An on-line detection system for soil organic carbon based on spectral analysis

Abstract

The present invention relates to the technical field of spectral analysis, and specifically discloses an on-line detection system for soil organic carbon based on spectral analysis, which includes 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 obtaining soil samples and performing preliminary analysis; the spectral acquisition module uses hyperspectral technology to collect the spectra of soil samples and 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 the spectral data and optimizes the data structure; the data processing module combines the results of spectral preprocessing and spectral resampling, and realizes accurate detection of soil organic carbon content 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 on-line soil organic carbon detection system based on spectral analysis. Background Art

[0002] Soil organic carbon is an important parameter for characterizing soil quality. The formation and turnover of soil organic carbon are crucial for 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, and 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, the background noise and spectral redundant information increase, which reduces the accuracy of spectral detection of soil organic carbon.

[0003] To solve the above problems, a technical solution is provided. Summary of the Invention

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

[0005] To achieve the above object, the present invention provides the following technical solution:

[0006] An on-line soil organic carbon detection system based on spectral analysis, characterized by comprising 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:

[0007] ,

[0008] 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, is the wavelength of the th spectral data point, is the measurement value after spectral preprocessing and spectral resampling at wavelength , satisfies , where is the spectral data measured at wavelength after preprocessing, is the spectral data resampled at wavelength , is the estimated value of the background signal at wavelength , is the weight of each wavelength point, is a small positive value to avoid a zero denominator; the detection model quantifies the difference between the processed spectral signal and the background signal, reflects the concentration of organic carbon in the soil, and optimizes the sensitivity of the detection model by adjusting the weight of each wavelength so that it can maintain high detection ability under different soil conditions.

[0009] As a further aspect of the present invention, the soil detection module is responsible for obtaining soil samples and performing preliminary analysis, including the following specific contents:

[0010] The soil detection module is responsible for the collection and pretreatment of soil samples. The soil detection module is equipped with a high-precision soil drill, which is driven electrically to drill at a preset location and depth. The design of the drill 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 samples. During the sampling process, the sensors of the soil detection module real-time monitor the temperature, humidity and pH value parameters of the soil. The soil drill can automatically adjust the sampling depth and frequency according to the real-time monitored environmental parameters. 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 and affecting the spectral information; while 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 the real-time data. If significant changes in soil characteristics are detected within a period of time, the soil detection module will increase the sampling frequency of soil samples to ensure sufficient data for analysis;

[0011] After collecting soil samples, the soil detection module will conduct 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. Based on the difference in soil particle size, the soil detection module can effectively screen out large particle impurities such as stones and plant residues with diameters larger than the preset standard through the filter screen aperture, thereby avoiding interference caused by the scattering and absorption of light by impurities during the subsequent spectral acquisition process. The soil detection module uses moisture detection technology to quickly and accurately measure the initial moisture content in the soil samples. 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. Through a gentle and uniform heating method, the moisture in the soil will slowly evaporate. At the same time, a high-precision humidity sensor is equipped to monitor the change of soil moisture content in real time to ensure that the soil moisture gradually decreases to the target range. During the drying process, the control of temperature is extremely important. Excessive temperature may damage the organic components in the soil and affect the test results. Therefore, the drying temperature is generally set between 40°C and 60°C, which can not only ensure the effective evaporation of moisture but also minimize the adverse effects on the soil samples. On the contrary, if the initial moisture content of the soil sample is too low, the soil detection module will carry out humidification treatment. Using a spray device, an appropriate amount of deionized water is evenly sprayed into the soil sample, and the soil is continuously stirred at the same time to make the moisture evenly distributed between the soil particles. Similarly, through real-time monitoring by the humidity sensor until the soil moisture reaches the ideal range;

