A rapid detection method and device for soil organic matter

Abstract

The present invention relates to a rapid detection method and device for soil organic matter based on near-infrared spectroscopy technology. Soil spectrum information at different humidity levels is collected by a self-made near-infrared spectroscopy detection device. The spectrum contains information about soil organic matter. The soil moisture content of 12% is used as the reference humidity. The spectrum is corrected by a polynomial smoothing algorithm and a humidity self-correction algorithm to eliminate the influence of different soil particle sizes and different moisture contents. Then, the physical and chemical true value of organic matter in the soil is determined by an acidic potassium dichromate volumetric method, and a one-to-one correspondence between the corrected spectral information and the organic matter content is established. The characteristic wavelength is then selected by combining a competitive adaptive reweighting algorithm to establish a soil organic matter detection model, and the model is used to realize the detection of organic matter in similar unknown soil samples. The sample of the present invention does not require complex pre-treatment, and has the advantages of fast detection speed, simple and convenient operation, detection stability, and high accuracy.

Description

Technical Field

[0001] The present application relates to the field of rapid detection of soil physical and chemical component content, and specifically to a method for rapid detection of soil organic matter based on near-infrared spectroscopy and a portable detection device. Background Art

[0002] Soil is the basis for maintaining the growth of farmland plants. Soil is formed by the weathering of rocks. Its main component is minerals. It is a mixture of minerals and organic matter produced by the weathering of rocks and biological activities. It exists in solid, gaseous and liquid states. Among them, soil organic matter (SOM) is an important component of soil. SOM is a carbon-containing organic compound that exists in various forms in the soil. It is the most active part of the soil and an important indicator for measuring the fertility of the soil. SOM in the soil is the main source of plant nutrients. It plays a very important role in promoting plant growth and development, improving soil structure and improving the ability of soil to retain water and fertilizer. It is not difficult to see that soil with high SOM content has a higher fertility level. It can not only provide richer nutrition for crop growth, but also has strong soil water and fertilizer retention capacity, which can reduce nutrient loss, save fertilizer use and improve fertilizer utilization. Except for the black soil area in Northeast China, the SOM content in various parts of my country is generally low. By understanding soil fertility information and adjusting crop input according to the soil properties of crop growth, the same or higher income can be achieved with the least or most economical input, which can improve the environment, efficiently utilize various agricultural resources, and achieve economic and environmental benefits. Therefore, the mastery of soil organic matter content information is one of the important contents of implementing precision agriculture. For the determination of organic matter content, chemical determination methods such as potassium dichromate external heating method are generally used in the laboratory. The chemical determination method has the advantage of high measurement accuracy. However, this method has high requirements for operators, takes a long time to measure, and consumes a large amount of reagents during the measurement process. There are many disadvantages for its promotion and use in agricultural production. At present, there are many shortcomings in the commonly used soil organic matter detection technology. In order to improve the development level of precision agriculture and agricultural informatization in my country, a fast and accurate soil organic matter content detection method is urgently needed.

[0003] Near infrared spectroscopy is a modern analytical detection technology that is fast, non-destructive, and green. It has been partially applied in the field of rapid detection of food quality and food safety, and some detection devices have been developed accordingly. However, there are two main problems with current food detection devices: 1. The device is large, expensive, and cannot be carried around, so its application range is limited and it is mainly used in large food companies; 2. All soil samples need to be air-dried, which is time-consuming and labor-intensive. There is no device that can quickly detect in-situ soil. Therefore, it is of great practical significance to invent a highly integrated, low-cost, and portable food rapid detection device and method. Summary of the invention

[0004] In order to address the deficiencies in the prior art, the present application proposes a method and device system for rapid detection of soil organic matter based on near-infrared; the detection method is fast, reliable, highly reproducible, and low-cost; the detection system is integrated, miniaturized, and convenient, making it more suitable for rapid on-site detection, and can realize rapid detection of organic matter content in the soil, and is suitable for technical fields such as soil quality evaluation and environmental monitoring.

[0005] The technical solution adopted by the present invention is as follows

[0006] The present invention collects spectral information of soil through a near-infrared spectral acquisition device. The spectrum contains a large amount of information about soil organic matter and moisture. Then, the spectral information dimension reduction and interference information reduction method are adopted. The physicochemical values ​​obtained by combining chemometrics with physicochemical experiments are used to establish a detection model, and the optimal band and the optimal model are screened out to invert and detect the organic matter content information of the soil.

