Soil leachate detection method and device based on multimodal spectral feature fusion

Abstract

The present invention provides a soil leachate solution detection method and device based on multimodal spectral feature fusion, relating to the field of soil detection technology. The method comprises: in response to a concentration detection request, irradiating the soil leachate solution with a mixed light beam to obtain multimodal spectral data, wherein the multimodal spectral data includes multiple spectral sub-data belonging to multiple modalities; performing spectral feature extraction on the multiple spectral sub-data to obtain multiple first spectral features; performing feature fusion on the multiple first spectral features to obtain fused features; and performing concentration detection based on the fused features to obtain the nitrogen concentration of the soil leachate solution, thereby improving the detection accuracy of the nitrogen concentration of the soil leachate solution.

Description

Technical Field

[0001] The present invention relates to the field of soil detection technology, and more specifically, to a soil leachate detection method and device based on multimodal spectral feature fusion. Background Art

[0002] Nitrogen is an important nutrient for crop growth, but excessive nitrogen application can cause nitrogen to enter groundwater or surface water through leaching, causing environmental problems such as eutrophication. Soil nitrogen leaching solution testing is an important part of agricultural environmental testing and non-point source pollution prevention and control. The core goal of the test is to quantify nitrate nitrogen ( ) migration flux, providing a basis for precision agriculture and environmental protection.

[0003] Currently, there are at least the following problems in soil detection technologies: Due to the complexity and dynamics of the soil-water system, the detection process faces technical challenges such as sampling errors, insufficient detection sensitivity, and poor real-time performance. In addition, the spectral method used in related technologies to detect nitrogen concentration in soil leachate has low detection accuracy, which directly affects the accuracy and reliability of the detection data, and thus affects the scientific nature of nitrogen loss assessment and the targeted nature of pollution prevention and control measures. Summary of the Invention

[0004] In view of this, the present invention provides a soil leachate detection method and device based on multimodal spectral feature fusion, which can improve detection accuracy.

[0005] One aspect of the present invention provides a soil leachate solution detection method based on multimodal spectral feature fusion, comprising: in response to a concentration detection request, irradiating the soil leachate solution with a mixed light beam to obtain multimodal spectral data, wherein the multimodal spectral data includes multiple spectral sub-data belonging to multiple modalities respectively; performing spectral feature extraction on the multiple spectral sub-data to obtain multiple first spectral features; performing feature fusion on the multiple first spectral features to obtain fused features; and performing concentration detection based on the fused features to obtain the nitrogen concentration of the soil leachate solution.

[0006] According to an embodiment of the present invention, the above-mentioned spectral feature extraction of the above-mentioned multiple spectral sub-data to obtain multiple first spectral features includes: performing feature extraction based on the above-mentioned multiple spectral sub-data to obtain multiple second spectral features; and aligning and matching the above-mentioned multiple second spectral features to obtain the above-mentioned multiple first spectral features.

[0007] According to an embodiment of the present invention, the above-mentioned multiple modalities include a first modality and multiple second modalities; wherein, the above-mentioned alignment and matching of the above-mentioned multiple second spectral features to obtain the above-mentioned multiple first spectral features includes: using the second spectral feature of the above-mentioned first modality as a reference matrix, aligning the second spectral features of each of the above-mentioned multiple second modalities based on the above-mentioned reference matrix, to obtain the third spectral features of each of the above-mentioned multiple second modalities; and using an iterative nearest point algorithm to optimize and align the third spectral features of each of the above-mentioned multiple second modalities with the second spectral features of the above-mentioned first modality, to obtain the first spectral features of each of the above-mentioned multiple second modalities, wherein the above-mentioned multiple first spectral features include the second spectral features of the above-mentioned first modality and the first spectral features of each of the above-mentioned multiple second modalities.

[0008] According to an embodiment of the present invention, the above-mentioned use of the iterative nearest point algorithm to optimize and align the third spectral features of each of the multiple second modalities with the second spectral features of the first modality to obtain the first spectral features of each of the multiple second modalities includes: using the above-mentioned iterative nearest point algorithm to calculate the matching error based on the third spectral features of each of the multiple second modalities and the second spectral features of the first modality; updating the third spectral features based on the matching error to obtain the updated third spectral features of each of the multiple second modalities; and when the matching error is less than or equal to an error threshold, determining the updated third spectral features of each of the multiple second modalities as the first spectral features of each of the multiple second modalities.

