Automatic slitting method for high-quality tobacco leaves

Abstract

The invention discloses a high-quality tobacco leaf automatic slitting method which comprises the following steps: data acquisition: scanning tobacco leaves through a hyperspectral camera system to obtain hyperspectral image data; region extraction: positioning and extracting main veins, branch veins and tobacco lamina parts by utilizing computer vision; extracting spectral information to obtain spectral curves in different wavelength ranges; analyzing chemical components; analyzing moisture characteristics; mechanical arm positioning is conducted, specifically, the to-be-cut tobacco leaf part is accurately positioned; cutting operation: cutting the main vein, the branch vein and the high-quality part by using cutting equipment; and quality selection: evaluating the quality of the cut tobacco leaf part, and selecting a high-quality tobacco leaf part suitable for high-grade cigarette development. According to the automatic slitting method for the high-quality tobacco leaves, a hyperspectral camera mode in machine vision and automatic cutting equipment are adopted, and a precise positioning technology is combined, so that all parts of the tobacco leaves are rapidly and accurately cut, and the high-quality tobacco leaves are obtained to be used for manufacturing high-grade cigarettes.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco processing, and more specifically, to a high-quality tobacco leaf automatic cutting method. Background Art

[0002] The tobacco industry has been committed to improving the quality of cigarettes, and the reasonable cutting of tobacco stems and tobacco leaves plays a key role in the final quality of cigarettes. In the production process of cigarettes, the cutting of tobacco stems and tobacco leaves is a key step that affects the quality of cigarettes. By accurately cutting the tobacco stems and tobacco leaves, the proportion and length of tobacco can be adjusted, thereby affecting the key quality indicators of cigarettes such as smoke sensation, combustion performance and taste. The cutting process of tobacco stems and tobacco leaves involves cutting, crushing and other operations, among which the precision and effect of cutting are directly related to the uniformity and quality of tobacco. Reasonable cutting technology can ensure the uniform distribution of tobacco components in cigarettes, thereby affecting key factors such as the burning speed and smoke generation of cigarettes. By accurately controlling the cutting process of tobacco stems and tobacco leaves, the quality and consistency of cigarettes can be improved, making them more in line with the taste needs of consumers. Therefore, in-depth research and technical optimization of the cutting process of tobacco stems and tobacco leaves are essential.

[0003] Each part of the tobacco leaf contains different chemical compositions and moisture characteristics. First, the stem is the main support of the tobacco leaf and plays a key role in forming the shape and stability of the cigarette, while the leaf is the most nutrient-rich part of the tobacco leaf, which contains nicotine, fatty acids, proteins and other components, which produce complex aromas and flavors when burned. Secondly, the moisture characteristics in the tobacco leaf are crucial. The appropriate moisture content can keep the tobacco leaf flexible and elastic, and is conducive to the adsorption of other components in the cigarette, thereby providing better burning conditions and taste. However, too high or too low moisture content will affect the burning performance of the tobacco leaf and the taste of the cigarette. Therefore, in the process of cutting tobacco leaves, scientific regulation should be carried out according to the chemical composition and moisture characteristics of different parts to ensure that high-quality tobacco leaves are cut. In short, the differences in the chemical composition and moisture characteristics of each part of the tobacco leaf determine the quality characteristics of the tobacco leaf. Through reasonable cutting and regulation processes, the quality of cigarettes can be improved, and then the taste and quality of cigarettes can be improved.

[0004] Therefore, a high-quality automatic tobacco leaf cutting method is urgently needed. Summary of the invention

[0005] The purpose of the present invention is to provide a high-quality automatic tobacco leaf cutting method to solve the above-mentioned problems in the prior art, by achieving rapid and accurate cutting of various parts of the tobacco leaves, so as to obtain high-quality tobacco leaves for the manufacture of high-grade cigarettes.