[0007] For the method and device of the present invention, the specific technical scheme adopted is as follows: a rapid detection method of soil organic matter with humidity self-correction, the steps are as follows:

[0008] Step 1, preparation of soil samples: first collect n tillage layers, soil samples at different locations with a depth of 0 to 20 cm in the tillage layer, each 200 g ± 20 g, pass through a 1 mm pore sieve, use the quartering method to retain 50 g, dry, and make n soil samples, where n is a positive integer;

[0009] Step 2: Acquisition of the physical and chemical true value of soil organic matter, using the acid potassium dichromate volumetric method to detect the content of organic matter in the soil, and obtaining the organic matter content in the soil determined by physical and chemical analysis, in units of g / kg;

[0010] Step 3, collecting the spectrum of soil organic matter: adding water to the soil dried in step 1 to prepare soil samples with moisture contents of 0%, 3%, 6%, 9%, 12%, 15%, 18%, and 21%, and using a portable near-infrared detection device to collect the spectrum information of soil samples with different moisture contents;

[0011] Step 4, soil spectrum processing and characteristic wavelength extraction: spectral processing is performed on the soil spectrum information with a moisture content of 12% collected in step 3; the measured value of organic matter detected by the acid potassium dichromate volumetric method in step 2 is used as the physical and chemical value, and a one-to-one correspondence between the processed spectral information and the physical and chemical value is established, that is, the corresponding physical and chemical value can be known through the spectral information; then different chemometric methods are used to screen characteristic variables, reduce data dimensions, and improve model accuracy;

[0012] Step 5, establishment and evaluation of the detection model: using the partial least squares regression algorithm to establish the soil organic matter detection model for the characteristic variables selected in step 4;

[0013] Step six, rapid detection of soil organic matter: data communication is carried out with the smart mobile terminal via Bluetooth to achieve real-time transmission of near-infrared spectral data of soil samples; by measuring the spectral information of similar unknown quality soil samples, after the data processing described in step four, the data is substituted into the soil organic matter detection model established in step five to achieve the detection of organic matter content in the soil sample to be tested.

[0014] Furthermore, in step 1, n≥100, the moisture content in the soil ranges from 0% to 20%, and the drying refers to drying the soil sample in a forced air drying oven at 108 degrees Celsius for 10 hours.

[0015] Furthermore, in step three, the collection of the soil organic matter spectrum is specifically as follows: using a light source as a signal source, a sample carrier (14) containing soil with a 1 mm hole sieve is placed in a groove of the head (2) of a portable detection housing, and when the light source is transmitted to the detection window (1) through a focusing lens, the soil in the quartz cuvette is irradiated, and the diffusely reflected light is transmitted through the lens to the near-infrared spectrometer, and the generated spectral data is collected by a micro-control circuit (10), and the spectral data is transmitted to an intelligent terminal (18) through a Bluetooth module to obtain diffuse reflection near-infrared spectral data of the soil.

[0016] Further, in step 4, the spectral processing method is a moisture correction algorithm and a polynomial smoothing algorithm;

[0017] The moisture correction algorithm comprises the following steps:

[0018] S1. Collect near infrared spectra of samples at different humidity levels;

[0019] S2. The collected spectral matrix X = XP + XQ + O, where: X is the original soil spectral matrix, P is the projection matrix of the useful subspace, which mainly comes from the information of organic matter, Q is the projection matrix of the interference subspace, which mainly comes from the interference of moisture, and O is the residual matrix, which comes from the interference of the instrument and the external environment;

[0020] S3. Calculate the difference spectrum matrix D: di = xi-xj, xi is the average spectrum of samples collected at a certain humidity level, where i = 1, 2...p, p represents p humidity levels, xj is the average spectrum of samples collected at the reference humidity, where j is one of 1, 2...p, and the difference spectrum matrix D is composed of di;

[0021] S4. Calculate the covariance matrix of D and perform singular value decomposition, i.e. SVD(D T D)=[USVT ], U is a left singular matrix, S is a diagonal matrix, the elements on the diagonal matrix are singular values ​​arranged from large to small, and V is a right singular matrix;

[0022] S5. Take the first c columns of V and get a subset V of the V matrix C ; where c becomes the number of factors;

[0023] S6. Calculate the interference subspace projection matrix Q: Q = V C V C T ;

[0024] S7. Calculate the useful subspace projection matrix P: P = IQ, I is the identity matrix, and p is also called the moisture correction matrix;

[0025] S8. Perform moisture correction on all spectral data to obtain spectral data after moisture self-correction, X * =XP.

