Natural rubber bark layer detection device based on hyperspectrum and detection method thereof

Abstract

The invention relates to a natural rubber tree detection device and detection method based on a hyperspectral technology. The device comprises a walking chassis, a mechanical arm, an imaging spectrometer, a visual camera, a portable supporting holder, a laser scanning system, a data acquisition system, a data transmission module, a data processing module and a power supply system. Spectral reflection data of different layers of the bark of the rubber tree are obtained by utilizing an imaging spectrometer, efficient sampling is realized by combining laser scanning, and the data are sent to an analysis terminal through wireless transmission. Through a built-in data processing module, a spectrum separation and feature extraction technology is adopted, and a machine learning model is combined to realize prediction of bark health conditions, tree ages and plant diseases and insect pests. The device has the characteristics of portability, non-destructiveness, high efficiency and intelligence, and is suitable for tree health management and precision agricultural monitoring in natural rubber parks.

Description

Technical Field

[0001] The invention relates to the field of automated machinery, and in particular to a natural rubber tree cortex layer detection device based on hyperspectral and a detection method thereof. Background Art

[0002] Natural rubber trees are important industrial plants and are widely used in rubber production. However, during the growth of rubber trees, the health of the bark is affected by many factors, such as pests and diseases, environmental factors, and growth stages. Traditional bark detection methods, such as manual inspection and chemical analysis, have great limitations, and are cumbersome to operate and inefficient. Existing non-destructive detection methods are mostly based on visual images or near-infrared technology, but these methods are often unable to efficiently and accurately analyze the different bark layers of rubber trees, especially when the bark layers are complex and the samples are variable, the accuracy and consistency of the test results are difficult to guarantee. Summary of the invention

[0003] The main purpose of the present invention is to provide a natural rubber tree bark layer detection device and detection method based on hyperspectral, and use hyperspectral imaging technology to accurately detect and analyze different layers of rubber tree bark, so as to solve the problems of bark detection difficulty, low precision and low efficiency in the prior art. Through the technical scheme of the present invention, different bark layers of natural rubber trees can be detected efficiently and non-destructively, and relevant health status, tree age, tree species and other information can be obtained, providing data support for agricultural management and rubber production.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A natural rubber bark layer detection device based on hyperspectrum comprises a walking chassis, a mechanical arm and a hyperspectral detection platform, wherein the walking chassis is fixedly mounted with the mechanical arm, the end of the mechanical arm is fixedly mounted with the hyperspectral detection platform, the hyperspectral detection platform comprises a laser scanning system and a portable supporting gimbal, the laser scanning system comprises an imaging spectrometer and a visual camera, the imaging spectrometer is fixedly mounted on the hyperspectral detection platform, the visual camera is fixedly mounted on the mechanical arm, the portable supporting gimbal comprises a platform lower bottom plate, a shock-absorbing rubber sleeve, a platform upper bottom plate, a longitudinal support arm, a horizontal support arm, a pitch motor, a transverse support arm and a yaw motor, the platform lower bottom plate is fixedly mounted on the end of the mechanical arm, a shock-absorbing rubber sleeve is fixedly mounted on the platform lower bottom plate, a platform upper bottom plate is fixedly mounted above the shock-absorbing rubber sleeve, a longitudinal support arm is fixedly mounted on the platform bottom plate, a pitch motor is fixedly mounted on the longitudinal support arm, a transverse support arm is fixedly connected to the output end of the pitch motor, a yaw motor is fixedly mounted on the lateral support arm, the output end of the yaw motor is fixedly connected to the horizontal support arm, and the imaging spectrometer is fixedly connected to the horizontal support arm.

[0005] Furthermore, the laser scanning system also includes a light shield and a supplementary light source, and the supplementary light source and the light shield are both fixedly mounted on the horizontal support arm.

[0006] Furthermore, the robotic arm includes a first connecting rod body, a first connecting rod motor, a first connecting rod sector gear, a second connecting rod body, a second connecting rod motor, a second connecting rod sector gear and a third connecting rod. The third connecting rod is fixedly installed on the walking chassis, and the second connecting rod is swingably connected to the second connecting rod body. The second connecting rod sector gear is fixedly installed on the side end surface of the connecting end of the third connecting rod and the second connecting rod body. The second connecting rod motor is fixedly installed on the second connecting rod body, and the output end of the second connecting rod motor is fixedly connected with a second swinging gear, and the second swinging gear is meshingly connected with the second connecting rod sector gear. The other end of the second connecting rod body is swingably connected to the first connecting rod body, and the first connecting rod sector gear is fixedly installed on the side end surface where the second connecting rod body is connected to the first connecting rod body. The first connecting rod motor is fixedly installed on the first connecting rod body, and the output end of the first connecting rod motor is fixedly connected with the first swinging gear, and the first swinging gear is meshingly connected with the first railing sector gear.