[0012] The soil detection module is built with a 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 of the soil sample and environmental parameters, including soil temperature, humidity, pH value, and the collection depth and frequency of the drill bit, etc., the various data and the environmental parameters will be immediately digitized and encoded and encapsulated in a predetermined data format. During the encoding process, key information such as time stamps and device identifiers will be added to each data so that the data processing module can accurately identify the source and collection time of the data, ensuring the traceability and effectiveness of the data. Subsequently, the communication unit will automatically select the best transmission method and channel according to the current network environment and the preset transmission strategy. During the transmission process, the error correction algorithm and data verification mechanism built in the communication unit start to play a role. The error correction algorithm can automatically detect and correct a small amount of data errors that may occur during the transmission process, such as individual bit flips caused by signal interference; the data verification mechanism verifies the integrity and accuracy of the transmitted data, for example, by using the cyclic redundancy check algorithm, to ensure that the data received by the receiving party is exactly the same as the data sent by the sending party. Once data errors or verification failures are found, the communication unit will automatically initiate a retransmission request to ensure the reliability of the data. When it detects that the network bandwidth is low or the network latency is high, the communication unit will automatically reduce the data transmission rate, give priority to ensuring the integrity and accuracy of the data, and at the same time improve the data transmission efficiency by optimizing the data transmission protocol and adjusting the data packet size, etc., reducing data transmission interruptions or delays caused by network problems. When the data is successfully transmitted to the data processing module, the communication unit will wait for the confirmation feedback information from the data processing module. If no confirmation information is received within the preset time, the communication unit will try to send the data again until the confirmation information is received, thus ensuring the reliability and stability of the data transmission.

[0013] As a further solution of the present invention, the spectral acquisition module uses hyperspectral technology to acquire the spectrum of the soil sample and obtain its spectral data, including the following specific contents:

[0014] The spectral acquisition module is responsible for efficiently and accurately acquiring spectral data of the collected soil samples. The spectral acquisition module adopts visible-near-infrared spectroscopy technology, which can obtain the spectral information of soil samples in a short time, so as to realize the real-time monitoring of soil organic carbon content. The spectral acquisition module includes a high-resolution spectrometer, a light source, a spectral sensor and a data acquisition unit. The selected light source is a high-intensity LED light source. The LED light source has a wide spectral range, which 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 spectral signal. The spectral sensor is widely used in the measurement of soil samples and can collect spectral 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 spectral 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. To improve the accuracy and stability of the data, the spectral acquisition module is built-in with a calibration mechanism to regularly calibrate and verify the high-resolution spectrometer to compensate for the fluctuations of the light source intensity and environmental interference.

[0015] As a further solution of the present invention, the spectral preprocessing module improves the clarity of the spectral signal by removing background noise, including the following specific contents:

[0016] The spectral preprocessing module removes the background noise caused by the improvement of spectral resolution by establishing a spectral preprocessing model. The formula of the spectral preprocessing model is:

[0017] ,

[0018] In the formula: is the wavelength, is the spectral data measured at wavelength after preprocessing, is the spectral data measured at wavelength , including the target signal and background noise, is the estimated background noise signal at wavelength , is the base of the natural logarithm, is a parameter for adjusting the noise suppression intensity, is the characteristic wavelength intersecting with the noise background.

[0019] As a further solution of the present invention, the spectral resampling module reduces the redundant information in the spectral data and optimizes the data structure, including the following specific contents:

[0020] The spectral resampling module determines the required target wavelength range and sampling interval based on the collected spectral data. The spectral resampling module establishes a spectral resampling model to process the original spectral data and generate new spectral data at specified wavelengths, making the resampled spectral data smoother and reducing redundant information. The formula of the spectral resampling model is:

[0021] ,

[0022] In the formula: is the wavelength, is the spectral data after resampling at wavelength , is the total number of original spectral data points, is the th wavelength of the spectral data point, is the th wavelength of the spectral data point, is the measured value of the original spectral signal at wavelength ; is the coefficient controlling the spectral smoothness. 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, while in regions with weaker characteristics, the spectral resampling module reduces the sampling density, thereby reducing the storage requirements and subsequent calculation burden while maintaining the data quality. After resampling, the spectral resampling module also performs standardization processing on the data, unifying the spectral data collected under different sources and conditions to a common scale, eliminating systematic errors caused by instrument differences or changes in operating conditions, and enhancing the comparability between data.

[0023] As a further solution of the present invention, the spectral acquisition module is connected to the soil detection module, the spectral preprocessing module is connected to the spectral acquisition module, the spectral resampling module is connected to the spectral preprocessing module, and the data processing module is respectively connected to the soil detection module and the spectral resampling module.

