Soil organic matter content on-line monitoring device and method

Abstract

The invention provides a device and a method for monitoring the content of organic matters in soil on line, and aims to solve the problem that the content of the organic matters in the soil cannot be regularly and continuously monitored in the prior art. The device is provided with a soil spectrum information acquisition module, and operates in a dark environment, so that the interference caused by the change of an ambient light source is effectively avoided. Besides, in consideration of the requirement that the sensor works in soil for a long time, in order to prevent moisture accumulation from influencing the measurement precision and stability, a ficus microcarpa leaf contour-imitated ventilation structure is designed in the sensor, the long-term reliability and accuracy of the equipment are ensured, and the mounting angle of the ficus microcarpa contour-imitated structure is 20 degrees.

Description

Technical Field

[0001] The present invention relates to the field of soil analysis and monitoring, and in particular to an online monitoring device and method for soil organic matter content. Technical Background

[0002] Soil is the basis of agricultural production, and the content of its different components has different effects on crop growth. Soil organic matter is a key component for maintaining soil health and ecosystem functions. It not only improves soil fertility, but also enhances soil water retention and structural stability. Soil organic matter plays an important role in the carbon cycle. Its dynamic changes are related to the release and absorption of greenhouse gases, and have a profound impact on climate change mitigation. However, soil degradation is becoming increasingly serious worldwide, resulting in a significant reduction in soil organic matter, which in turn affects crop yields and environmental quality. Long-term monitoring of soil organic matter content is of great significance to agricultural and environmental management. It helps to assess soil health and provide a scientific basis for the formulation of land management and protection measures, thereby achieving sustainable agricultural and environmental protection goals. Continuous monitoring can timely understand the changing trends of soil organic matter, guide scientific fertilization, and improve farmland productivity.

[0003] Traditional methods for measuring soil organic matter content mainly rely on laboratory chemical analysis. Although this method can provide accurate measurement results, it has many disadvantages. First, this method requires the consumption of a large amount of chemical reagents, which is not only costly, but also produces harmful residues and causes potential pollution to the environment. Secondly, the laboratory chemical analysis process is complicated and time-consuming. It takes a long time from sample collection to obtaining results, and it is impossible to achieve real-time monitoring of soil organic matter content. In addition, since samples need to be sent to the laboratory for processing and analysis, this method is not suitable for measuring the organic matter content of large areas of soil and monitoring periodic changes, and it is difficult to meet the needs of modern agriculture for fast, convenient and low-cost soil monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide an online monitoring device for soil organic matter content, aiming to solve the problem in the prior art that soil organic matter content cannot be monitored regularly and continuously. The soil spectral information acquisition module of the device operates in a lightless environment, effectively avoiding the interference caused by changes in ambient light sources. The sensor works in the soil for a long time. In order to prevent moisture accumulation from affecting its accuracy and stability, a ventilation structure is designed inside the sensor.

[0005] The purpose of the present invention is achieved through the following technical solutions:

[0006] A soil organic matter content online monitoring device comprises a bracket 10 and a plurality of soil spectrum acquisition modules. A base 17 is installed at the bottom of the bracket 10. The measuring device and the base 10 are connected via a data transmission line 15. The soil spectrum acquisition module comprises a shell 16 in the shape of a quadrangular pyramid with two air holes 5, an optical fiber 9 located at each side edge inside the shell, a light source 8 located at the four corners of the bottom surface inside the shell, a photoelectric sensor 6 located at the center of the bottom surface of the shell, and an airflow guiding structure 7 located inside the shell. The shell 16 has no top surface. The light from the light source 8 is transmitted to the soil to be detected via the optical fiber 9, and then transmitted to the photoelectric sensor via the soil to be detected. The airflow guiding structure imitates the contour surface of a banyan leaf. The two air holes 5 are located on two opposite bottom edges of the shell 16. The airflow guiding structure 7 is located below the air holes 5. After the airflow is blown into from one air hole 5, it is guided and dispersed to the other air hole 5 via the airflow guiding structure 7.

[0007] As a more optimal technical solution of the present invention, a solar panel 14 is installed on the top of the bracket 10 .