[0007] Furthermore, the walking chassis includes a front drive wheel system, a rear drive wheel system, a chassis body, tracks and a power supply system. The front drive wheel system and the rear drive wheel system are fixedly installed on the front and rear ends of the chassis body respectively. The power supply system is also fixedly installed on the chassis body. The front drive wheel system and the rear drive wheel system are connected by tracks.

[0008] Furthermore, the imaging spectrometer is used to obtain spectral reflectance data of different layers of natural rubber tree bark, and the imaging spectrometer covers a wavelength range of 1000nm to 2500nm; The main structure of the portable support gimbal is made of lightweight and high-strength materials and is used to mount the imaging spectrometer, so as to facilitate the stable operation of the imaging spectrometer when the device is moved among the trees; The laser scanning system is used to scan the surface of the rubber tree and guide the imaging spectrometer to accurately sample the target area.

[0009] Furthermore, a data acquisition system, a data transmission module and a data processing module are fixedly installed on the walking chassis. The data acquisition system is electrically connected to the laser scanning system and the imaging spectrometer, the data acquisition system is electrically connected to the data transmission module, and the data transmission module is electrically connected to the data processing module; The data processing module performs noise reduction, spectrum separation and feature extraction on the collected spectral data to generate spectral features for analysis. The data acquisition system, data transmission module and data processing module are all electrically connected to the power supply system.

[0010] Furthermore, the laser scanning system can adjust the scanning range and resolution according to the texture characteristics of the rubber tree surface; the data acquisition system and the data transmission module are both provided with a Bluetooth module and a Wi-Fi module for realizing data transmission within a short and long distance range; the power supply system includes a solar auxiliary power supply module for extending the endurance of the device in field operations; the data processing module uses a principal component analysis algorithm to reduce the dimension of the spectral data, and combines the support vector machine model to classify and predict the health status, tree age and disease of the rubber tree bark; the imaging spectrometer works in conjunction with the supplementary light source to provide uniform illumination to the target bark area when there is insufficient light.

[0011] Furthermore, a detection method of a portable natural rubber tree cortex layer detection device based on hyperspectral is characterized by comprising the following steps: Step 1: The visual camera identifies the distance L1 between the detection device and the natural rubber tree, and the data processing module controls the front drive wheel system speed ω1 and the rear drive wheel system speed ω2 of the walking chassis according to the natural rubber forest environment output; Step 2: When the distance L1 detected by the visual camera reaches the target distance range, the walking chassis stops moving, and the visual depth camera further identifies the spatial position of the bark of the target natural rubber tree to be detected as x0, y0, and z0, and feeds the information back to the data processing module through the data acquisition system and the data transmission module; Step 3: The initial spatial position of the end of the robotic arm is x1, y1, z1, and the data processing module controls the end of the robotic arm to move to the target spatial position x2, y2, z2; Step 4: The data processing module controls the laser scanning system of the hyperspectral detection platform to scan the surface of the rubber tree bark and identify the target area; Step 5, using an imaging spectrometer to obtain spectral reflectance data of the rough cortex, sand cortex, yellow cortex and cystic cortex of the natural rubber tree bark, and transmitting the spectral data to the data processing module through a data acquisition system and a data transmission module; Step 6: In the data processing module, the spectral data is subjected to noise reduction, band selection and feature extraction, and a network model for classification, detection, recognition and identification of different cortical layers of natural rubber trees is established; Step 7: Use a machine learning-based model to analyze the spectral data and generate a bark health report, including tree age estimation, health status diagnosis, and pest and disease warning information.

[0012] Furthermore, in the step four, the wavelet transform technology is used to reduce the noise of the spectral data to improve the accuracy of data analysis; in the step five, the machine learning model is combined with a deep learning algorithm to extract health features and optimize classification results through multi-layer convolution of spectral data of different bark layers.

[0013] Furthermore, the bark health report is displayed in a graphical interface through a mobile terminal, and supports users to view the test data and analysis results in real time.