[0006] The present invention provides a high-quality tobacco leaf automatic cutting method, which comprises:

[0007] Data collection: Scan tobacco leaves with a hyperspectral camera system to obtain hyperspectral image data;

[0008] Region extraction: using computer vision technology to locate and extract the main veins, side veins and tobacco leaves of tobacco leaves;

[0009] Spectral information extraction, extracting the spectral information of each pixel from the hyperspectral image data, and performing band selection and reconstruction to obtain spectral curves within different wavelength ranges;

[0010] Chemical composition analysis: Based on the extracted spectral curves, the chemical composition content of each part is predicted by chemometric methods;

[0011] Moisture signature analysis, non-destructive detection and prediction of moisture content in various parts of tobacco leaves using a pre-built regression model that characterizes the relationship between spectral signals and moisture content;

[0012] Robotic arm positioning: Based on the results of tobacco leaf area extraction and the pre-set cutting plan, the robot arm accurately locates the tobacco leaf part to be cut;

[0013] Cutting operation: according to the positioning information of the robot arm, the cutting equipment is used to automatically cut the main vein, branch veins and high-quality parts;

[0014] Quality selection, comparing the cut tobacco leaf parts with pre-established chemical composition models and moisture characteristic models to evaluate the quality of the cut tobacco leaf parts and select high-quality tobacco leaf parts suitable for high-grade cigarette development.

[0015] In the method for automatically cutting high-quality tobacco leaves as described above, preferably, the data acquisition includes scanning the tobacco leaves through a hyperspectral camera system to obtain hyperspectral image data, which specifically includes:

[0016] The tobacco leaves are scanned using a professional hyperspectral camera system to provide hyperspectral image data in multiple bands. The professional hyperspectral camera system includes Specim FX series or Hyper-Cam.

[0017] In the method for automatically cutting high-quality tobacco leaves as described above, preferably, the region extraction uses computer vision technology to locate and extract the main vein, branch veins and tobacco leaves of the tobacco leaves, and specifically includes:

[0018] Use shape recognition algorithms or deep learning methods to extract the main veins, side veins and tobacco leaves.

[0019] The high-quality tobacco leaf automatic cutting method as described above, wherein preferably, the spectral information extraction, extracting the spectral information of each pixel from the hyperspectral image data, and performing band selection and reconstruction to obtain spectral curves within different wavelength ranges, specifically includes:

[0020] Hyperspectral data processing software is used to perform band selection, reconstruction and sample extraction on the collected hyperspectral images to obtain the spectral curve of each pixel.

[0021] In the method for automatic cutting of high-quality tobacco leaves as described above, preferably, the chemical composition analysis predicts the chemical composition content of each part by a chemometric method based on the extracted spectral curve, specifically including:

[0022] Based on the spectral curves, a chemometrics software package was used to apply principal component analysis or partial least squares method to model and analyze the quantitative relationship between the spectrum and chemical composition.

[0023] In the method for automatic cutting of high-quality tobacco leaves as described above, preferably, the moisture characteristic analysis uses a pre-built regression model to perform non-destructive detection and prediction of the moisture content of each part of the tobacco leaf, specifically including:

[0024] According to the correlation between the spectral curve and the moisture content, a regression model was constructed using a machine learning algorithm.

[0025] In the method for automatically cutting high-quality tobacco leaves as described above, preferably, the positioning of the robot arm, based on the result of tobacco leaf area extraction and the pre-set cutting scheme, accurately positions the tobacco leaf portion to be cut by the robot arm, specifically including:

[0026] According to the results of tobacco leaf area extraction, a precision visual navigation system is used to achieve high-precision tobacco leaf positioning through robotic arm positioning technology.

[0027] The method for automatically cutting high-quality tobacco leaves as described above, wherein preferably, the cutting operation, based on the positioning information of the robot arm, uses a cutting device to automatically complete the cutting of the main vein, the branch vein and the high-quality part, specifically includes:

[0028] According to the positioning information of the robotic arm, select professional equipment suitable for tobacco leaf cutting to plan the cutting path, and perform automated cutting operations through programming and control of the equipment.