[0009] According to an embodiment of the present invention, the method further includes: when the matching error is greater than the error threshold, performing optimized registration based on the updated third spectral features of each of the multiple second modalities and the second spectral features of the first modality.

[0010] According to an embodiment of the present invention, the above-mentioned feature extraction is performed based on the above-mentioned multiple spectral sub-data to obtain multiple second spectral features, including: for each spectral sub-data, decentralizing the above-mentioned spectral sub-data to obtain preprocessed spectral sub-data; performing principal component analysis on the above-mentioned preprocessed spectral sub-data to obtain a principal component matrix; and extracting the above-mentioned second spectral features from the above-mentioned principal component matrix.

[0011] According to an embodiment of the present invention, the above-mentioned use of a mixed light beam to irradiate the soil leachate solution to obtain multimodal spectral data includes: using the above-mentioned mixed light beam to irradiate the above-mentioned soil leachate solution to obtain a mixed spectrum, wherein the above-mentioned mixed spectrum includes multiple initial spectra belonging to the above-mentioned multiple modes respectively; performing spatial light modulation on the above-mentioned multiple initial spectra to obtain multiple target spectra; and using a detector to perform photoelectric detection on the above-mentioned multiple target spectra to obtain the above-mentioned multimodal spectral data.

[0012] Another aspect of the present invention provides a soil leaching solution detection device based on multimodal spectral feature fusion, comprising: a fluorescent excitation light source for exciting a first light beam; a broadband light source for exciting a second light beam; a beam combiner for combining the first light beam and the second light beam to obtain a mixed light beam, and outputting the mixed light beam to the soil leaching solution; a detector for photoelectrically detecting the spectrum of the soil leaching solution generated by the mixed light beam to obtain multimodal spectral data, wherein the multimodal spectral data includes multiple spectral sub-data belonging to multiple modalities; and a processor for performing spectral feature extraction on the multiple spectral sub-data to obtain multiple first spectral features, performing feature fusion on the multiple first spectral features to obtain fused features, and performing concentration detection based on the fused features to obtain the nitrogen concentration of the soil leaching solution.

[0013] According to an embodiment of the present invention, the soil leaching solution is used to output a mixed spectrum based on the mixed light beam; the device further includes: a light modulation module, used to receive the mixed spectrum, perform spatial light modulation on the mixed spectrum to obtain multiple target spectra, and project the multiple target spectra to a detector; wherein the detector is also used to perform photoelectric detection on the multiple target spectra to obtain the multimodal spectral data.

[0014] According to an embodiment of the present invention, the above-mentioned light modulation module includes: a grating for performing spectroscopic processing on the above-mentioned mixed spectrum to obtain multiple initial spectra belonging to the above-mentioned multiple modes respectively; a first lens for converging the above-mentioned multiple initial spectra to a spatial light modulator; the above-mentioned spatial light modulator is used to perform spectral spatial distribution modulation on the above-mentioned multiple initial spectra respectively to obtain the above-mentioned multiple target spectra; and a second lens for projecting the above-mentioned multiple target spectra respectively to the multiple arrays of the above-mentioned detectors.

[0015] According to an embodiment of the present invention, multimodal spectral technology is used to obtain multimodal spectral data. Based on the different detection principles of multimodal spectral technology, the detection information of soil leachate can be enriched. Spectral feature extraction of multiple spectral sub-data can compensate for the information differences between multiple modalities. Feature fusion of the first spectral features corresponding to multiple modalities can reduce the interference of other components in the soil leachate, improve the detection accuracy of the nitrogen concentration of the soil leachate, and improve the accuracy and reliability of nitrogen detection data. This allows for more accurate assessment of soil nitrogen loss, improves crop yield and quality, and can also implement targeted pollution prevention and control measures, reduce environmental governance costs, and protect aquatic ecosystems. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The above and other objects, features and advantages of the present invention will become more apparent through the following description of the embodiments of the present invention with reference to the accompanying drawings, in which:

[0017] Figure 1 The following is an operational flow chart of a soil leachate detection method based on multimodal spectral feature fusion according to an embodiment of the present invention;

[0018] Figure 2 A schematic diagram of a soil leachate detection method based on multimodal spectral feature fusion according to an embodiment of the present invention is shown;