[0014] Compared with the prior art, the present invention has the following beneficial effects: The present invention uses hyperspectral imaging technology to accurately detect and analyze different layers of rubber tree bark, solving the problems of difficulty, low precision, and low efficiency in bark detection in the prior art. Through the technical solution of the present invention, different bark layers of natural rubber trees can be detected efficiently and non-destructively, and relevant information such as health status, tree age, and tree species can be obtained, providing data support for agricultural management and rubber production. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the three-dimensional structure of the present invention; Figure 2 It is a schematic diagram of the three-dimensional structure of the portable supporting platform of the present invention; Figure 3 It is a control flow diagram of the present invention; Figure 4 It is a schematic diagram of the workflow of the present invention.

[0016] Reference numerals: 100. Hyperspectral detection platform; 200. Robotic arm; 300. Walking chassis; 101. Lower plate of platform; 102. Shock-absorbing rubber sleeve; 103. Upper plate of platform; 104. Longitudinal support arm; 105. Sunshade; 106. Supplementary light source; 107. Lateral support arm; 108. Pitch motor; 109. Horizontal support arm; 110. Yaw motor; 111. Imaging spectrometer; 112. Visual camera; 201. First connecting rod body; 202. First connecting rod motor; 203. First connecting rod sector gear; 204. Second connecting rod body; 205. Second connecting rod motor; 206. Second connecting rod sector gear; 207. Third connecting rod; 301. Front drive wheel system; 302. Rear drive wheel system; 303. Chassis body; 304. Track; 600. Power supply system; 400. Data transmission module; 500. Data processing module. DETAILED DESCRIPTION

[0017] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, thereby making a clearer and more definite definition of the protection scope of the present invention.

[0018] See also Figure 1-4As shown, a natural rubber bark layer detection device based on hyperspectral comprises a walking chassis 300, a mechanical arm 200 and a hyperspectral detection platform 100, wherein the walking chassis 300 is fixedly mounted with the mechanical arm 200, and the end of the mechanical arm 200 is fixedly mounted with the hyperspectral detection platform 100, and the hyperspectral detection platform 100 comprises a laser scanning system and a portable supporting gimbal, wherein the laser scanning system comprises an imaging spectrometer 111 and a visual camera 112, wherein the imaging spectrometer 111 is fixedly mounted on the hyperspectral detection platform 100, and the visual camera 112 is fixedly mounted on the mechanical arm 200, and the portable supporting gimbal comprises a platform lower bottom plate 101, a shock-absorbing rubber sleeve 102, a platform bottom plate 103, a longitudinal support arm 104, a horizontal support arm 109, a pitch motor 108, a lateral support arm 107 and a yaw motor 110, wherein the platform lower bottom plate 101 is fixedly mounted on the end of the mechanical arm 200, and the horizontal support arm 109, A shock-absorbing rubber sleeve is fixedly installed on the bottom plate 101 under the platform, and a platform bottom plate 103 is fixedly installed above the shock-absorbing rubber sleeve 102. The shock-absorbing rubber sleeve 102 is used for overall shockproofing of the platform. A longitudinal support arm 104 is fixedly installed on the platform bottom plate 103, and a pitch motor 108 is fixedly installed on the longitudinal support arm 104. A transverse support arm 107 is fixedly connected to the output end of the pitch motor 108, and a yaw motor 110 is fixedly installed on the transverse support arm 107. A horizontal support arm 109 is fixedly connected to the output end of the yaw motor 110, and an imaging spectrometer 111 is fixedly connected to the horizontal support arm 109. The imaging spectrometer 111 is fixed by the horizontal support arm 109. The yaw motor 110 is used to adjust the clockwise / counterclockwise rotation angle of the imaging spectrometer 111, the transverse support arm 107 is used to support the yaw motor 110, and the pitch motor 108 is used to adjust the pitch angle of the imaging spectrometer.

[0019] The laser scanning system further includes a light shield 105 and a supplementary light source 106 , and both the supplementary light source 106 and the light shield 105 are fixedly mounted on a horizontal support arm 109 .