[0008] As a more optimal technical solution of the present invention, it also includes a data processing module, which includes a battery 1 for powering it, an STM32 single-chip microcomputer development board 2 for analyzing spectral data, a 4G module 3 for uploading data in real time, and a signal amplification module 4.

[0009] As a more optimal technical solution of the present invention, the light source 8 has four different wavelength bands: 620-750nm, 850nm, 940nm and 950nm.

[0010] As a more optimal technical solution of the present invention, the outline of the banyan leaf is a curved ellipse, the major axis a of the ellipse is 160 mm, the minor axis b is 100 mm; the outline curvature k is 0.0125 mm -1 , the corresponding curvature radius R is 80mm.

[0011] As a more optimal technical solution of the present invention, the surface roughness R a 0.1μm.

[0012] As a more optimal technical solution of the present invention, the banyan leaf installation angle θ is 20°, and the installation angle is the angle between the line between the highest points A and B of the leaf lying flat and the straight line where the two vents are located.

[0013] Another object of the present invention is to provide a method for online monitoring of soil organic matter content, comprising the following steps:

[0014] Placing a plurality of soil spectrum acquisition modules at a plurality of locations of the soil to be measured;

[0015] The light source is irradiated through one end of the V-shaped optical fiber, and the light is transmitted to the soil and transmitted to the photoelectric sensor at the other end of the V-shaped optical fiber after diffuse reflection from the soil. The optical signal is converted into an electrical signal and amplified before being transmitted to the STM32 microcontroller development board. The organic matter content of the soil is calculated through the built-in trained machine learning model, and the data is uploaded to the cloud platform through the 4G module.

[0016] Compared with the traditional soil organic matter content measurement method, the present invention has the following beneficial effects:

[0017] The real-time data upload is realized through the 4G module, and users can view it in real time on their mobile phones or computers, which greatly improves the timeliness and efficiency of monitoring. The use of spectral analysis technology eliminates the need for chemical reagents and avoids the generation of chemical residues. Its ventilation and air exchange structure designed with the contour of banyan leaves effectively prevents moisture accumulation and ensures the long-term stability of the sensor. Multiple soil organic matter sensitive band light sources are used to construct a machine learning model to improve the accuracy and reliability of the prediction. In addition, the device is equipped with four soil spectrum acquisition modules, which collect data from multiple points at the same time and calculate the average value, making the data more representative. Finally, the use of solar panels for power supply enables the device to work continuously in the field for a long time, which has the advantages of sustainability and environmental protection and reduces maintenance costs. The present invention can monitor the content of soil organic matter efficiently, accurately and environmentally friendly, and is suitable for the analysis of periodic changes in large areas of soil. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of the soil organic matter content measuring device of the present invention.

[0019] Figure 2 It is a structural schematic diagram of the soil spectrum acquisition module of the present invention;

[0020] Figure 3 is a cross-sectional view of the soil spectrum acquisition module of the present invention;

[0021] Figure 4 is a structural schematic diagram of the airflow guiding structure of the present invention;

[0022] Figure 5 is a schematic diagram of the airflow direction of the present invention;

[0023] Figure 6 is a schematic diagram of the optical transmission path of the present invention;

[0024] Figure 7 This is the support vector machine model training result diagram;

[0025] Figure 8 is a comparison chart of the actual value and the model predicted value of Example 1 of the present invention;

[0026] Fig. 9is a comparison chart of actual values ​​and model predicted values ​​of Example 2 (without airflow guiding structure) of the present invention;

[0027] Fig.10 is a comparison chart of actual values ​​and model predicted values ​​of Example 3 of the present invention (the airflow guiding structure is in other forms);

[0028] Fig.11 This is a comparison chart of the actual value and the model prediction value of Example 4 of the present invention (the installation angle is other angle 8 degrees);

[0029] Fig.12 is a structural schematic diagram of the airflow guiding structure of the present invention;

[0030] Fig.13 yes Fig.12 AA section view. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below with reference to the accompanying drawings.