[0019] Figure 3 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to an embodiment of the present invention is shown;

[0020] Figure 4 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to another embodiment of the present invention is shown;

[0021] Figure 5 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to another embodiment of the present invention is shown;

[0022] Figure 6 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to yet another embodiment of the present invention is shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the present invention. In the following detailed description, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of embodiments of the present invention. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of known structures and technologies are omitted to avoid unnecessary confusion of the concept of the present invention.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present invention. The terms "comprise", "include", etc. used herein indicate the presence of the features, steps, operations and / or components, but do not exclude the presence or addition of one or more other features, steps, operations or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When expressions such as "at least one of A, B, and C, etc." are used, they should generally be interpreted in accordance with the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0027] Nitrogen in soil is mainly in the form of ammonium nitrogen ( ) and nitrate nitrogen ( Nitrate nitrogen exists in two forms: ammonium and nitrate-nitrogen. Ammonium nitrogen is positively charged, easily adsorbed by soil colloids, and relatively stable. Nitrate nitrogen, on the other hand, is negatively charged and less easily adsorbed by soil. Therefore, it easily migrates with water and enters groundwater or surface water through leaching. Measuring nitrate nitrogen migration flux is key to assessing nitrogen loss risks. By understanding the migration pathways and rates of nitrate nitrogen in soil, the likelihood and extent of its entry into groundwater or surface water can be predicted. Quantifying nitrate nitrogen migration flux can provide a basis for precision agriculture and environmental protection.

[0028] Many factors need to be considered when sampling soil nitrogen leaching solutions. Soil nitrogen leaching solutions exhibit spatial variability. Soil itself is highly spatially heterogeneous, and this, combined with the unevenness of farmland management practices (such as fertilization and irrigation), leads to significant differences in the intensity of nitrogen leaching solutions at different locations. Soil nitrogen leaching solutions also exhibit temporal dynamics. In the short term following rainfall or irrigation events, nitrogen concentrations in soil nitrogen leaching solutions often experience a pulsed increase, which can last from hours to days. If sampling frequency is insufficient, periods of nitrogen variation are missed, leading to errors in the assessment of nitrogen loss. Furthermore, sampling depth can also affect assessment results. Different crops have different root distribution depths, and soil nitrogen leaching solutions primarily occur below the root layer. Scientific sampling depth design, combined with specific crop and soil conditions, is necessary to be ecologically meaningful.

[0029] Sample storage and pretreatment of soil nitrogen leaching solutions can also lead to testing errors. Nitrogen forms (particularly nitrate nitrogen) in soil nitrogen leaching solution samples may continue to transform after sampling. Microbial activity can cause nitrate nitrogen to be reduced to nitrite nitrogen or even nitrogen gas, and light and temperature changes can accelerate this transformation. Suspended particles and colloids in soil nitrogen leaching solution samples may interfere with subsequent analysis and require removal through filtration or centrifugation. However, the filter material can affect the evaluation results, especially the assessment of dissolved organic nitrogen.

[0030] Different analytical instruments have varying sensitivity for detecting the migration flux of nitrate nitrogen. Nitrate nitrogen concentrations in groundwater are typically low, making test strips and reflectance photometry difficult to meet sensitivity requirements. While ion chromatography can measure multiple ions simultaneously, the analysis time for each sample is long, making it difficult to rapidly test large quantities of samples.

[0031] Spectroscopic methods, based on the characteristic absorption of nitrate nitrogen in the ultraviolet and near-infrared bands, utilize chemometric algorithms for qualitative and quantitative analysis. They are rapid, non-destructive, and can simultaneously detect multiple components, making them suitable for online testing. Because soil nitrogen leachate solutions have complex compositions, they contain a large number of interfering substances in addition to nitrate nitrogen.

[0032] By using near-infrared light to measure the absorption characteristics of soil leachate and analyzing the spectral data, rapid detection of components in soil leachate can be achieved. The near-infrared spectral range typically ranges from 700 nm to 2500 nm. Spectral information within this band is related to the chemical bond vibrations of organic and inorganic matter in the soil. Nitrogen (e.g., nitrate) in soil leachate exhibits characteristic absorption peaks in the near-infrared spectrum. By analyzing the intensity and position of these absorption peaks, qualitative and quantitative analysis of nitrogen can be achieved. However, while organic matter has low spectral absorption within the near-infrared band, water molecules have high absorption. This can easily overshadow the absorption characteristics of the target substance, nitrate, due to strong absorption peaks, reducing the quantitative prediction accuracy of the model.