[0020] The mechanical arm 200 includes a first connecting rod body 201, a first connecting rod motor 202, a first connecting rod sector gear 203, a second connecting rod body 204, a second connecting rod motor 205, a second connecting rod sector gear 206 and a third connecting rod 207. The third connecting rod 207 is fixedly mounted on the walking chassis 300. The second connecting rod body 204 is swingably connected to the third connecting rod 207. The second connecting rod sector gear 206 is fixedly mounted on the side end surface of the connecting end of the third connecting rod 207 and the second connecting rod body 204. The second connecting rod motor 205 is fixedly mounted on the second connecting rod body 204. 5. A second swinging gear is fixedly connected to the output end of the second connecting rod motor 205, and the second swinging gear is meshingly connected to the second connecting rod sector gear 206. The other end of the second connecting rod body 204 is swingingly connected to the first connecting rod body 201. The first connecting rod sector gear 203 is fixedly installed on the side end surface where the second connecting rod body 204 is connected to the first connecting rod body 201. The first connecting rod motor 202 is fixedly installed on the first connecting rod body 201. The output end of the first connecting rod motor 202 is fixedly connected to the first swinging gear, and the first swinging gear is meshingly connected to the first railing sector gear.

[0021] The walking chassis 300 includes a front driving wheel system 301, a rear driving wheel system 302, a chassis body 303, tracks 304 and a power supply system 600. The front driving wheel system 301 and the rear driving wheel system 302 are fixedly installed at the front and rear ends of the chassis body 303 respectively. The power supply system 600 is also fixedly installed on the chassis body 303. The front driving wheel system 301 and the rear driving wheel system 302 are connected via tracks 304.

[0022] The imaging spectrometer 111 is used to obtain spectral reflectance data of different layers of natural rubber tree bark, and the imaging spectrometer 111 covers a wavelength range of 1000nm to 2500nm; The main structure of the portable support gimbal is made of lightweight and high-strength materials, and is used to mount the imaging spectrometer 111, so as to facilitate the stable operation of the imaging spectrometer 111 when the device moves among the trees; The laser scanning system is used to scan the surface of the rubber tree and guide the imaging spectrometer 111 to accurately sample the target area.

[0023] The walking chassis 300 is also fixedly mounted with a data acquisition system, a data transmission module 400 and a data processing module 500. The data acquisition system is electrically connected to the laser scanning system and the imaging spectrometer 111, the data acquisition system is electrically connected to the data transmission module 400, and the data transmission module 400 is electrically connected to the data processing module 500. The data processing module 500 performs noise reduction, spectrum separation and feature extraction on the collected spectral data to generate spectral features for analysis. The data acquisition system, the data transmission module 400 and the data processing module 500 are all electrically connected to the power supply system 600.

[0024] The laser scanning system can adjust the scanning range and resolution according to the texture characteristics of the rubber tree surface; the data acquisition system and the data transmission module 400 are both provided with a Bluetooth module and a Wi-Fi module for realizing data transmission within a short and long distance range; the power supply system 600 includes a solar auxiliary power supply module for extending the endurance of the device in field operations; the data processing module 500 uses a principal component analysis PCA algorithm to reduce the dimension of the spectral data, and combines the support vector machine SVM model to classify and predict the health status, tree age and disease of the rubber tree bark; the imaging spectrometer 111 works in conjunction with the supplementary light source 106 to provide uniform illumination to the target bark area when there is insufficient light.

[0025] The detection method comprises the following steps: Step 1: The visual camera 112 identifies the distance L1 between the detection device and the natural rubber tree, and the data processing module 500 controls the speed ω1 of the front driving wheel system 301 and the speed ω2 of the rear driving wheel system 302 of the walking chassis 300 according to the natural rubber forest environment output; Step 2: When the distance L1 detected by the visual camera 112 reaches the target distance range, the walking chassis 300 stops moving, and the visual depth camera further identifies the spatial position of the bark of the target natural rubber tree to be detected as x0, y0, z0, and feeds the information back to the data processing module 500 through the data acquisition system and the data transmission module 400; Step 3: The initial spatial position of the end of the robotic arm 200 is x1, y1, z1, and the data processing module 500 controls the end of the robotic arm 200 to move to the target spatial position x2, y2, z2; Step 4: The data processing module 500 controls the laser scanning system of the hyperspectral detection platform 100 to perform surface scanning on the rubber tree bark to identify the target area; Step 5: Using the imaging spectrometer 111 to obtain the spectral reflectance data of the coarse cortex, sand cortex, yellow cortex and cystic cortex of the natural rubber tree bark, the spectral data is transmitted to the data processing module 500 through the data acquisition system and the data transmission module 400; Step 6: In the data processing module 500, the spectral data is subjected to noise reduction, band selection and feature extraction, and a network model for classification, detection, recognition and identification of different cortical layers of natural rubber trees is established; Step 7: Use a machine learning-based model to analyze the spectral data and generate a bark health report, including tree age estimation, health status diagnosis, and pest and disease warning information.