[0024] The present invention consists of 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 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 the quality of samples. The spectral acquisition module uses visible-near-infrared spectroscopy technology combined with high-precision equipment and calibration mechanisms to achieve efficient and accurate acquisition of spectral data. The spectral preprocessing module removes background noise through a model. The spectral resampling module uses a model to reduce redundant information, optimize the data structure, and perform standardization processing. The data processing module establishes a detection model, combines the results of spectral preprocessing and resampling, reduces interference, ensures accuracy, detects the soil organic carbon concentration, optimizes the sensitivity by adjusting weights, and enables the system to maintain high detection ability under different soil conditions, solving the problems of high cost, long time consumption of traditional soil organic carbon detection methods, and background noise and redundant information in hyperspectral technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 FIG. is a schematic structural diagram of an on-line soil organic carbon detection system based on spectral analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] Embodiment 1.

[0028] Referring to Figure 1 the structural schematic diagram, the embodiment of the present invention provides an on-line 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 obtaining soil samples and conducting preliminary analysis; the spectral acquisition module uses hyperspectral technology to collect the spectrum of soil samples and 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 the data structure; the data processing module combines the results of spectral preprocessing and spectral resampling, and improves the accuracy of detecting the soil organic carbon concentration by establishing a detection model.

[0029] Further, the soil detection module is responsible for obtaining soil samples and conducting preliminary analysis, including the following specific contents:

[0030] The soil detection module is responsible for the collection and pretreatment of soil samples. The soil detection module is equipped with a high-precision soil drill bit, which is driven electrically 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 samples. During the sampling process, the sensors of the soil detection module monitor the temperature, humidity, and pH value parameters of the soil in real time. The soil drill bit can automatically adjust the sampling depth and frequency according to the real-time monitored environmental parameters. 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 and affecting subsequent analysis; while under low humidity conditions, the soil detection module will select a deeper sampling point to obtain sufficient soil samples. The soil detection module dynamically adjusts the sampling frequency by analyzing the real-time data. If significant changes in soil characteristics are detected within a certain period, the soil detection module will increase the sampling frequency of soil samples to ensure sufficient data for analysis;

[0031] After the soil samples are collected, 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 is built-in with a communication unit, and the communication unit can transmit the real-time collected data and environmental parameters to the data processing module.

[0032] Furthermore, the spectral acquisition module uses hyperspectral technology to acquire the spectral data of soil samples, including the following specific content:

[0033] The spectral acquisition module is responsible for the efficient and accurate acquisition of spectral data of the collected soil samples. The spectral acquisition module adopts visible-near infrared spectroscopy technology and can obtain the spectral information of soil samples in a short time, so as to realize the real-time monitoring of soil organic carbon content. The spectral acquisition module includes a high-resolution spectrometer, a light source, a spectral sensor, and a data acquisition unit. The selected light source is a high-intensity LED light source. The LED light source has a wide spectral range and can cover the visible and near infrared regions. The output of the light source is modulated to achieve the optimal light intensity, thereby improving the signal-to-noise ratio of the spectral signal. The spectral sensor is widely used in the measurement of soil samples and can collect spectral 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 spectral information of each wavelength. The highly sensitive detector inside the high-resolution spectrometer can capture weak reflection signals in real time and convert them into digital signals to improve the accuracy and stability of the data;

[0034] The spectral acquisition module has a built-in calibration mechanism that regularly calibrates and validates the high-resolution spectrometer to compensate for fluctuations in light source intensity and environmental interference. First, before each soil sample detection, the high-resolution spectrometer needs to be preheated to bring it to a stable working state, and the preheating time is set according to the requirements of the high-resolution spectrometer manual. After preheating, a standard whiteboard is placed at the measurement position of the spectral sensor. The standard whiteboard has known and stable spectral reflection characteristics and can be used as a calibration reference. Then, the calibration program is started, and the high-resolution spectrometer collects the reflected spectral signal of the standard whiteboard. The spectral acquisition module automatically records the measured values at each wavelength at this time. According to the pre-set calibration algorithm, the spectral data of the collected standard whiteboard is compared with the theoretical spectral data of the standard whiteboard, and the deviation value of the high-resolution spectrometer is calculated. Finally, the deviation value is applied to the subsequent acquisition process of the actual soil sample spectral data to correct the measured values in real time to compensate for the influence of factors such as light source intensity fluctuations, aging of optical components, and environmental interference on the measurement results.