[0033] When ultraviolet-visible light (wavelength range of approximately 190nm to 780nm) passes through a soil leachate, molecules or ions in the solution absorb light of specific wavelengths. The intensity of the absorption is proportional to the concentration of the substance in the soil leachate, in accordance with the Beer-Lambert law. By measuring the absorbance, the concentration of the substance in the soil leachate can be calculated. However, dissolved organic matter has strong absorption at 200nm-220nm, which can seriously interfere with the determination of nitrate nitrogen. Although correction can be made by subtracting the background value at 275nm, this cannot completely eliminate the influence of organic matter in samples with high organic matter content. In addition, chloride ion is another significant interfering substance. High-concentration chloride ion peaks can mask the adjacent nitrate nitrogen peak, affecting the accuracy of integration.

[0034] Therefore, the detection accuracy of nitrogen concentration in soil leachate is low when using a single-mode spectrum.

[0035] The embodiments of the present invention provide a soil leachate detection method and device based on multimodal spectral feature fusion, in order to solve the above technical problems.

[0036] Figure 1The following is an operational flow chart of a soil leachate detection method based on multimodal spectral feature fusion according to an embodiment of the present invention.

[0037] like Figure 1 As shown, the soil leachate detection method based on multimodal spectral feature fusion includes operations S110 to S140.

[0038] In operation S110 , in response to a concentration detection request, the soil leaching solution is irradiated with a mixed light beam to acquire multimodal spectral data.

[0039] According to an embodiment of the present invention, the multimodal spectral data includes a plurality of spectral sub-data respectively belonging to a plurality of modalities.

[0040] In operation S120 , spectrum feature extraction is performed on the plurality of spectrum sub-data to obtain a plurality of first spectrum features.

[0041] In operation S130 , feature fusion is performed on the plurality of first spectral features to obtain a fused feature.

[0042] In operation S140 , concentration detection is performed based on the fused features to obtain the nitrogen concentration of the soil leachate.

[0043] In one example, a partial least squares regression method can be used to establish a prediction model, using soil leachate samples with known nitrogen concentrations as a training set to obtain regression parameters. Based on the fused features and regression parameters, the nitrogen concentration of the soil leachate is obtained.

[0044] In one example, a mixed light beam may include a near-infrared light beam, a fluorescent light beam, and an ultraviolet-visible light beam. The mixed light beam irradiates the soil leaching solution, and the corresponding multi-modalities obtained include near-infrared light modality, fluorescent modality, and ultraviolet-visible light modality. The multiple spectral sub-data of the multiple modalities may be near-infrared light absorption spectrum, fluorescence spectrum, and ultraviolet-visible light absorption spectrum.

[0045] Fluorescence spectroscopy technology can achieve qualitative and quantitative analysis of the components of soil leachate by stimulating fluorescent substances (such as organic matter, humic acid, microbial metabolites, etc.) in the soil leachate and measuring the intensity and wavelength characteristics of the emission spectrum.

[0046] Fluorescence spectroscopy, UV-visible spectroscopy, and near-infrared spectroscopy are three analytical techniques based on the interaction between matter and electromagnetic radiation, but their mechanisms of action and spectral ranges differ significantly. Fluorescence spectroscopy involves the relaxation luminescence of excited-state electrons, UV-visible spectroscopy directly relates to the fundamental absorption of electronic transitions, and near-infrared spectroscopy focuses on the low-energy overtone absorption of molecular vibrations.

[0047] According to an embodiment of the present invention, multimodal spectral technology is used to obtain multimodal spectral data. Based on the different detection principles of multimodal spectral technology, the detection information of soil leachate can be enriched. Spectral feature extraction of multiple spectral sub-data can compensate for the information differences between multiple modalities. Feature fusion of the first spectral features corresponding to multiple modalities can reduce the interference of other components in the soil leachate, improve the detection accuracy of the nitrogen concentration of the soil leachate, and improve the accuracy and reliability of nitrogen detection data. This allows for more accurate assessment of soil nitrogen loss, improves crop yield and quality, and can also implement targeted pollution prevention and control measures, reduce environmental governance costs, and protect aquatic ecosystems.