[0026] In the step 4, the wavelet transform technology is used to reduce the noise of the spectral data to improve the accuracy of data analysis; in the step 5, the machine learning model is combined with a deep learning algorithm to extract health features and optimize classification results through multi-layer convolution of spectral data of different bark layers.

[0027] The bark health report is displayed in a graphical interface through a mobile terminal, and supports users to view test data and analysis results in real time.

[0028] The above description is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiment. Any equivalent modifications or changes made by ordinary technicians in this field based on the contents disclosed by the present invention should be included in the protection scope recorded in the claims.

Claims

1. A natural rubber tree cortex layer detection device based on hyperspectral, comprising a walking chassis (300), a mechanical arm (200) and a hyperspectral detection platform (100), wherein the walking chassis (300) is fixedly mounted with the mechanical arm (200), and the end of the mechanical arm (200) is fixedly mounted with the hyperspectral detection platform (100), characterized in that: The hyperspectral detection platform (100) comprises a laser scanning system and a portable support platform. The laser scanning system comprises an imaging spectrometer (111) and a visual camera (112). The imaging spectrometer (111) is fixedly mounted on the hyperspectral detection platform (100). The visual camera (112) is fixedly mounted on a mechanical arm (200). The portable support platform comprises a platform bottom plate (101), a shock-absorbing rubber sleeve (102), a platform bottom plate (103), a longitudinal support arm (104), a horizontal support arm (109), a pitch motor (108), a lateral support arm (107) and a yaw motor (110). The platform bottom plate (101) is fixedly mounted on the mechanical arm (200). 00), a shock-absorbing rubber sleeve (102) is fixedly installed on the lower bottom plate (101) of the platform, a platform upper bottom plate (103) is fixedly installed above the shock-absorbing rubber sleeve (102), a longitudinal support arm (104) is fixedly installed on the platform bottom plate (103), a pitch motor (108) is fixedly installed on the longitudinal support arm (104), a transverse support arm (107) is fixedly connected to the output end of the pitch motor (108), a yaw motor (110) is fixedly installed on the transverse support arm (107), the output end of the yaw motor (110) is fixedly connected to the horizontal support arm (109), and the horizontal support arm (109) is fixedly connected to the imaging spectrometer (111).

2. A natural rubber tree cortex layer detection device based on hyperspectral according to claim 1, characterized in that: The laser scanning system further comprises a light shield (105) and a supplementary light source (106), and both the supplementary light source (106) and the light shield (105) are fixedly mounted on the horizontal support arm (109).

3. A natural rubber tree cortex layer detection device based on hyperspectral according to claim 1, characterized in that: The mechanical arm (200) comprises a first connecting rod body (201), a first connecting rod motor (202), a first connecting rod sector gear (203), a second connecting rod body (204), a second connecting rod motor (205), a second connecting rod sector gear (206) and a third connecting rod (207). The third connecting rod (207) is fixedly mounted on a walking chassis (300). The third connecting rod (207) is swingably connected to the second connecting rod body (204). The second connecting rod sector gear (206) is fixedly mounted on the side end surface of the connecting end of the third connecting rod (207) and the second connecting rod body (204). The second connecting rod motor (205) is fixedly connected to the output end of the second connecting rod motor (205), and the second swinging gear is meshingly connected to the second connecting rod sector gear (206). The other end of the second connecting rod body (204) is swingably connected to the first connecting rod body (201), and the side end surface where the second connecting rod body (204) is connected to the first connecting rod body (201) is fixedly installed with the first connecting rod sector gear (203). The first connecting rod motor (202) is fixedly installed on the first connecting rod body (201), and the output end of the first connecting rod motor (202) is fixedly connected to the first swinging gear, and the first swinging gear is meshingly connected to the first railing sector gear.

4. The natural rubber tree cortex layer detection device based on hyperspectral according to claim 1, characterized in that: The walking chassis (300) comprises a front driving wheel system (301), a rear driving wheel system (302), a chassis body (303), a crawler track (304) and a power supply system (600). The front driving wheel system (301) and the rear driving wheel system (302) are fixedly mounted at the front end and the rear end of the chassis body (303), respectively. The power supply system (600) is also fixedly mounted on the chassis body (303). The front driving wheel system (301) and the rear driving wheel system (302) are connected via the crawler track (304).