[0029] In the method for automatically cutting high-quality tobacco leaves as described above, preferably, the quality selection compares the cut tobacco leaf parts with the pre-established chemical composition model and moisture characteristic model to evaluate the quality of the cut tobacco leaf parts and select the high-quality tobacco leaf parts suitable for the development of high-grade cigarettes, specifically including:

[0030] Using data analysis software, the collected spectral information is compared and predicted with the pre-established chemical composition model and moisture characteristic model, thereby evaluating the quality of the cut tobacco leaf part.

[0031] The high-quality tobacco leaf automatic cutting method as described above, wherein preferably, after the data collection and before the region extraction, the high-quality tobacco leaf automatic cutting method further comprises:

[0032] Image preprocessing, preprocessing the collected hyperspectral image, the preprocessing includes: performing at least one operation of spatial correction, background elimination, noise filtering and color calibration on the collected hyperspectral image.

[0033] The present invention provides a high-quality automatic tobacco leaf cutting method, which adopts a hyperspectral camera method in machine vision to scan the contour of tobacco leaves, distinguish the main veins, side veins and tobacco leaves of the tobacco leaves, and determine the chemical composition and moisture characteristics of each part of the tobacco leaves based on different spectral information provided by the hyperspectral technology; then a cutting device is adopted, combined with a mechanical arm positioning technology, to automatically locate and cut the main veins and side veins of the tobacco leaves, so as to separate higher-quality tobacco leaf parts, which can be used for the development of high-grade cigarettes; by adopting a hyperspectral camera method in machine vision and an automated cutting device, combined with precise positioning technology, it is possible to achieve rapid and accurate cutting of each part of the tobacco leaves, so as to obtain high-quality tobacco leaves for the manufacture of high-grade cigarettes, which not only improves the efficiency of tobacco processing, but also can effectively control the cutting quality of tobacco leaves and improve the overall quality of cigarettes; the combination of tobacco stem and tobacco leaf cutting technology with hyperspectral technology provides more accurate and advanced technical means for cigarette production, which can further improve the quality and market competitiveness of cigarettes. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to make the purpose, technical solutions and advantages of the present invention more clear, the present invention will be further described below with reference to the accompanying drawings, in which:

[0035] Figure 1 This is a flow chart of an embodiment of the high-quality tobacco leaf automatic cutting method provided by the present invention. DETAILED DESCRIPTION

[0036] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. The description of the exemplary embodiments is merely illustrative and is in no way intended to limit the present disclosure and its application or use. The present disclosure can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to make the present disclosure thorough and complete and to fully express the scope of the present disclosure to those skilled in the art. It should be noted that unless otherwise specifically stated, the relative arrangement of the components and steps, the composition of the materials, the numerical expressions and the numerical values ​​set forth in these embodiments should be interpreted as being merely exemplary and not as limiting.

[0037] The words "first", "second" and similar words used in this disclosure do not indicate any order, quantity or importance, but are only used to distinguish different parts. The words "include" or "comprises" and similar words mean that the elements before the word include the elements listed after the word, and do not exclude the possibility of including other elements. "Up", "down" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0038] In the present disclosure, when a specific component is described as being located between a first component and a second component, there may or may not be an intermediate component between the specific component and the first component or the second component. When a specific component is described as being connected to other components, the specific component may be directly connected to the other components without an intermediate component, or may not be directly connected to the other components but have an intermediate component.

[0039] All terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as those understood by ordinary technicians in the field to which the present disclosure belongs, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries, such as general dictionaries, should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an idealized or extremely formal sense, unless explicitly defined herein.

[0040] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the technologies, methods, and equipment should be considered part of the specification.

[0041] Hyperspectral technology has been widely used in the agricultural and food industries, and has gradually become a research hotspot in the tobacco field. Hyperspectral technology can detect the spectral characteristics of substances at various wavelengths to achieve rapid and non-destructive analysis of tobacco components, and has the characteristics of high sensitivity and high resolution. In the process of cutting tobacco stems and leaves, hyperspectral technology can be used to monitor key parameters such as the chemical composition, moisture content, and nutritional components of tobacco leaves. By real-time monitoring and adjusting the cutting parameters, the quality consistency of tobacco leaves can be maintained to the maximum extent, and the overall quality of cigarettes can be improved.