[0048] According to an embodiment of the present invention, a mixed light beam is used to irradiate a soil leachate solution to obtain a mixed spectrum, wherein the mixed spectrum includes multiple initial spectra belonging to multiple modes respectively; spatial light modulation is performed on the multiple initial spectra to obtain multiple target spectra; and photoelectric detection of the multiple target spectra is performed using a detector to obtain multimodal spectral data.

[0049] According to an embodiment of the present invention, feature extraction is performed based on the plurality of spectral sub-data to obtain a plurality of second spectral features.

[0050] According to an embodiment of the present invention, for each spectral sub-data, decentralized processing is performed on the spectral sub-data to obtain pre-processed spectral sub-data.

[0051] In one example, the spectral subdata can be centered using mean Perform decentralized processing to obtain pre-processed spectral sub-data ,in, , Is a positive integer.

[0052] (1);

[0053] (2);

[0054] in, Represents the average spectral data of the spectral sub-data of the training set during the prediction model training process.

[0055] According to an embodiment of the present invention, for each spectral sub-data, principal component analysis is performed on the preprocessed spectral sub-data to obtain a principal component matrix.

[0056] In one example, a principal component analysis (PCA) method may be used to perform principal component analysis on the preprocessed spectral sub-data.

[0057] The preprocessed spectral sub-data Divide the spectrum by the standard deviation of the multimodal spectral data , get the spectral sub-data after standardization , which can be expressed as:

[0058] (3);

[0059] (4);

[0060] in, express The The first spectral sub-data The absorbance at each wavelength, Represents the first The average absorbance at each wavelength.

[0061] The normalized spectral sub-data , organized as a matrix .

[0062] Calculate the covariance matrix :

[0063] (5);

[0064] in, Represents the matrix transpose, the covariance matrix Perform singular value decomposition to obtain the principal component matrix :

[0065] (6);

[0066] in, and is an orthogonal matrix, The elements on the diagonal are the covariance matrix The diagonal matrix of the singular values ​​of .

[0067] In one example, from the principal component matrix Select the first three column vectors as the spectral principal component vector matrix.

[0068] According to an embodiment of the present invention, for each spectral sub-data, a second spectral feature is extracted from the principal component matrix.

[0069] In one example, from the principal component matrix The second spectral features are extracted from the three modes, and the second spectral features corresponding to the three modes can be expressed as , , .

[0070] According to an embodiment of the present invention, alignment and matching are performed on the plurality of second spectral features to obtain the plurality of first spectral features.

[0071] According to an embodiment of the present invention, the plurality of modalities includes a first modality and a plurality of second modalities.

[0072] In one example, the first modality may be a near-infrared light modality, and the plurality of second modalities may be a fluorescence modality and an ultraviolet-visible light modality.

[0073] According to an embodiment of the present invention, the second spectral feature of the first modality is used as a reference matrix, and the second spectral features of the plurality of second modalities are aligned based on the reference matrix to obtain the third spectral features of the plurality of second modalities.

[0074] In one example, the second spectral features corresponding to the three modalities are , , , as a three-dimensional space coordinate point, with the second spectral feature of the near-infrared light absorption spectrum As the reference matrix, the second spectral feature of the fluorescence spectrum and the second spectral feature of UV-visible absorption spectrum Align with the reference matrix respectively to obtain the third spectral feature of the fluorescence spectrum and the third spectral feature of the UV-visible absorption spectrum .

[0075] (7);

[0076] (8).

[0077] According to an embodiment of the present invention, an iterative closest point algorithm is used to optimize and align the third spectral features of each of the multiple second modalities with the second spectral features of the first modality to obtain the first spectral features of each of the multiple second modalities.

[0078] The Iterative Closest Point (ICP) algorithm can be used to align two models. Through iterative optimization, the distance between the two models is gradually reduced so that the two models match as closely as possible in space.

[0079] According to an embodiment of the present invention, an iterative closest point algorithm is used to calculate a matching error based on the third spectral features of each of the plurality of second modalities and the second spectral features of the first modality.

[0080] In one example, an iterative closest point algorithm is used based on the third spectral feature of the fluorescence spectrum. , the third spectral feature of UV-visible absorption spectrum , the second spectral feature of near-infrared absorption spectrum , calculate the matching error :

[0081] (9);

[0082] in, The number of points representing the second spectral feature, The value range is .