5. The natural rubber tree cortex layer detection device based on hyperspectral according to claim 1, characterized in that: The imaging spectrometer (111) is used to obtain spectral reflectance data of different layers of natural rubber tree bark, and the imaging spectrometer (111) covers a wavelength range of 1000nm to 2500nm; The main structure of the portable supporting platform is made of lightweight high-strength material and is used to mount the imaging spectrometer (111), so as to facilitate the stable operation of the imaging spectrometer (111) when the device is moved among the trees. The laser scanning system is used to scan the surface of the rubber tree and guide the imaging spectrometer (111) to accurately sample the target area.

6. The natural rubber tree cortex layer detection device based on hyperspectral according to claim 1, characterized in that: A data acquisition system, a data transmission module (400) and a data processing module (500) are also fixedly mounted on the walking chassis (300); the data acquisition system is electrically connected to the laser scanning system and the imaging spectrometer (111); the data acquisition system is electrically connected to the data transmission module (400); and the data transmission module (400) is electrically connected to the data processing module (500); The data processing module (500) performs noise reduction, spectrum separation and feature extraction on the collected spectral data to generate spectral features for analysis. The data collection system, the data transmission module (400) and the data processing module (500) are all electrically connected to the power supply system (600).

7. The natural rubber tree cortex layer detection device based on hyperspectral according to claim 6, characterized in that: The laser scanning system can adjust the scanning range and resolution according to the texture characteristics of the rubber tree surface; the data acquisition system and the data transmission module (400) are both provided with a Bluetooth module and a Wi-Fi module for realizing data transmission within a short distance and a long distance; the power supply system (600) includes a solar auxiliary power supply module for extending the endurance of the device in field operations; the data processing module (500) uses a principal component analysis (PCA) algorithm to reduce the dimension of the spectral data, and combines a support vector machine (SVM) model to classify and predict the health status, tree age and disease of the rubber tree bark; the imaging spectrometer (111) cooperates with the supplementary light source (106) to provide uniform illumination to the target bark area when the illumination is insufficient.

8. A detection method applied to the portable natural rubber tree cortex layer detection device based on hyperspectral as claimed in any one of claims 1 to 7, characterized in that: The steps include: Step 1: The visual camera (112) identifies the distance L1 between the detection device and the natural rubber tree, and the data processing module (500) controls the speed ω1 of the front driving wheel system (301) and the speed ω2 of the rear driving wheel system (302) of the walking chassis (300) according to the natural rubber forest environment output; Step 2: When the distance L1 detected by the visual camera (112) reaches the target distance range, the walking chassis (300) stops moving, and the visual depth camera further identifies the spatial position of the bark of the natural rubber tree to be detected as x0, y0, z0, and feeds back the information to the data processing module (500) through the data acquisition system and the data transmission module (400); Step 3: The initial spatial position of the end of the robotic arm (200) is x1, y1, z1, and the data processing module (500) controls the end of the robotic arm (200) to move to the target spatial position x2, y2, z2; Step 4: The data processing module (500) controls the laser scanning system of the hyperspectral detection platform (100) to perform surface scanning on the rubber tree bark to identify the target area; Step 5: using an imaging spectrometer (111) to obtain spectral reflectance data of the coarse cortex, sand cortex, yellow cortex and cystic cortex of the natural rubber tree bark, and transmitting the spectral data to the data processing module (500) through a data acquisition system and a data transmission module (400); Step 6: In the data processing module (500), the spectral data is subjected to noise reduction, band selection and feature extraction, and a network model for classifying, detecting, identifying and distinguishing different cortical layers of natural rubber trees is established; Step 7: Use a machine learning-based model to analyze the spectral data and generate a bark health report, including tree age estimation, health status diagnosis, and pest and disease warning information.

9. The detection method of the portable natural rubber tree cortex layer detection device based on hyperspectral according to claim 8 is characterized in that: In the step 4, the wavelet transform technology is used to reduce the noise of the spectral data to improve the accuracy of data analysis; in the step 5, the machine learning model is combined with the deep learning algorithm to extract health features and optimize the classification results through multi-layer convolution of spectral data of different bark layers.

10. The detection method of the portable natural rubber tree cortex layer detection device based on hyperspectral according to claim 8 is characterized in that: The bark health report is displayed in a graphical interface through a mobile terminal, and supports users to view test data and analysis results in real time.

Images