Soil organic carbon on-line detection system based on spectral analysis

Abstract

The invention relates to the technical field of spectral analysis, and particularly discloses a spectral analysis-based soil organic carbon on-line detection system, which comprises a soil detection module, a spectrum acquisition module, a spectrum preprocessing module, a spectrum resampling module and a data processing module, the soil detection module is responsible for obtaining a soil sample and carrying out preliminary analysis; the spectrum acquisition module is used for performing spectrum acquisition on a soil sample by utilizing a hyperspectral technology to obtain spectrum data of the soil sample; the spectrum preprocessing module improves the definition of a spectrum signal by removing background noise; the spectrum resampling module is used for reducing redundant information in spectrum data and optimizing a data structure; and the data processing module combines the spectrum preprocessing and spectrum resampling results, and realizes accurate detection of the organic carbon content of the soil by establishing a detection model.

Description

Technical Field

[0001] The present invention relates to the technical field of spectral analysis, and more specifically, to an online soil organic carbon detection system based on spectral analysis. Background Art

[0002] Soil organic carbon is an important parameter to characterize soil quality. The formation and turnover of soil organic carbon are crucial to reducing greenhouse gas emissions and play an irreplaceable role in the stability and improvement of the entire ecological environment. However, when studying soil organic carbon, traditional measurement methods are costly and time-consuming. Hyperspectral technology can quickly and reliably estimate soil organic carbon and its components. Visible-near infrared reflectance spectroscopy can detect soil organic carbon non-destructively, quickly and efficiently. However, with the improvement of the spectral resolution of visible-near infrared reflectance spectroscopy, background noise and spectral redundant information increase, which reduces the accuracy of spectral detection of soil organic carbon.

[0003] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0004] In order to overcome the above-mentioned defects of the prior art, the present invention provides an online detection system for soil organic carbon based on spectral analysis, which collects soil samples and uses hyperspectral technology to collect spectra of the soil samples, combines spectral preprocessing technology and spectral resampling technology, thereby achieving the purpose of background noise removal and reducing spectral redundant information, and finally establishes a high-precision detection model to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions: A soil organic carbon online detection system based on spectral analysis is characterized in that it includes a soil detection module, a spectral acquisition module, a spectral preprocessing module, a spectral resampling module and a data processing module. The data processing module combines the results of spectral preprocessing and spectral resampling to establish a detection model. The detection model not only reduces the interference of redundant information, but also uses the results of preprocessing and resampling to ensure the accuracy of data analysis. The formula of the detection model is: , In the formula, is the detected soil organic carbon concentration value, is the total number of spectral data points after spectral preprocessing and spectral resampling, For the The wavelength of the spectral data point, After spectral preprocessing and spectral resampling, the wavelength The measured value below satisfy ,in, After preprocessing, the wavelength The spectral data measured below, For the wavelength The resampled spectral data, is the background signal at wavelength The estimated value of is the weight of each wavelength point, is a small positive value to avoid the denominator being zero; the detection model quantifies the difference between the processed spectral signal and the background signal to reflect the concentration of organic carbon in the soil, and adjusts the weight of each wavelength , optimize the sensitivity of the detection model so that it can maintain efficient detection capabilities under different soil conditions.