[0083] According to an embodiment of the present invention, the third spectral feature is updated based on the matching error to obtain updated third spectral features of each of the plurality of second modalities.

[0084] In one example, based on the matching error Update the third spectral feature of fluorescence spectrum , the third spectral feature of UV-visible absorption spectrum , and obtain the updated third spectral feature of the fluorescence spectrum , updated third spectral feature of UV-visible absorption spectrum .

[0085] (10);

[0086] (11).

[0087] According to an embodiment of the present invention, when the matching error is less than or equal to the error threshold, the updated third spectral features of each of the plurality of second modalities are determined as the first spectral features of each of the plurality of second modalities.

[0088] According to an embodiment of the present invention, when the matching error is greater than the error threshold, optimized registration is performed based on the updated third spectral features of each of the plurality of second modalities and the second spectral features of the first modality.

[0089] According to embodiments of the present invention, principal component analysis and alignment based on a reference matrix can achieve coarse alignment of the second spectral features of multiple second modalities, thereby extracting key nitrogen information. Using an iterative closest point algorithm, fine alignment of the second spectral features of multiple second modalities can be achieved, compensating for information differences between different modalities. Extracting nitrogen information from different modalities into the same dimension for detection can eliminate interference and improve the accuracy of nitrogen concentration detection.

[0090] According to an embodiment of the present invention, the plurality of first spectral features include the second spectral feature of the first modality and the first spectral features of each of the plurality of second modalities.

[0091] Figure 2 A schematic diagram of a soil leachate detection method based on multimodal spectral feature fusion according to an embodiment of the present invention is shown.

[0092] like Figure 2 As shown, the first modality can be a near-infrared light modality, and the second modality can include a fluorescence modality and an ultraviolet-visible light modality. The spectral sub-data of the near-infrared light, the spectral sub-data of the fluorescence, and the spectral sub-data of the ultraviolet-visible light are decentralized and singular value decomposed to obtain a principal component matrix. The second spectral characteristics of the near-infrared light, the second spectral characteristics of the fluorescence, and the second spectral characteristics of the ultraviolet-visible light are determined from the principal component matrix. Using the second spectral characteristics of the near-infrared light as a reference matrix, the second spectral characteristics of the fluorescence and the second spectral characteristics of the ultraviolet-visible light are aligned and matched with the reference matrix to obtain the third spectral characteristics of the fluorescence and the third spectral characteristics of the ultraviolet-visible light. Determine whether the matching error between the third spectral characteristic of fluorescence and the third spectral characteristic of ultraviolet-visible light and the second spectral characteristic of near-infrared light is greater than an error threshold. If the matching error is greater than the error threshold, update the third spectral characteristic of fluorescence and the third spectral characteristic of ultraviolet-visible light. If the matching error is less than the error threshold, determine the updated third spectral characteristic of fluorescence and the third spectral characteristic of ultraviolet-visible light as the first spectral characteristic of fluorescence and the first spectral characteristic of ultraviolet-visible light, and determine the second spectral characteristic of near-infrared light as the first spectral characteristic of near-infrared light.

[0093] In one example, the first spectral characteristics of the three modalities include the second spectral characteristics of the near-infrared light absorption spectrum , the updated third spectral feature of the fluorescence spectrum and the updated third spectral feature of the UV-visible absorption spectrum .

[0094] Perform feature fusion on the three first spectral features to obtain the fusion feature .

[0095] (12).

[0096] Based on the fusion features, the partial least squares regression method was used to establish the matrix of the prediction model. The soil leachate samples with known nitrogen concentrations were used as training sets to calculate the regression parameters. The prediction model can be expressed as:

[0097] (13);

[0098] in, represents the regression error; represents the nitrogen concentration matrix used in the training set; Represents the fusion features used in the training set.

[0099] Regression parameters The calculation formula can be expressed as:

[0100] (14).

[0101] Based on fusion features Conduct concentration testing to obtain the nitrogen concentration matrix of the soil leaching solution :

[0102] (15).

[0103] The present invention also provides a soil leachate detection device based on multimodal spectral feature fusion.

[0104] Figure 3 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to an embodiment of the present invention is shown.