[0042] like Figure 1 As shown, the high-quality tobacco leaf automatic cutting method provided in this embodiment includes the following steps in actual implementation:

[0043] Step S1, data acquisition, scanning tobacco leaves through a hyperspectral camera system to obtain hyperspectral image data.

[0044] Specifically, a professional hyperspectral camera system is used to scan tobacco leaves to provide hyperspectral image data in multiple bands. The professional hyperspectral camera system includes Specim FX series or Hyper-Cam.

[0045] In one embodiment of the present invention, a Specim FX series hyperspectral camera system is used to scan tobacco leaves, and hyperspectral image data including ultraviolet, visible and infrared bands are obtained. In a specific implementation, the tobacco leaves are first placed on an appropriate device and scanned using a Specim FX hyperspectral camera. The camera system can simultaneously capture spectral information in multiple wavelength ranges, including invisible ultraviolet and infrared bands, so that a hyperspectral image containing multi-dimensional information can be obtained.

[0046] Furthermore, after the data collection and before the region extraction, the high-quality tobacco leaf automatic cutting method further includes:

[0047] Step S10: image preprocessing: preprocessing the collected hyperspectral image.

[0048] The preprocessing includes: performing at least one of spatial correction, background elimination, noise filtering and color calibration on the collected hyperspectral image. In a specific implementation, the preprocessing can be performed using the open source image processing library OpenCV or the professional image processing software ENVI / IDL.

[0049] In one embodiment of the present invention, the collected hyperspectral image is corrected and enhanced to improve the accuracy and reliability of subsequent processing. Specifically, the method in the OpenCV library can be used to perform noise correction and contrast enhancement on the collected hyperspectral image, which can remove the noise in the image and enhance the contrast of the image, thereby making the subsequent image processing and analysis more accurate and reliable.

[0050] Step S2: region extraction: using computer vision technology to locate and extract the main vein, branch veins and tobacco leaves of the tobacco leaf.

[0051] In step S2, the main vein, branch veins and tobacco leaf parts of the tobacco leaf can be located and extracted by contour segmentation and feature extraction. Specifically, the main vein, branch veins and tobacco leaf parts of the tobacco leaf are extracted by using shape recognition algorithms (such as edge detection, local binarization) or deep learning methods (such as convolutional neural networks). In specific implementation, it can be implemented by OpenCV and deep learning frameworks such as TensorFlow or PyTorch.

[0052] In one embodiment of the present invention, in the corrected image, the main vein, the side vein and the tobacco leaf are located and segmented using the Canny edge detection algorithm. The Canny edge detection algorithm is an image processing technology that can effectively identify the edges in the image.

[0053] In step S1, step S10 and step S2, high-quality hyperspectral image data can be obtained through data acquisition, image preprocessing and tobacco leaf region extraction, and the tobacco leaves can be segmented into main vein, branch vein and leaf parts.

[0054] Step S3, spectral information extraction, extracting the spectral information of each pixel from the hyperspectral image data, and performing band selection and reconstruction to obtain spectral curves within different wavelength ranges.

[0055] Specifically, the hyperspectral data processing software is used to perform band selection, reconstruction and sample extraction on the collected hyperspectral image to obtain the spectral curve of each pixel point, wherein the hyperspectral data processing software may be, for example, ENVI or Spectral Python.

[0056] In one embodiment of the present invention, a band range of interest is selected from the image data extracted from the tobacco leaf area, and the spectral curve of each pixel is extracted using the Spectral Python library, so that the reflectance or absorbance information of each pixel at different wavelengths can be obtained, providing a basis for subsequent spectral analysis and modeling.

[0057] Step S4: chemical composition analysis: based on the extracted spectral curve, the chemical composition content of each part is predicted by chemometric method.