[0026] The polynomial smoothing algorithm (SG) performs polynomial least squares fitting on a spectral curve by setting different polynomial orders and smoothing window widths in spectral signal processing. Its essence is a weighted average method that emphasizes the central role of the center point. The calculation formula of the SG algorithm is as follows:

[0027]

[0028] Where Xi,SG is the spectral reflectance or absorptivity after SG smoothing at wavelength i, C j is the smoothing coefficient, which can be obtained by polynomial fitting, m is the number of smoothing windows on one side of the wavelength, and N is the normalized exponent.

[0029] Furthermore, the data processing method and the soil organic matter detection model are both embedded in the detection software of the smart mobile terminal.

[0030] A rapid detection system for soil organic matter content of the present invention comprises a housing body (8), a head (2), a detection window (1), a head square lens (4), a sample carrier (14), a light source module (3), a near-infrared spectrometer (5), a micro control circuit (10), a power management circuit (7), a rechargeable battery (12), a switch (11), a base (13), an OLED status indicator screen (17), and an intelligent terminal (18);

[0031] The light emitted by the light source module (3) passes through the square lens (4) on the head of the detection window (1) and is focused together and projected onto the sample to be tested. The light reflected by the sample passes through the square lens (4) on the head and is received by the near-infrared spectrometer (5). The light received by the near-infrared spectrometer (5) is subjected to signal extraction and amplification by a single-point detector and a DMD chip inside the near-infrared spectrometer (5). After analog-to-digital conversion, the light is wirelessly transmitted to the intelligent terminal (18) through a micro control circuit (10); the near-infrared spectrometer (5), the power management circuit (7), the rechargeable battery (12), and the switch (11) are respectively connected to the micro control circuit (10); the OLED status indicator screen (17) is embedded in the shell body (8); the head (2) and the base (13) are connected to the shell body (8) to form a protective shell.

[0032] Furthermore, the light source module (3) is a full-band halogen light source with an operating power of 1.4 W, which is placed at an angle of 100° in the same direction and fixed on the near-infrared spectrometer (5). A square lens is provided at the front end of the light source and is placed on the detection window (1). The lens focuses the light 1 mm in front of the lens. The power management circuit (7) is connected via a flat cable, and the light source is focused on the outer wall of the sample carrier through the lens.

[0033] Furthermore, the micro control circuit (10) is integrated with an STM32 chip, a TM4C1297 chip and a Bluetooth module to realize the control of the light source module (3) and the processing and transmission of spectral data, as well as the operation of a 38*12mm OLED status indicator screen (17) for displaying the working status of the detection device.

[0034] Furthermore, the light source module (3) and the power management circuit (7) are connected to the micro control circuit (10) via a flat cable, and the near-infrared spectrometer (5) is connected to the micro control circuit (10) via a USB data cable; the outer shell of the system is a Φ110x180 cylinder to ensure the portability of the device; the outer shell of the system is divided into a head (2), a shell body (8) and a base (13), which are fixed using a Φ2*5mm screw hole (19); the head (2) has a Φ70*20mm groove for placing a sample carrier, and a 10*10mm square window is provided in the center of the groove so that light can irradiate the sample carrier; the sample carrier (14) is a Φ60*30mm quartz cuvette.

[0035] Beneficial effects of the present invention:

[0036] First, for the rapid detection of organic matter content in soil: the present invention aims at the fact that the spectral information of water will interfere with the spectral information of organic matter. The water self-correction algorithm is used to eliminate the interference of water on the spectral information. The established model for detecting organic matter content in soil has higher robustness.

[0037] Secondly, the present invention integrates spectral interference correction, data preprocessing, variable screening algorithm and detection model into the rapid detection system, which simplifies the steps from spectral data collection to export and processing, so that the detection results of soil organic matter can be quickly obtained within 8 seconds.