[0105] like Figure 3 As shown, the soil leachate detection device based on multimodal spectral feature fusion includes a fluorescence excitation light source 210, a broadband light source 220, a beam combiner 230, a detector 240 and a processor 250.

[0106] According to an embodiment of the present invention, a fluorescent excitation light source 210 is used to excite a first light beam. A broadband light source 220 is used to excite a second light beam. A beam combiner 230 is used to combine the first light beam and the second light beam to obtain a mixed light beam, and output the mixed light beam to the soil leaching solution 201. A detector 240 is used to perform photoelectric detection on the spectrum generated by the mixed light beam in the soil leaching solution 201 to obtain multimodal spectral data, wherein the multimodal spectral data includes multiple spectral sub-data belonging to multiple modalities. A processor 250 is used to extract spectral features from the multiple spectral sub-data to obtain multiple first spectral features, perform feature fusion on the multiple first spectral features to obtain a fused feature, and perform concentration detection based on the fused feature to obtain the nitrogen concentration of the soil leaching solution 201.

[0107] In one example, the second light beam may be an ultraviolet-visible light beam or a near-infrared light beam.

[0108] Figure 4 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to another embodiment of the present invention is shown.

[0109] According to an embodiment of the present invention, the soil leaching solution 201 is configured to output a mixed spectrum based on the mixed light beam.

[0110] like Figure 4As shown, the soil leachate detection device based on multimodal spectral feature fusion further includes a light modulation module 260.

[0111] According to an embodiment of the present invention, the optical modulation module 260 is used to receive the mixed spectrum, perform spatial optical modulation on the mixed spectrum to obtain multiple target spectra, and project the multiple target spectra to the detector 240. The detector 240 is also used to perform photoelectric detection on the multiple target spectra to obtain multimodal spectral data.

[0112] Figure 5 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to another embodiment of the present invention is shown.

[0113] like Figure 5 As shown, the light modulation module 260 includes a grating 261 , a first lens 262 , a spatial light modulator 263 and a second lens 264 .

[0114] According to an embodiment of the present invention, grating 261 is used to perform spectroscopic processing on the mixed spectrum to obtain multiple initial spectra belonging to multiple modes. First lens 262 is used to converge the multiple initial spectra onto spatial light modulator 263. Spatial light modulator 263 is used to modulate the spatial distribution of the multiple initial spectra to obtain multiple target spectra. Second lens 264 is used to project the multiple target spectra onto the multiple arrays of detector 240.

[0115] Figure 6 A schematic diagram of a soil leachate detection device based on multimodal spectral feature fusion according to yet another embodiment of the present invention is shown.

[0116] like Figure 6 As shown, the soil leachate detection device based on multimodal spectral feature fusion can also include a control device 270. The control device 270 can be connected to the processor 250, the fluorescence excitation light source 210, and the broadband light source 220. The control device 270 can be used to control the time-sharing operation of the fluorescence excitation light source 210 and the broadband light source 220.

[0117] The above describes embodiments of the present invention. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present invention. Although each embodiment has been described separately above, this does not mean that the measures in each embodiment cannot be advantageously used in combination. Without departing from the scope of the present invention, those skilled in the art may make various substitutions and modifications, which should all fall within the scope of the present invention.

Claims

1. A soil leachate detection method based on multimodal spectral feature fusion, characterized in that: include: In response to a concentration detection request, irradiating the soil leaching solution with a mixed light beam to obtain multimodal spectral data, wherein the multimodal spectral data includes a plurality of spectral sub-data respectively belonging to a plurality of modalities; Performing feature extraction based on the multiple spectral sub-data respectively to obtain multiple second spectral features; Aligning and matching the plurality of second spectral features to obtain a plurality of first spectral features; Performing feature fusion on the plurality of first spectral features to obtain a fused feature; and Performing concentration detection based on the fusion feature to obtain the nitrogen concentration of the soil leachate; Wherein, the multiple modes include a first mode and multiple second modes; The step of aligning and matching the plurality of second spectral features to obtain a plurality of first spectral features includes: Using the second spectral feature of the first modality as a reference matrix, aligning the second spectral features of each of the plurality of second modalities based on the reference matrix to obtain third spectral features of each of the plurality of second modalities; and An iterative closest point algorithm is used to optimize and align the third spectral features of each of the multiple second modalities with the second spectral features of the first modality to obtain the first spectral features of each of the multiple second modalities, wherein the multiple first spectral features include the second spectral features of the first modality and the first spectral features of each of the multiple second modalities.