[0035] Further, the spectral preprocessing module improves the clarity of the spectral signal by removing background noise, including the following specific contents:

[0036] The spectral preprocessing module removes the background noise caused by the improvement of spectral resolution by establishing a spectral preprocessing model. The formula of the spectral preprocessing model is:

[0037] ,

[0038] In the formula: is the wavelength, is the spectral data measured at wavelength after preprocessing, is the spectral data measured at wavelength , including the target signal and background noise, is the estimated background noise signal at wavelength , is the base of the natural logarithm, is a parameter for adjusting the noise suppression intensity, is the characteristic wavelength intersecting with the noise background.

[0039] Further, the spectral resampling module reduces the redundant information in the spectral data and optimizes the data structure, including the following specific contents:

[0040] The spectral resampling module determines the required target wavelength range and sampling interval based on the collected spectral data. The spectral resampling module establishes a spectral resampling model to process the original spectral data and generate new spectral data at specified wavelengths, making the resampled spectral data smoother and reducing redundant information. The formula for the spectral resampling model is:

[0041] ,

[0042] In the formula: is the wavelength, is the spectral data after resampling at wavelength , is the total number of original spectral data points, is the th wavelength of the spectral data point, is the th wavelength of the spectral data point, is the measured value of the original spectral signal at wavelength ; is the coefficient controlling the spectral smoothing degree. 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 the bands with obvious spectral characteristics, the spectral resampling module increases the sampling points, while in the regions with weaker characteristics, the spectral resampling module reduces the sampling density, thereby reducing the storage requirements and subsequent computational burden while maintaining the data quality. After resampling, the spectral resampling module also performs standardization processing on the data, unifying the spectral data collected under different sources and conditions to a common scale, eliminating systematic errors caused by instrument differences or changes in operating conditions, and enhancing the comparability between data.

[0043] Furthermore, the data processing module combines the results of spectral preprocessing and spectral resampling, and improves the accuracy of soil organic carbon concentration detection by establishing a detection model, including the following specific contents:

[0044] 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 for the detection model is:

[0045] ,

[0046] 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, is the The wavelength of each spectral data point, is the measurement value after spectral preprocessing and spectral resampling at the wavelength where, satisfies , where is the spectral data measured at the wavelength after preprocessing, is the spectral data resampled at the wavelength , is the estimated value of the background signal at the wavelength , is the weight of each wavelength point, is a small positive value to avoid a zero denominator; the detection model quantifies the difference between the processed spectral signal and the background signal, reflects the concentration of soil organic carbon, and optimizes the sensitivity of the detection model by adjusting the weight of each wavelength, enabling it to maintain high detection efficiency under different soil conditions.

[0047] In this embodiment, the spectral acquisition module is connected to the soil detection module, the spectral preprocessing module is connected to the spectral acquisition module, the spectral resampling module is connected to the spectral preprocessing module, and the data processing module is respectively connected to the soil detection module and the spectral resampling module.

[0048] The present invention consists of 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 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, humidity, and pH value, the soil drill adjusts the sampling depth and frequency in real time to ensure the sample quality. The spectral acquisition module uses visible-near-infrared spectroscopy technology combined with high-precision equipment and a calibration mechanism to achieve efficient and accurate acquisition of spectral data. The spectral preprocessing module removes background noise through a model. The spectral resampling module uses a model to reduce redundant information, optimize the data structure, and perform standardization processing. The data processing module establishes a detection model, combines the results of spectral preprocessing and resampling, reduces interference, ensures accuracy, detects the concentration of soil organic carbon, and optimizes the sensitivity by adjusting the weight, enabling the system to maintain high detection efficiency under different soil conditions, solving the problems of high cost, time-consuming and laborious of traditional soil organic carbon detection methods and the problems of background noise and redundant information in hyperspectral technology.

[0049] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the said claims.

[0050] Finally: The above are only the preferred embodiments of the present invention and are not used to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention shall be included within 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; 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; 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

Images