[0006] As a further solution of the present invention, the soil detection module is responsible for obtaining soil samples and performing preliminary analysis, including the following specific contents: The soil detection module is responsible for the collection and pre-processing of soil samples. The soil detection module is equipped with a high-precision soil drill bit, which is electrically driven to drill at a preset location and depth. The design of the drill bit takes into account the characteristics of different soil types and can effectively penetrate hard soil and clay to ensure the integrity and uniformity of the sample. During the sampling process, the sensor of the soil detection module monitors the temperature, humidity and pH parameters of the soil in real time. The soil drill bit can automatically adjust the collection depth and frequency according to the environmental parameters monitored in real time. When the soil humidity is detected to be too high or too low, the sampling depth is automatically optimized. Under high humidity conditions, the soil detection module will select a shallower sampling depth to avoid collecting too much water to affect the spectral information; under low humidity conditions, the soil detection module selects a deeper sampling point to obtain sufficient soil samples. The soil detection module dynamically adjusts the sampling frequency by analyzing real-time data. If a large change in soil characteristics is detected within a period of time, the soil detection module will increase the sampling frequency of soil samples to ensure that sufficient data is obtained for analysis; After collecting soil samples, the soil detection module will perform preliminary processing on the soil samples. The preliminary processing includes removing large particle impurities and adjusting the moisture content to ensure the accuracy of subsequent analysis. The soil detection module can effectively filter out large particle impurities such as stones and plant residues with diameters larger than the preset standard through the filter aperture based on the difference in soil particle size, thereby avoiding interference of impurities with the scattering and absorption of light during the subsequent spectral acquisition process. The soil detection module uses moisture detection technology to quickly and accurately determine the initial moisture content in the soil sample. Subsequently, according to the preset optimal moisture content range, the soil detection module adopts a precise moisture adjustment mechanism. If the initial moisture content of the soil sample is too high, the soil detection module will start the drying program, and slowly evaporate the moisture in the soil through gentle and uniform heating. At the same time, a high-precision humidity sensor is equipped to monitor the changes in soil moisture content in real time to ensure that the soil moisture gradually decreases to the target range. During the drying process, temperature control is extremely important. Too high a temperature may destroy the organic components in the soil and affect the detection results. Therefore, the drying temperature is generally set between 40°C and 60°C, which can not only ensure effective evaporation of moisture, but also minimize the adverse effects on the soil sample. On the contrary, if the initial moisture content of the soil sample is too low, the soil detection module will perform humidification treatment, using a spray device to evenly spray an appropriate amount of deionized water into the soil sample, and constantly stir the soil so that the moisture can be evenly distributed between the soil particles. Similarly, the humidity sensor is used for real-time monitoring until the soil moisture reaches the ideal range; The soil detection module has a built-in communication unit, which can transmit the real-time collected data and environmental parameters to the data processing module. The communication unit adopts wireless transmission technology. First, after the soil detection module collects various data and environmental parameters of the soil sample, including soil temperature, humidity, pH value, and the collection depth and frequency of the drill bit, the various items and environmental parameters will be immediately digitized and encoded and packaged according to a predetermined data format. During the encoding process, key information such as timestamp and device identification will be added to each data so that the data processing module can accurately identify the source and collection time of the data to ensure the traceability and validity of the data. Subsequently, the communication unit will automatically select the best transmission mode and channel according to the current network environment and the preset transmission strategy. During the transmission process, the built-in error correction algorithm and data verification mechanism of the communication unit begin to play a role. The error correction algorithm can automatically detect and correct a small number of data errors that may occur during the transmission process. , such as individual bit flipping caused by signal interference; the data verification mechanism verifies the integrity and accuracy of the transmitted data, such as using a cyclic redundancy check algorithm to ensure that the data received by the receiver is completely consistent with the data sent by the sender; once a data error is found or the verification fails, the communication unit will automatically initiate a retransmission request to ensure the reliability of the data. When it is detected that the network bandwidth is low or the network delay is high, the communication unit will automatically reduce the data transmission rate to give priority to ensuring the integrity and accuracy of the data. At the same time, by optimizing the data transmission protocol and adjusting the data packet size, the efficiency of data transmission is improved, and data transmission interruptions or delays caused by network problems are reduced. When the data is successfully transmitted to the data processing module, the communication unit will wait for the confirmation feedback information of the data processing module. If the confirmation information is not received within the preset time, the communication unit will try to send the data again until the confirmation information is received, thereby ensuring the reliability and stability of data transmission.