[0032] Example 1

[0033] like Figures 1 to 6 As shown, the present invention provides an online real-time monitoring device for soil organic matter content imitating the outline of a banyan leaf, comprising a bracket 10 and a plurality of soil spectrum acquisition modules, a base 17 is installed at the bottom of the bracket 10, the measuring device and the base 10 are connected through a data transmission line 15, the soil spectrum acquisition module comprises a shell 16 in the shape of a quadrangular pyramid with two air holes 5, an optical fiber 9 located at each side edge inside the shell, a light source 8 located at the four corners of the bottom surface inside the shell, a photoelectric sensor 6 located at the center of the bottom surface of the shell and an airflow guiding structure 7 located inside the shell, the shell 16 has no top surface, the light source 8 is transmitted to the soil to be detected through the optical fiber 9, and then transmitted to the photoelectric sensor through the soil to be detected; the airflow guiding structure imitates the curved surface of the banyan leaf outline, the two air holes 5 are located on the two opposite bottom edges of the shell 16, the airflow guiding structure 7 is located below the air holes 5, after the airflow is blown in from one air hole 5, it is guided and dispersed to the other air hole 5 through the airflow guiding structure 7; the top of the bracket 10 is installed with a solar panel 14. It also includes a data processing module, which includes a battery 1 for powering it, an STM32 microcontroller development board 2 for analyzing spectral data, a 4G module 3 for real-time data upload, and a signal amplification module 4. The light source 8 has four different wavelengths of 620-750nm, 850nm, 940nm and 950nm.

[0034] The present invention solves the problem that the soil organic matter content cannot be monitored regularly and continuously in the prior art. The soil spectrum information acquisition module of the device operates in a lightless environment, effectively avoiding the interference caused by changes in the ambient light source. The sensor works for a long time in the soil. In order to prevent moisture accumulation from affecting its accuracy and stability, a ventilation structure imitating the outline of banyan leaves is designed inside the sensor. The working principle of the device is to irradiate the light source through one end of multiple V-shaped optical fibers, and the light is transmitted to the soil and transmitted to the photoelectric sensor at the other end of the V-shaped optical fiber after diffuse reflection from the soil. The optical signal is converted into an electrical signal and transmitted to the STM32 single-chip microcomputer development board after signal amplification. The STM32 single-chip microcomputer development board has a built-in trained machine learning model. In order to improve the prediction accuracy, four different bands of light sources are used, namely 620-750nm, 850nm, 940nm and 950nm. After receiving the signal, the development board calculates the organic matter content of the soil and uploads the data to the cloud platform through the 4G module. Users can view the data in real time through mobile phones or computers. The device is powered by solar panels and can be placed in the field for a long time. The instrument is equipped with four soil spectrum acquisition modules, which are connected to the rack through wires. The points of the four soil spectrum information acquisition modules can move freely, and can collect data from four points at the same time and calculate the average value, so as to realize dynamic online real-time monitoring of soil organic matter content. Because the data of a single measuring point may have accidental errors or local deviations, multi-point acquisition can more comprehensively reflect the overall situation of the soil, so it can improve the representativeness and accuracy of the data. The multi-point data acquisition function makes the monitoring results more valuable for reference, which helps users to make scientific soil management and decision-making. The soil spectrum information acquisition module can collect spectral information of 10-40cm soil depth. The spectrum information acquisition module operating in a lightless environment has designed a soil spectrum information acquisition module. The light sources of four different bands in the module are separated. The light source of each band is transmitted to the soil through one end of the V-shaped optical fiber, and then transmitted to the photoelectric sensor through soil diffuse reflection. The soil spectrum information acquisition module obtains the spectral information of the soil in a lightless environment, effectively avoiding the interference caused by changes in the ambient light source. Usually, changes in external light sources will affect the spectral measurement of the sensor, resulting in inaccurate data. By working in a light-free environment, the spectral information acquisition module can maintain consistent measurement conditions, ensuring that the acquired spectral data is pure and stable, thereby improving the accuracy and credibility of the monitoring data. Figure 8 shown.