2. The method according to claim 1, characterized in that The method of using an iterative closest point algorithm to optimize and align the third spectral features of each of the plurality of second modalities with the second spectral features of the first modality to obtain the first spectral features of each of the plurality of second modalities includes: Calculating a matching error based on the third spectral features of each of the plurality of second modes and the second spectral features of the first mode using the iterative closest point algorithm; updating the third spectral feature based on the matching error to obtain updated third spectral features of each of the plurality of second modalities; and When the matching error is less than or equal to an error threshold, the updated third spectral features of each of the plurality of second modalities are determined as the first spectral features of each of the plurality of second modalities.

3. The method according to claim 2, characterized in that The method further comprises: When the matching error is greater than the error threshold, optimized registration is performed based on the updated third spectral features of each of the plurality of second modalities and the second spectral features of the first modality.

4. The method according to claim 1, wherein The extracting features based on the plurality of spectral sub-data to obtain a plurality of second spectral features includes: For each spectral sub-data, performing decentralization processing on the spectral sub-data to obtain pre-processed spectral sub-data; Performing principal component analysis on the preprocessed spectral sub-data to obtain a principal component matrix; and The second spectral feature is extracted from the principal component matrix.

5. The method according to claim 1, characterized in that The method of irradiating the soil solution with a mixed light beam to obtain multimodal spectral data includes: irradiating the soil leachate solution with the mixed light beam to obtain a mixed spectrum, wherein the mixed spectrum includes a plurality of initial spectra respectively belonging to the plurality of modes; performing spatial light modulation on the multiple initial spectra to obtain multiple target spectra; and The multiple target spectra are photoelectrically detected by a detector to obtain the multimodal spectral data.

6. A soil leachate detection device based on multimodal spectral feature fusion, characterized in that: include: a fluorescence excitation light source, for exciting the first light beam; a broadband light source for exciting the second light beam; a beam combiner, configured to combine the first light beam and the second light beam to obtain a mixed light beam, and output the mixed light beam to the soil leaching solution; a detector for photoelectrically detecting a spectrum of the soil leachate solution generated by the mixed light beam to obtain multimodal spectral data, wherein the multimodal spectral data includes a plurality of spectral sub-data respectively belonging to a plurality of modalities; and a processor configured to extract spectral features from the plurality of spectral sub-data to obtain a plurality of second spectral features, align and match the plurality of second spectral features to obtain a plurality of first spectral features, perform feature fusion on the plurality of first spectral features to obtain a fused feature, and perform concentration detection based on the fused feature to obtain the nitrogen concentration of the soil leachate; Wherein, the multiple modes include a first mode and multiple second modes; The step of aligning and matching the plurality of second spectral features to obtain a plurality of first spectral features includes: Using the second spectral feature of the first modality as a reference matrix, aligning the second spectral features of each of the plurality of second modalities based on the reference matrix to obtain third spectral features of each of the plurality of second modalities; and An iterative closest point algorithm is used to optimize and align the third spectral features of each of the multiple second modalities with the second spectral features of the first modality to obtain the first spectral features of each of the multiple second modalities, wherein the multiple first spectral features include the second spectral features of the first modality and the first spectral features of each of the multiple second modalities.

7. The device according to claim 6, characterized in that The soil leaching solution is used to output a mixed spectrum based on the mixed light beam; The device further comprises: an optical modulation module, configured to receive the mixed spectrum, perform spatial optical modulation on the mixed spectrum to obtain a plurality of target spectra, and project the plurality of target spectra to a detector; The detector is further used to perform photoelectric detection on the multiple target spectra to obtain the multimodal spectral data.

8. The device according to claim 7, characterized in that The optical modulation module includes: a grating for performing spectroscopic processing on the mixed spectrum to obtain a plurality of initial spectra respectively belonging to the plurality of modes; a first lens for converging the plurality of initial spectra onto a spatial light modulator; The spatial light modulator is used to perform spectral spatial distribution modulation on the multiple initial spectra respectively to obtain the multiple target spectra; and The second lens is used to project the multiple target spectra to the multiple arrays of the detector respectively.

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