[0007] As a further solution of the present invention, the spectrum acquisition module uses hyperspectral technology to collect spectrum of soil samples to obtain spectrum data thereof, including the following specific contents: The spectrum acquisition module is responsible for efficient and accurate spectrum data acquisition of the collected soil samples. The spectrum acquisition module adopts visible-near infrared spectrum technology, which can obtain the spectrum information of soil samples in a short time, so as to realize real-time monitoring of soil organic carbon content. The spectrum acquisition module includes a high-resolution spectrometer, a light source, a spectrum sensor and a data acquisition unit. The light source is a high-intensity LED light source. The LED light source has a wide spectrum range and can cover the visible light and near infrared regions. The output of the light source is modulated to achieve the best light intensity, so as to improve the signal-to-noise ratio of the spectrum signal. The spectrum sensor is widely used in the measurement of soil samples and can collect spectrum data at different sampling depths and positions. During the data acquisition process, the high-resolution spectrometer splits the incident light through optical elements and converts it into spectrum information of each wavelength. The high-sensitivity detector inside the high-resolution spectrometer can capture weak reflection signals in real time and convert them into digital signals. In order to improve the accuracy and stability of the data, the spectrum acquisition module has a built-in calibration mechanism to regularly calibrate and verify the high-resolution spectrometer to compensate for the fluctuation of the light source intensity and environmental interference.

[0008] As a further solution of the present invention, the spectrum preprocessing module improves the clarity of the spectrum signal by removing background noise, including the following specific contents: The spectrum preprocessing module removes the background noise caused by the improvement of spectral resolution by establishing a spectrum preprocessing model. The formula of the spectrum preprocessing model is: , Where: is the wavelength, After preprocessing, the wavelength The spectral data measured below, For the wavelength The spectral data measured under the condition includes the target signal and background noise. For the wavelength The estimated background noise signal is is the base of natural logarithms, To adjust the parameters of noise suppression strength, is the characteristic wavelength that intersects with the noise background.

[0009] As a further solution of the present invention, the spectrum resampling module reduces redundant information in the spectrum data and optimizes the data structure, including the following specific contents: The spectrum resampling module determines the required target wavelength range and sampling interval according to the collected spectrum data. The spectrum resampling module establishes a spectrum resampling model, processes the original spectrum data, and generates new spectrum data at a specified wavelength, so that the resampled spectrum data is smoother and reduces redundant information. The formula of the spectrum resampling model is: , Where: is the wavelength, For the wavelength The resampled spectral data, is the total number of original spectral data points, For the The wavelength of the spectral data point, For the The wavelength of the spectral data point, is the original spectral signal at wavelength The measured value below A coefficient to control the degree of spectral smoothing; the spectral resampling module automatically evaluates the data quality of each wavelength point, combines the signal-to-noise ratio and spectral characteristics, and selects an appropriate resampling strategy. For bands with obvious spectral characteristics, the spectral resampling module increases the sampling points, and in areas with weaker characteristics, the spectral resampling module reduces the sampling density, thereby maintaining data quality while reducing storage requirements and subsequent computing burdens; after the resampling is completed, the spectral resampling module will also standardize the data, unify the spectral data collected from different sources and conditions into a common scale, eliminate systematic errors caused by instrument differences or changes in operating conditions, and enhance the comparability of data.

[0010] As a further solution of the present invention, the spectrum acquisition module is connected to the soil detection module, the spectrum preprocessing module is connected to the spectrum acquisition module, the spectrum resampling module is connected to the spectrum preprocessing module, and the data processing module is connected to the soil detection module and the spectrum resampling module respectively.

[0011] The present invention consists of a soil detection module, a spectrum acquisition module, a spectrum preprocessing module, a spectrum resampling module and a data processing module. The soil detection module can accurately collect soil samples and conduct preliminary analysis, and adjust the sampling depth and frequency in real time according to soil temperature, humidity, pH value, etc. to ensure sample quality; the spectrum acquisition module uses visible-near infrared spectroscopy technology combined with high-precision equipment and calibration mechanism to achieve efficient and accurate collection of spectral data; the spectrum preprocessing module removes background noise through a model; the spectrum resampling module uses a model to reduce redundant information, optimize data structure and perform standardized processing; the data processing module establishes a detection model, combines spectrum preprocessing and resampling results, reduces interference, ensures accuracy, detects soil organic carbon concentration, and optimizes sensitivity by adjusting weights, so that the system maintains efficient detection capabilities under different soil conditions, solves the problems of high cost and time-consuming traditional soil organic carbon detection methods and background noise and redundant information in hyperspectral technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a structural schematic diagram of an online soil organic carbon detection system based on spectral analysis of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Example 1.