[0038] Third, compared with the existing conventional detection technology, the national standard detection method for soil organic matter - acid dichromic acid volumetric method, has a detection time of about 1 hour, while the detection time of the present invention is only a few seconds to produce results, the detection speed is fast, the detection accuracy can reach 90%, and the detection accuracy is high. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 The diagram is a schematic diagram of the internal structure of a near-infrared spectroscopy data acquisition device, including (1) a detection window, (2) a head, (3) a light source module, (4) a square lens on the head, (5) a near-infrared spectrometer, (6) a USB interface, (7) a power management circuit, (8) a housing, (9) a spectrometer card slot, (10) a micro control circuit, (11) a switch, (12) a rechargeable battery, and (13) a base.

[0040] Figure 2 This is a schematic diagram of the near infrared spectroscopy data acquisition system. It includes (14) sample carrier, (15) charging interface; (16) screw hole; (17) OLED display; (18) smart terminal;

[0041] Figure 3 Spectral information of soil organic matter collected in the example

[0042] Figure 4 Spectral information of soil organic matter after moisture correction

[0043] Figure 5 Modeling evaluation of 120 samples after CARS screening wavelength DETAILED DESCRIPTION

[0044] The technical solution of the present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0045] The present invention is universal for the rapid detection of the content of internal components of granular powder materials. The present invention selects the detection of organic matter in soil as an implementation example, and the detection of internal components of other granular powders can refer to the method of this implementation example.

[0046] Embodiment 1:

[0047] The following describes the implementation steps of this example in detail with reference to the accompanying drawings:

[0048] (1) Preparation of soil samples: Soil samples of 120 tillage layers (0-20 cm depth) and different sites were collected, each weighing 200 ± 20 g, passed through a 1 mm sieve, and 50 g was retained using the quartering method. The soil samples were then dried to prepare 120 soil samples.

[0049] (2) By adding water to the soil samples, soil samples with moisture contents of 0%, 3%, 6%, 9%, 12%, 15%, 18%, and 21% were prepared, and a portable near-infrared detection device was used to collect spectral information of soil samples with different moisture contents.

[0050] (3) To obtain the true physical and chemical value of soil organic matter, the content of organic matter in the soil was detected by the acid potassium dichromate volumetric method, and the organic matter content in the soil determined by physical and chemical analysis was obtained in g / kg.

[0051] (4) Collection of near-infrared spectra of soil samples: A soil organic matter detection device structure is as follows Figure 2 As shown, it includes a housing, a detection window, a focusing lens, a sample carrier, a light source module, a near-infrared spectrometer, a control circuit, a power management circuit, a rechargeable battery, a switch, an OLED status indicator screen, and an Android phone. During collection, a cuvette containing a soil sample is placed in front of the detection window at the front end of the housing. When the light source reaches the surface of the cuvette through the lens, the diffuse reflected light generated is received by the near-infrared spectrometer, and the near-infrared spectrometer transmits the received spectral information to the micro-control circuit through a data line. The micro-control circuit transmits the received spectral data to the smart mobile terminal through Bluetooth and displays it on the human-computer interaction interface. The cuvette is a 30*Φ60mm quartz cuvette; the light source is a full-band halogen light source, and the wavelength detected by the near-infrared spectrometer 2 is 900-1700nm, the resolution is 10.53, the number of scanning points is 228, the smoothness is 5 points, and the average number of scanning times is 5 times, and the near-infrared spectral data of soil organic matter is collected.

[0052] (5) Spectral preprocessing and characteristic wavelength extraction: The spectral information of the 120 soil samples collected was preprocessed by using the moisture elimination algorithm and the polynomial smoothing algorithm to process the diffuse reflectance spectrum data. CARS was used to screen variables, and 22 variables were screened. The characteristic wavelengths corresponding to the 22 variables are as follows: 905.53nm, 965.87nm, 982.37nm, 1069.68nm, 1073.36nm, 1077. 04nm1100.25nm,1123.27nm,1223.98nm,1228.63nm,1275.80nm,1300.75nm,1322.11nm,1360.95n m,1418.58nm,1422.87nm,1505.92nm,1531.54nm,1569.86nm,1575.84nm,1578.83nm,1625.95nm,

[0053] (6) Establishment and evaluation of detection model: CARS (competitive adaptive reweighting algorithm) was used to establish a soil organic matter detection model for the selected characteristic wavelengths. The correlation coefficient of the established model reached 0.9159, the RMSEC was 5.6357, and the model stability was good. The moisture elimination algorithm, polynomial smoothing algorithm and the established soil organic matter detection model were implanted into the detection software of the smart mobile terminal.