[0035] The spectrum information acquisition module of the present invention is the core component of the online real-time monitoring device for soil organic matter content, which is responsible for obtaining the spectral reflectance information of the soil and converting it into an electrical signal. The module is composed of a light source, an optical fiber transmission system, a photoelectric sensor and a ventilation structure imitating the outline of a banyan leaf. The light source adopts four bands of 620-750nm, 850nm, 940nm and 950nm, covering the characteristic absorption peaks of soil organic matter. Multiple V-shaped optical fibers transmit light, and the light is transmitted to the photoelectric sensor after diffuse reflection from the soil. The sensor converts the light signal into an electrical signal, and the signal processing unit performs amplification, filtering and A / D conversion to ensure high data quality and low noise. The module operates in a lightless environment to avoid interference from ambient light sources, and adopts dustproof, waterproof and impact-resistant design to adapt to complex outdoor environments. A ventilation and ventilation system imitating the outline of a banyan leaf is designed inside the module. When the airflow is blown in from the ventilation duct, it is guided and dispersed to the airflow outlet through the curved surface of the imitation banyan leaf outline, promoting the gas flow in the spectrum acquisition module, thereby effectively preventing moisture accumulation and ensuring that the sensor maintains high accuracy and stability during long-term operation. The high performance and innovative design of this module provide a reliable data basis for soil organic matter content analysis and are the key to achieving online real-time monitoring.

[0036] In the process of establishing the machine learning model, various machine learning models such as partial least squares, support vector machine, and random forest were compared, and support vector machine was finally selected as the modeling method. 200 soil samples were collected and their organic matter content was determined by laboratory chemical methods. At the same time, the spectral reflectance information of the samples was obtained using light sources of four different bands: 620-750nm, 850nm, 940nm, and 950nm. With soil spectral reflectance as input and soil organic matter content as output, the training set and test set of the model were divided according to a ratio of 3:1 to establish an inversion model for soil organic matter content. After the model is established, the soil spectral reflectance information of the above four bands is input to predict the organic matter content of the soil. Then, the trained model is burned into the STM32 development board, and the spectral information obtained by the photoelectric sensor is processed by signal amplification, denoising, A / D conversion, etc. On the STM32 development board, the soil organic matter content data can be obtained according to the established machine learning model. Finally, these data are uploaded to the cloud platform through the 4G module. The multi-band light source establishes a machine learning model to improve prediction accuracy, and combines the trained machine learning model to improve the accuracy of soil organic matter content prediction. The multi-band light source can fully cover the different characteristic information of the soil spectrum, and the machine learning model can more accurately analyze these spectral data through learning and training a large amount of data, thereby improving the prediction accuracy. This greatly enhances the analytical ability of the device, making the monitoring results more scientific and reliable.

[0037] The present invention uses solar panels for power supply to ensure long-term stable operation in the wild, so that it can be placed in the field for a long time for continuous monitoring. The power supply system consists of solar panels, charging controllers, batteries and power management modules. High-efficiency solar panels convert light energy into electrical energy, and adjust the current and voltage through the charging controller to prevent overcharging and over-discharging, and adapt to different temperature environments. Large-capacity batteries store electrical energy to ensure that they can still work normally at night or in continuous rainy weather. The power management module intelligently manages voltage regulation, power distribution and status monitoring, gives priority to power supply to core sensors and data transmission modules, and automatically reduces power consumption when the sensor is on standby, further extending the battery life. This design ensures the continuity and real-time nature of data collection when the device is without power supply. The device can continue to operate unattended, ensuring the continuity and stability of soil organic matter content monitoring, and providing reliable data support for agricultural production.

[0038] The cloud platform has a large-capacity, high-reliability storage system, uses distributed database technology for data storage, and has backup and recovery functions. The uploaded data is processed and analyzed in real time on the cloud platform, providing and generating visual charts and reports. Users access the cloud platform through mobile phones or computers to view data and historical trends in real time, and can customize queries and export data. The cloud platform adopts multi-level security measures to ensure the security of data transmission and storage, and protects data privacy through permission management. In addition, the cloud platform has good scalability and compatibility, can add more sensor nodes and data types, and seamlessly integrate with other agricultural information systems and IoT devices. The introduction of the real-time online monitoring system solves the problem of untimely data acquisition in traditional soil monitoring. Users can obtain the latest soil organic matter content information without going to the site in person, which greatly improves the convenience and efficiency of monitoring, and also helps to take corresponding agricultural measures in a timely manner.