[0015] See also Figure 1 Structural schematic diagram, an embodiment of the present invention provides an online soil organic carbon detection system based on spectral analysis, including a soil detection module, a spectral acquisition module, a spectral preprocessing module, a spectral resampling module and a data processing module; the soil detection module is responsible for acquiring soil samples and performing preliminary analysis; the spectral acquisition module uses hyperspectral technology to perform spectral acquisition on soil samples to obtain their spectral data; the spectral preprocessing module improves the clarity of spectral signals by removing background noise; the spectral resampling module reduces redundant information in spectral data and optimizes data structure; the data processing module combines spectral preprocessing and spectral resampling results to establish a detection model to improve the accuracy of soil organic carbon concentration detection.

[0016] Furthermore, the soil detection module is responsible for obtaining soil samples and performing preliminary analysis, including the following specific contents: The soil detection module is responsible for the collection and pre-processing of soil samples. The soil detection module is equipped with a high-precision soil drill bit, which is electrically driven to drill at a preset location and depth. The design of the drill bit takes into account the characteristics of different soil types and can effectively penetrate hard soil and clay to ensure the integrity and uniformity of the sample. During the sampling process, the sensor of the soil detection module monitors the temperature, humidity and pH parameters of the soil in real time. The soil drill bit can automatically adjust the collection depth and frequency according to the environmental parameters monitored in real time. When the soil humidity is detected to be too high or too low, the sampling depth is automatically optimized. Under high humidity conditions, the soil detection module will select a shallower sampling depth to avoid collecting too much water to affect subsequent analysis; under low humidity conditions, the soil detection module selects a deeper sampling point to obtain sufficient soil samples. The soil detection module dynamically adjusts the sampling frequency by analyzing real-time data. If a large change in soil characteristics is detected within a period of time, the soil detection module will increase the sampling frequency of soil samples to ensure that sufficient data is obtained for analysis; After soil samples are collected, the soil detection module will perform preliminary processing on the soil samples, which includes removing large particle impurities and adjusting the moisture content to ensure the accuracy of subsequent analysis; the soil detection module has a built-in communication unit, which can transmit the real-time collected data and environmental parameters to the data processing module.

[0017] Furthermore, the spectrum acquisition module uses hyperspectral technology to collect spectrum of soil samples and obtains spectrum data thereof, including the following specific contents: The spectrum acquisition module is responsible for efficient and accurate spectrum data acquisition of the collected soil samples. The spectrum acquisition module adopts visible-near infrared spectrum technology, which can obtain the spectrum information of soil samples in a short time, so as to realize real-time monitoring of soil organic carbon content. The spectrum acquisition module includes a high-resolution spectrometer, a light source, a spectrum sensor and a data acquisition unit. The light source is a high-intensity LED light source. The LED light source has a wide spectrum range and can cover the visible light and near infrared regions. The output of the light source is modulated to achieve the best light intensity, so as to improve the signal-to-noise ratio of the spectrum signal. The spectrum sensor is widely used in the measurement of soil samples and can collect spectrum data at different sampling depths and positions. During the data acquisition process, the high-resolution spectrometer splits the incident light through optical elements and converts it into spectrum information of each wavelength. The high-sensitivity detector inside the high-resolution spectrometer can capture the weak reflection signal in real time and convert it into a digital signal to improve the accuracy and stability of the data. The spectrum acquisition module has a built-in calibration mechanism to calibrate and verify the high-resolution spectrometer regularly to compensate for the fluctuation of light source intensity and environmental interference. First, before each soil sample test, the high-resolution spectrometer needs to be preheated to make the high-resolution spectrometer reach a stable working state. The preheating time is set according to the requirements of the high-resolution spectrometer manual. After the preheating is completed, a standard white board is placed at the measurement position of the spectrum sensor. The standard white board has a known and stable spectral reflection characteristic and can be used as a calibration benchmark. Then, the calibration program is started, and the high-resolution spectrometer collects the reflection spectrum signal of the standard white board. The spectrum acquisition module automatically records the measurement values ​​at each wavelength at this time. According to the pre-set calibration algorithm, the collected spectral data of the standard white board is compared with the theoretical spectral data of the standard white board to calculate the deviation value of the high-resolution spectrometer. Finally, the deviation value is applied to the subsequent actual soil sample spectral data collection process to correct the measurement value in real time to compensate for the influence of factors such as light source intensity fluctuation, optical component aging and environmental interference on the measurement results.