[0054] (7) Use the soil organic matter detection model established in (6) to detect soil samples of unknown quality: by measuring the spectral information of the unknown quality soil and then substituting it into the established soil organic matter inversion model, the organic matter in the soil can be detected. Table 1 shows the results of the inversion of soil organic matter content and physical and chemical analysis based on moisture self-correction near infrared spectroscopy technology

[0055] Table 1 Inversion of soil organic matter content and physical and chemical analysis results based on near-infrared spectroscopy technology with moisture self-correction

[0056]

[0057]

[0058] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "specific examples", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and purpose of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A rapid detection method for soil organic matter with self-correction of humidity, characterized in that: The steps are as follows: Step 1, preparation of soil samples: first collect n tillage layers, soil samples at different locations with a depth of 0 to 20 cm in the tillage layer, each 200 g ± 20 g, pass through a 1 mm pore sieve, use the quartering method to retain 50 g, dry, and make n soil samples, where n is a positive integer; Step 2: Acquisition of the physical and chemical true value of soil organic matter, using the acid potassium dichromate volumetric method to detect the content of organic matter in the soil, and obtaining the organic matter content in the soil determined by physical and chemical analysis, in units of g / kg; Step 3, collecting the spectrum of soil organic matter: adding water to the soil dried in step 1 to prepare soil samples with moisture contents of 0%, 3%, 6%, 9%, 12%, 15%, 18%, and 21%, and using a portable near-infrared detection device to collect the spectrum information of soil samples with different moisture contents; Step 4, soil spectrum processing and characteristic wavelength extraction: spectral processing is performed on the soil spectrum information with a moisture content of 12% collected in step 3; the measured value of organic matter detected by the acid potassium dichromate volumetric method in step 2 is used as the physical and chemical value, and a one-to-one correspondence between the processed spectral information and the physical and chemical value is established, that is, the corresponding physical and chemical value can be known through the spectral information; then different chemometric methods are used to screen characteristic variables, reduce data dimensions, and improve model accuracy; Step 5, establishment and evaluation of the detection model: using the partial least squares regression algorithm to establish the soil organic matter detection model for the characteristic variables selected in step 4; Step 6, rapid detection of soil organic matter: data communication is performed with a smart mobile terminal via Bluetooth to achieve real-time transmission of near-infrared spectral data of soil samples; spectral information of similar unknown quality soil samples is measured, and after the data processing described in step 4, the data is substituted into the soil organic matter detection model established in step 5 to achieve detection of the organic matter content of the soil sample to be tested; In step 4, the spectrum processing method is a moisture correction algorithm and a polynomial smoothing algorithm; The moisture correction algorithm comprises the following steps: S1. Collect near infrared spectra of samples at different humidity levels; S2. The collected spectral matrix X = XP + XQ + O, where: X is the original soil spectral matrix, P is the projection matrix of the useful subspace, which mainly comes from the information of organic matter, Q is the projection matrix of the interference subspace, which mainly comes from the interference of moisture, and O is the residual matrix, which comes from the interference of the instrument and the external environment; S3. Calculate the difference spectrum matrix D: di = xi-xj, xi is the average spectrum of samples collected at a certain humidity level, where i = 1, 2...p, p represents p humidity levels, xj is the average spectrum of samples collected at the reference humidity, where j is one of 1, 2...p, and the difference spectrum matrix D is composed of di; S4. Calculate the covariance matrix of D and perform singular value decomposition, i.e. SVD(D T D)=[USV T ], U is a left singular matrix, S is a diagonal matrix, the elements on the diagonal matrix are singular values ​​arranged from large to small, and V is a right singular matrix; S5. Take the first c columns of V and get a subset V of the V matrix C ; where c becomes the number of factors; S6. Calculate the interference subspace projection matrix Q: Q = V C V C T ; S7. Calculate the useful subspace projection matrix P: P = IQ, I is the identity matrix, and p is also called the moisture correction matrix; S8. Perform moisture correction on all spectral data to obtain spectral data after moisture self-correction, X * =XP; The polynomial smoothing algorithm (SG) performs polynomial least squares fitting on a spectral curve by setting different polynomial orders and smoothing window widths in spectral signal processing. Its essence is a weighted average method that emphasizes the central role of the center point. The calculation formula of the SG algorithm is as follows: Where Xi,SG is the spectral reflectance or absorptivity after SG smoothing at wavelength i, C j is the smoothing coefficient, which can be obtained by polynomial fitting, m is the number of smoothing windows on one side of the wavelength, and N is the normalized exponent.