[0039] A ventilation and ventilation structure imitating the outline of a banyan leaf is designed inside the soil spectrum module to solve the problem of moisture accumulation when the sensor is working in the soil for a long time. The natural form of the banyan leaf has excellent ventilation effect, and the bionic design forms an effective air flow inside the sensor, which is conducive to the discharge of moisture in the sensor and keeps the inside dry. Not only does it improve the accuracy and stability of the sensor, but it also extends its service life, providing a reliable guarantee for long-term monitoring. The leaf outline can guide and disperse the airflow when the wind blows through the leaves, and this effect can help the leaves improve the efficiency of airflow. The banyan leaf has a large leaf area and can provide a wider range of airflow guidance. Its shape and outline naturally adapt to the airflow flow, which is conducive to guiding and dispersing the airflow when the wind blows, improving the air flow efficiency, and thus achieving a better drying effect. Soil moisture and tiny particles may affect the performance and life of the sensor. Maintaining proper ventilation can prevent moisture accumulation and ensure that the sensor is dry inside, thereby maintaining its high accuracy and stability. The streamlined structure of the banyan leaf enhances the airflow guidance effect of the present invention on its surface. In aerodynamics, it exhibits a higher lift coefficient and a lower drag coefficient, which facilitates the passage of airflow.

[0040] The overall outline of the banyan leaf is an ellipse, defined by the ellipse: 2 / a 2 +y 2 / b 2 =1, where: x and y are the coordinates on the blade surface; a and b are the major and minor axes of the ellipse, respectively. The airflow guidance function is achieved by imitating the outline of the banyan leaf, and the size is set to 160mm for the major axis a and 100mm for the minor axis b. This shape helps the air to transition smoothly when flowing on the surface and reduces airflow separation.

[0041] Banyan leaves have a unique curved shape that can effectively guide airflow. The contour curvature of the bionic design is defined as follows: k = 1 / R; where: k is the curvature, in mm -1 ; R is the radius of curvature, in mm. In order to simulate the natural contour curvature of banyan leaves, the curvature k in the design is 0.0125 mm -1 , the corresponding curvature radius R is 80mm. This curvature imitates the structure of banyan leaves, which can guide airflow, reduce turbulence and optimize air flow. Fig.13 shown.

[0042] Banyan leaf surface roughness: Where: Ra is the surface roughness, in μm; L is the measuring length, in mm; f(x) is the surface profile function, in μm; The surface roughness Ra is selected as 0.1 μm. A very smooth surface helps maintain the laminar state of the airflow.

[0043] The installation angle of the bionic banyan leaf structure in the present invention is crucial to the airflow guidance. The optimal installation angle can be calculated by the airflow deflection angle: Δθ = arctan (v y / v x ), where: Δθ is the airflow deflection angle, that is, the installation angle, in degrees, v y and v x are the velocity components of the airflow in the y and x directions, respectively, in m / s, and the installation angle θ is 20° to ensure that the airflow can be effectively guided and reduce airflow separation, thereby improving ventilation and air exchange efficiency. The installation angle is the angle between the line between the highest points A and B of the blade lying flat and the straight line where the two vents are located.

[0044] Example 2

[0045] The difference between this embodiment and embodiment 1 is that the airflow guide structure is not installed. Fig. 9 As shown in the figure, by comparing the actual value with the model prediction value, it can be seen that the model test result is inaccurate.

[0046] Example 3

[0047] The difference between this embodiment and embodiment 1 is that the airflow guiding structure is a bamboo leaf-like structure. Fig.10 As shown in the figure, by comparing the actual value with the model prediction value, it can be seen that the model test result is inaccurate.

[0048] Example 4

[0049] The difference between this embodiment and embodiment 1 is that the installation angle is 8°. Fig.11 As shown in the figure, by comparing the actual value with the model prediction value, it can be seen that the test result of the model is inaccurate.