[0018] Furthermore, the spectrum preprocessing module improves the clarity of the spectrum signal by removing background noise, including the following specific contents: The spectrum preprocessing module removes the background noise caused by the improvement of spectral resolution by establishing a spectrum preprocessing model. The formula of the spectrum preprocessing model is: , Where: is the wavelength, After preprocessing, the wavelength The spectral data measured below, For the wavelength The spectral data measured under the condition includes the target signal and background noise. For the wavelength The estimated background noise signal is is the base of natural logarithms, To adjust the parameters of noise suppression strength, is the characteristic wavelength that intersects with the noise background.

[0019] Furthermore, the spectrum resampling module reduces redundant information in the spectrum data and optimizes the data structure, including the following specific contents: The spectrum resampling module determines the required target wavelength range and sampling interval according to the collected spectrum data. The spectrum resampling module establishes a spectrum resampling model, processes the original spectrum data, and generates new spectrum data at a specified wavelength, so that the resampled spectrum data is smoother and reduces redundant information. The formula of the spectrum resampling model is: , Where: is the wavelength, For the wavelength The resampled spectral data, is the total number of original spectral data points, For the The wavelength of the spectral data point, For the The wavelength of the spectral data point, is the original spectral signal at wavelength The measured value below A coefficient to control the degree of spectral smoothing; the spectral resampling module automatically evaluates the data quality of each wavelength point, combines the signal-to-noise ratio and spectral characteristics, and selects an appropriate resampling strategy. For bands with obvious spectral characteristics, the spectral resampling module increases the sampling points, and in areas with weaker characteristics, the spectral resampling module reduces the sampling density, thereby maintaining data quality while reducing storage requirements and subsequent computing burdens; after the resampling is completed, the spectral resampling module will also standardize the data, unify the spectral data collected from different sources and conditions into a common scale, eliminate systematic errors caused by instrument differences or changes in operating conditions, and enhance the comparability of data.

[0020] Furthermore, the data processing module combines the spectral preprocessing and spectral resampling results to establish a detection model to improve the accuracy of soil organic carbon concentration detection, including the following specific contents: The data processing module combines the results of spectral preprocessing and spectral resampling to establish a detection model. The detection model not only reduces the interference of redundant information, but also uses the results of preprocessing and resampling to ensure the accuracy of data analysis. The formula of the detection model is: , In the formula, is the detected soil organic carbon concentration value, is the total number of spectral data points after spectral preprocessing and spectral resampling, For the The wavelength of the spectral data point, After spectral preprocessing and spectral resampling, the wavelength The measured value below satisfy ,in, After preprocessing, the wavelength The spectral data measured below, For the wavelength The resampled spectral data, is the background signal at wavelength The estimated value of is the weight of each wavelength point, is a small positive value to avoid the denominator being zero; the detection model quantifies the difference between the processed spectral signal and the background signal to reflect the concentration of organic carbon in the soil, and adjusts the weight of each wavelength , optimize the sensitivity of the detection model so that it can maintain efficient detection capabilities under different soil conditions.

[0021] In this embodiment, the spectrum acquisition module is connected to the soil detection module, the spectrum preprocessing module is connected to the spectrum acquisition module, the spectrum resampling module is connected to the spectrum preprocessing module, and the data processing module is connected to the soil detection module and the spectrum resampling module respectively.

