyll a, the wavelength of 820 nm is the wavelength corresponding to the near-infrared plateau region, the wavelength of 760 nm is the wavelength corresponding to the photosystem II sensitive region, and the wavelength of 950 nm is the wavelength corresponding to the water absorption valley.

4. The sweet potato carbon-nitrogen ratio detection method based on a multi-spectral and fluorescence sensor according to claim 1, wherein, The expression of the fluorescence spectrum index of each leaf is: Where F is the fluorescence spectral index of the current leaf, and are the fluorescence intensities at wavelengths 685 nm and 740 nm in the fluorescence spectral data of the current leaf respectively, and are the fluorescence lifetimes corresponding to wavelengths 685 nm and 740 nm in the fluorescence spectral data of the current leaf respectively; among them, the wavelength 685 nm is the wavelength corresponding to the emission peak of chlorophyll a, and the wavelength 740 nm is the wavelength corresponding to the far-red light emission peak.

5. The sweet potato carbon-nitrogen ratio detection method based on multi-spectral and fluorescence sensors according to claim 1, characterized in that, The process of obtaining the feature vector of each leaf is: Calculate the sum of the normalized values of temperature, humidity and light intensity in the environmental data of each leaf as the environmental index of each leaf; The vector composed of the correction value of the spectral data coupling eigenvalue, the multispectral index, the fluorescence spectral index, and the environmental index of each leaf is used as the eigenvector of each leaf.

6. The sweet potato carbon-nitrogen ratio detection method based on a multi-spectral and fluorescence sensor according to claim 1, wherein, The process for obtaining the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected is as follows: The eigenvectors and carbon-nitrogen ratios of all leaf samples in each growth stage are used as the inputs of the regression prediction model for training to obtain the trained regression prediction model for each growth stage; The eigenvector of the sweet potato leaf to be detected is used as the input of the regression prediction model corresponding to its growth stage, and the output is the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected.

7. A sweet potato carbon-nitrogen ratio detection system based on multispectral and fluorescence sensors, implementing the method described in claim 1, characterized in that, The system includes: Data fusion layer: In each growth stage, collect the multispectral, fluorescence spectral, carbon-nitrogen ratio, and environmental data of each sweet potato leaf; obtain the fluorescence lifetime corresponding to each wavelength in the fluorescence spectrum of each leaf; the environmental data includes soil conductivity; construct the weight coefficient of each wavelength in each growth stage based on the correlation between the carbon-nitrogen ratio of the leaf and the reflectance of each wavelength; based on the weight coefficient and the multispectral and fluorescence spectra of each leaf, construct the coupling eigenvalue of the spectral data of each leaf; correct the coupling eigenvalue of the spectral data of each leaf according to the soil conductivity of each leaf. Feature engineering layer: Perform feature combination according to the reflectance of the characteristic wavelengths in the multispectral of each leaf to construct the multispectral index of each leaf; perform feature combination according to the fluorescence intensity and corresponding fluorescence lifetime of the characteristic wavelengths in the fluorescence spectrum of each leaf to construct the fluorescence spectral index of each leaf; construct the eigenvector of each leaf based on the correction value of the spectral data coupling eigenvalue, the multispectral index, the fluorescence spectral index, and the environmental data of each leaf. Time series modeling layer: Construct a prediction model according to the eigenvectors and carbon-nitrogen ratios of all leaf samples in each growth stage, and combine the eigenvector of the sweet potato leaf to be detected to obtain the predicted value of the carbon-nitrogen ratio of the sweet potato leaf to be detected; based on the difference between the eigenvector of the sweet potato leaf to be detected and the eigenvectors of all leaves in all growth stages, and the carbon-nitrogen ratio of the leaves in the previous growth stage of the growth stage where the sweet potato leaf to be detected is located, combine the predicted value to construct the carbon-nitrogen ratio of the sweet potato leaf to be detected.

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