[0050] The spectrum information acquisition module in the present invention is the core component of the online real-time monitoring device for soil organic matter content, which is responsible for obtaining the spectral reflectance information of the soil and converting it into an electrical signal. The module is composed of a light source, an optical fiber transmission system, a photoelectric sensor and a ventilation structure imitating the outline of a banyan leaf. The light source adopts four bands of 620-750nm, 850nm, 940nm and 950nm, covering the characteristic absorption peaks of soil organic matter. Multiple V-shaped optical fibers transmit light, and the light is transmitted to the photoelectric sensor after diffuse reflection from the soil. The sensor converts the light signal into an electrical signal, and the signal processing unit performs amplification, filtering and A / D conversion to ensure high data quality and low noise. The module operates in a lightless environment to avoid interference from ambient light sources, and adopts dustproof, waterproof and impact-resistant design to adapt to complex outdoor environments. A ventilation and ventilation system imitating the outline of a banyan leaf is designed inside the module. When the airflow is blown in from the ventilation duct, it is guided and dispersed to the airflow outlet through the curved surface of the imitation banyan leaf outline, promoting the gas flow in the spectrum acquisition module, thereby effectively preventing moisture accumulation and ensuring that the sensor maintains high accuracy and stability during long-term operation. The high performance and innovative design of this module provide a reliable data basis for soil organic matter content analysis and are the key to achieving online real-time monitoring.

[0051] In the process of establishing the machine learning model in the present invention, a variety of machine learning models such as partial least squares, support vector machine, random forest are compared, and support vector machine is finally selected as the modeling method. 200 soil samples are collected and their organic matter content is determined by laboratory chemical methods. At the same time, the spectral reflectance information of the sample is obtained using light sources of four different bands of 620-750nm, 850nm, 940nm and 950nm. With soil spectral reflectance as input and soil organic matter content as output, the training set and test set of the model are divided according to the ratio of 3:1 to establish an inversion model of soil organic matter content. After the model is established, the soil spectral reflectance information of the above four bands is input to predict the organic matter content of the soil. Then, the trained model is burned into the STM32 development board, and the spectral information obtained by the photoelectric sensor is processed by signal amplification, denoising, A / D conversion, etc. On the STM32 development board, according to the established machine learning model, the content data of soil organic matter can be obtained. Finally, these data are uploaded to the cloud platform through the 4G module. The multi-band light source establishes a machine learning model to improve prediction accuracy, and combines the trained machine learning model to improve the accuracy of soil organic matter content prediction. The multi-band light source can fully cover the different characteristic information of the soil spectrum, and the machine learning model can more accurately analyze these spectral data through learning and training a large amount of data, thereby improving the prediction accuracy. This greatly enhances the analytical ability of the device, making the monitoring results more scientific and reliable.

[0052] The present invention uses solar panels for power supply to ensure long-term stable operation in the wild, so that it can be placed in the field for a long time for continuous monitoring. The power supply system consists of solar panels, charging controllers, batteries and power management modules. High-efficiency solar panels convert light energy into electrical energy, and adjust the current and voltage through the charging controller to prevent overcharging and over-discharging, and adapt to different temperature environments. Large-capacity batteries store electrical energy to ensure that they can still work normally at night or in continuous rainy weather. The power management module intelligently manages voltage regulation, power distribution and status monitoring, gives priority to power supply to core sensors and data transmission modules, and automatically reduces power consumption when the sensor is on standby, further extending the battery life. This design ensures the continuity and real-time nature of data collection when the device is without power supply. The device can continue to operate unattended, ensuring the continuity and stability of soil organic matter content monitoring, and providing reliable data support for agricultural production.