[0022] The present invention consists of a soil detection module, a spectrum acquisition module, a spectrum preprocessing module, a spectrum resampling module and a data processing module. The soil detection module can accurately collect soil samples and conduct preliminary analysis. During the collection process, the soil detection module is equipped with a high-precision soil drill. According to parameters such as soil temperature and humidity, pH value, etc., the soil drill adjusts the sampling depth and frequency in real time to ensure the sample quality; the spectrum acquisition module uses visible-near infrared spectroscopy technology combined with high-precision equipment and calibration mechanism to achieve efficient and accurate collection of spectral data; the spectrum preprocessing module removes background noise through a model; the spectrum resampling module uses a model to reduce redundant information, optimize data structure and perform standardized processing; the data processing module establishes a detection model, combines spectrum preprocessing and resampling results, reduces interference, ensures accuracy, detects soil organic carbon concentration, and optimizes sensitivity by adjusting weights, so that the system maintains efficient detection capabilities under different soil conditions, solves the problems of high cost and time-consuming and labor-intensive traditional soil organic carbon detection methods and background noise and redundant information in hyperspectral technology.

[0023] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0024] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An online soil organic carbon detection system based on spectral analysis, characterized in that: The invention comprises a soil detection module, a spectrum acquisition module, a spectrum preprocessing module, a spectrum resampling module and a data processing module. The spectrum preprocessing module removes background noise caused by the improvement of spectrum resolution by establishing a spectrum preprocessing model, thereby improving the clarity of spectrum signals. The spectrum resampling module determines the required target wavelength range and sampling interval according to the collected spectrum data. The spectrum resampling module establishes a spectrum resampling model, processes the original spectrum data, and generates new spectrum data at a specified wavelength, so that the resampled spectrum data is smoother and redundant information is reduced. The data processing module combines the results of spectrum preprocessing and spectrum resampling to establish a detection model. The detection model not only reduces the interference of redundant information, but also uses the results of preprocessing and resampling to ensure the accuracy of data analysis. The formula of the detection model is: ， In the formula, is the detected soil organic carbon concentration value, is the total number of spectral data points after spectral preprocessing and spectral resampling, For the The wavelength of the spectral data point, After spectral preprocessing and spectral resampling, the wavelength The measured value below satisfy ,in, After preprocessing, the wavelength The measured spectral data, For the wavelength The resampled spectral data, is the background signal at wavelength The estimated value of is the weight of each wavelength point, A small positive value to avoid zero denominator.

2. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The formula of the spectral preprocessing model is: ， Where: is the wavelength, After preprocessing, the wavelength The spectral data measured below, For the wavelength The spectral data measured under the condition includes the target signal and background noise. For the wavelength The estimated background noise signal is is the base of natural logarithms, To adjust the parameters of noise suppression strength, is the characteristic wavelength that intersects with the noise background.

3. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The formula of the spectral resampling model is: ， Where: is the wavelength, For the wavelength The resampled spectral data, is the total number of original spectral data points, For the The wavelength of the spectral data point, For the The wavelength of the spectral data point, is the original spectral signal at wavelength The measured value below is a coefficient that controls the degree of spectral smoothing.

4. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The spectrum acquisition module is connected to the soil detection module, the spectrum preprocessing module is connected to the spectrum acquisition module, the spectrum resampling module is connected to the spectrum preprocessing module, and the data processing module is connected to the soil detection module and the spectrum resampling module respectively.

5. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The soil detection module is responsible for obtaining soil samples. The soil detection module is equipped with a high-precision soil drill bit, which can automatically adjust the sampling depth and frequency according to the soil temperature, humidity and pH value; the soil drill bit can select a shallower sampling depth under high humidity conditions and a deeper sampling point under low humidity conditions.

6. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The soil detection module performs preliminary processing including removing large particle impurities and adjusting the moisture content. It has a built-in communication unit that transmits the real-time collected data and environmental parameters to the data processing module.

7. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The spectrum acquisition module includes a high-resolution spectrometer, a high-intensity LED light source, a spectrum sensor and a data acquisition unit. The LED light source has a wide spectral range, and its output is modulated to improve the signal-to-noise ratio; the spectrum sensor collects spectrum data at different sampling depths and positions; the high-resolution spectrometer splits light through optical elements and captures the reflected signal with a high-sensitivity detector and converts it into a digital signal.

8. The online soil organic carbon detection system based on spectral analysis according to claim 1 is characterized in that: The spectrum resampling module automatically evaluates the quality of wavelength point data and selects a resampling strategy, increases sampling points for bands with obvious spectral features, and reduces sampling density in regions with weaker features. The spectrum resampling module performs standardization on the data after resampling.

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