2. A rapid detection method for soil organic matter with self-correction of humidity according to claim 1, characterized in that: In step 1, n≥100, the moisture content in the soil ranges from 0% to 20%, and the drying refers to drying the soil sample in a forced air drying oven at 108 degrees Celsius for 10 hours.

3. The method for rapid detection of soil organic matter with self-correction of humidity according to claim 1, characterized in that: In step three, the collection of the soil organic matter spectrum is specifically as follows: using a light source as a signal source, a sample carrier (14) containing soil with a 1 mm hole sieve is placed in a groove of the head (2) of a portable detection housing, and when the light source is transmitted to the detection window (1) through a focusing lens, the soil in the quartz cuvette is irradiated, and the diffusely reflected light is transmitted through the lens to the near-infrared spectrometer, and the generated spectral data is collected by a micro-control circuit (10), and the spectral data is transmitted to an intelligent terminal (18) through a Bluetooth module to obtain diffuse reflection near-infrared spectral data of the soil.

4. The method for rapid detection of soil organic matter with self-correction of humidity according to claim 1, characterized in that: The data processing method and the soil organic matter detection model are both embedded in the detection software of the smart mobile terminal.

5. A rapid detection system for soil organic matter content, characterized in that: It comprises a housing body (8), a head (2), a detection window (1), a square lens (4) on the head, a sample carrier (14), a light source module (3), a near-infrared spectrometer (5), a micro control circuit (10), a power management circuit (7), a rechargeable battery (12), a switch (11), a base (13), an OLED status indicator screen (17), and an intelligent terminal (18); The light emitted by the light source module (3) passes through the square lens (4) on the head of the detection window (1) and is focused together and projected onto the sample to be tested. The light reflected by the sample passes through the square lens (4) on the head and is received by the near-infrared spectrometer (5). The light received by the near-infrared spectrometer (5) is subjected to signal extraction and amplification by a single-point detector and a DMD chip inside the near-infrared spectrometer (5). After analog-to-digital conversion, the light is wirelessly transmitted to the intelligent terminal (18) through a micro control circuit (10); the near-infrared spectrometer (5), the power management circuit (7), the rechargeable battery (12), and the switch (11) are respectively connected to the micro control circuit (10); the OLED status indicator screen (17) is embedded in the shell body (8); the head (2) and the base (13) are connected to the shell body (8) to form a protective shell.

6. A rapid detection system for soil organic matter content according to claim 5, characterized in that: The light source module (3) is a full-band halogen light source with an operating power of 1.4 W. It is placed at an angle of 100° in the same direction and fixed on the near-infrared spectrometer (5). A square lens is provided at the front end of the light source and is placed on the detection window (1). The lens focuses the light 1 mm in front of the lens. The power management circuit (7) is connected via a flat cable, and the light source is focused on the outer wall of the sample carrier through the lens.

7. A rapid detection system for soil organic matter content according to claim 5, characterized in that: The micro control circuit (10) integrates an STM32 chip, a TM4C1297 chip and a Bluetooth module to realize the control of the light source module (3) and the processing and transmission of spectral data, as well as the operation of a 38*12mm OLED status indicator screen (17) for displaying the working status of the detection device.

8. A rapid detection system for soil organic matter content according to claim 5, characterized in that: The light source module (3) and the power management circuit (7) are connected to the micro control circuit (10) via a flat cable, and the near-infrared spectrometer (5) is connected to the micro control circuit (10) via a USB data cable; the outer shell of the system is a Φ110x180 cylinder to ensure the portability of the device; the outer shell of the system is divided into a head (2), a shell body (8) and a base (13), which are fixed using a Φ2*5mm screw hole (19); the head (2) has a Φ70*20mm groove for placing a sample carrier, and a 10*10mm square window is provided in the center of the groove so that light can irradiate the sample carrier; the sample carrier (14) is a Φ60*30mm quartz cuvette.

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