[0053] The cloud platform in the present invention has a large-capacity, high-reliability storage system, adopts distributed database technology for data storage, and has backup and recovery functions. The uploaded data is processed and analyzed in real time on the cloud platform, and visual charts and reports are generated. Users access the cloud platform through mobile phones or computers, view data and historical trends in real time, and can customize queries and export data. The cloud platform adopts multi-level security measures to ensure the security of data transmission and storage, and protects data privacy through authority management. In addition, the cloud platform has good scalability and compatibility, can add more sensor nodes and data types, and seamlessly integrate with other agricultural information systems and Internet of Things devices. The scheduled upload of soil organic matter content data is realized through the 4G module. The device can transmit the data of soil organic matter content to the cloud platform in real time, and users can view it in real time on mobile phones or computers. The introduction of the real-time online monitoring system solves the problem of untimely data acquisition in traditional soil monitoring. Users can obtain the latest soil organic matter content information without going to the site in person, which greatly improves the convenience and efficiency of monitoring, and also helps to take corresponding agricultural measures in time.

[0054] The present invention provides a ventilation and ventilation structure imitating the outline of banyan leaves inside the soil spectrum module, which is intended to solve the problem of moisture accumulation when the sensor is operated in the soil for a long time. The natural form of banyan leaves has excellent ventilation effect, and the bionic design forms effective air flow inside the sensor, which is conducive to the discharge of moisture in the sensor and keeps the internal dry. Not only improves the accuracy and stability of the sensor, but also extends its service life, providing reliable protection for long-term monitoring. The leaf outline can guide and disperse the airflow when the wind blows through the leaves, which can help the leaves improve the efficiency of airflow. The banyan leaves have a large leaf area and can provide a wider range of airflow guidance. Its shape and outline are naturally adapted to airflow flow, which is conducive to guiding and dispersing airflow when the wind blows, improving air flow efficiency, and thus achieving better drying effect. Moisture and tiny particles in the soil may affect the performance and life of the sensor. Maintaining proper ventilation can not only effectively prevent moisture accumulation, but also ensure a dry environment inside the sensor, thereby maintaining its high accuracy and stability.

[0055] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", 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. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0056] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. An online monitoring device for soil organic matter content, characterized in that: The invention comprises a bracket and a plurality of soil spectrum acquisition modules. A base is installed at the bottom of the bracket. The measuring device and the base are connected via a data transmission line. The soil spectrum acquisition module comprises a shell in the shape of a quadrangular pyramid with two air vents, an optical fiber located at each side edge inside the shell, a light source located at the four corners of the bottom surface inside the shell, a photoelectric sensor located at the center of the bottom surface of the shell and an airflow guiding structure located inside the shell. The shell has no top surface. The light from the light source is transmitted to the soil to be detected via the optical fiber, and then transmitted to the photoelectric sensor via the soil to be detected. The airflow guiding structure imitates the contour surface of a banyan leaf. The two air vents are located on two opposite bottom edges of the shell. The airflow guiding structure is located below the air vents. After the airflow is blown in from one air vent, it is guided and dispersed to the other air vent via the airflow guiding structure.

2. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: The outline of the airflow guiding structure is a curved ellipse with a curvature of k 0.0125mm -1 .

3. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: The major axis of the ellipse is 160 mm, the minor axis is 100 mm; the corresponding radius of curvature is R It is 80mm.

4. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: The surface roughness of the airflow guiding structure R a 0.1μm.

5. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: The installation angle of the airflow guide structure θ is 20°.

6. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: A solar panel is installed on the top of the bracket.

7. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: It also includes a data processing module, which includes a battery for powering it, an STM32 single-chip microcomputer development board for parsing spectral data, a 4G module for uploading data in real time, and a signal amplification module.

8. The on-line monitoring device for soil organic matter content according to claim 1, characterized in that: The light sources are in four different wavebands: 620-750nm, 850nm, 940nm and 950nm.

9. A method for online monitoring of soil organic matter content, characterized in that: The following steps are included: Multiple soil spectrum acquisition modules are placed at multiple locations of the soil to be measured; a light source is irradiated through one end of a V-shaped optical fiber, the light is transmitted to the soil and, after diffuse reflection from the soil, is transmitted to the photoelectric sensor at the other end of the V-shaped optical fiber. The optical signal is converted into an electrical signal and amplified before being transmitted to the STM32 microcontroller development board. The organic matter content of the soil is calculated through the built-in trained machine learning model, and the data is uploaded to the cloud platform through the 4G module.

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