[0058] Specifically, based on the spectral curve, a chemometrics software package is used to apply principal component analysis (PCA) or partial least squares (PLS) to model and analyze the quantitative relationship between the spectrum and the chemical composition. The chemometrics software package may be, for example, Unscrambler, SIMCA or Wise-Modeler.

[0059] In one embodiment of the present invention, based on the relationship between the spectrum and the chemical components, a chemical component regression model is established using a partial least squares (PLS) algorithm, wherein the chemical component regression model is a relationship model between the spectrum and the chemical component content of each part of the tobacco leaf. A prediction model can be established by using the spectral data of the training sample and the corresponding chemical analysis results, which can associate the measured spectral data with the chemical component content of each part of the tobacco leaf, thereby realizing the content prediction of each part.

[0060] Step S5, moisture feature analysis, using a pre-built regression model to perform non-destructive detection and prediction of the moisture content of each part of the tobacco leaf, wherein the regression model is used to characterize the relationship between the spectral signal and the moisture content.

[0061] Specifically, according to the correlation between the spectral curve and the moisture content, a moisture regression model is constructed by a machine learning algorithm. The moisture regression model may be, for example, a support vector regression (SVR) or a random forest. In a specific implementation, it may be implemented using a toolkit such as scikit-learn in Python or caret in R language.

[0062] In one embodiment of the present invention, a support vector regression (SVR) algorithm is used to establish a regression model based on the spectral curve and the known moisture content of tobacco leaves. Through this model, the moisture content of tobacco leaves can be non-destructively detected and predicted.

[0063] In steps S3 to S5, spectral information is extracted and chemical composition analysis and moisture characteristic analysis are performed to evaluate the quality of tobacco leaves.

[0064] Step S6, robotic arm positioning: based on the tobacco leaf area extraction result and the pre-set cutting plan, the robotic arm accurately positions the tobacco leaf portion to be cut.

[0065] Specifically, according to the result of tobacco leaf area extraction, a precision visual navigation system is used to achieve high-precision tobacco leaf positioning through a robotic arm positioning technology, wherein the precision visual navigation system may be, for example, a 3D camera or a laser radar, and the robotic arm positioning technology may be, for example, RoboDK or URSim.

[0066] In one embodiment of the present invention, a three-dimensional camera and a robotic arm technology are used to accurately locate the tobacco leaf portion to be cut. Specifically, the tobacco leaf position information is captured in real time by a visual navigation system, and the tobacco leaf portion to be cut is accurately located using this information, providing an important basis for subsequent cutting operations.

[0067] Step S7, cutting operation, according to the positioning information of the robot arm, using the cutting equipment to automatically complete the cutting of the main vein, branch veins and high-quality parts.

[0068] Specifically, according to the positioning information of the robot arm, professional equipment suitable for tobacco leaf cutting (such as CAD software) is selected to plan the cutting path, and the equipment is controlled through programming to perform automated cutting operations.

[0069] In one embodiment of the present invention, a slicer is used to automatically slice the accurately positioned tobacco leaves. The slicer generally has high-speed and precise cutting capabilities, and slices the tobacco leaves according to preset parameters and requirements. In this way, the required tobacco leaf slices can be obtained quickly and efficiently, and the shape and size of the slices can be ensured to meet the requirements.

[0070] Step S8, quality selection, comparing the cut tobacco leaf parts with the pre-established chemical composition model and moisture characteristic model to evaluate the quality of the cut tobacco leaf parts and select high-quality tobacco leaf parts suitable for the development of high-grade cigarettes.

[0071] Specifically, the collected spectral information is compared and predicted with the pre-established chemical composition model and moisture characteristic model using data analysis software, thereby evaluating the quality of the cut tobacco leaf portion. The data analysis software may be, for example, MATLAB or Python.

[0072] In one embodiment of the present invention, the quality of the cut tobacco leaf parts is evaluated based on the results of the spectral and chemical composition models. The quality of the cut tobacco leaf parts can be evaluated based on the predicted sugar content, nicotine content, moisture content and other important parameters. These prediction results will help select high-quality tobacco leaf parts suitable for the development of high-grade cigarettes. By evaluating the quality of each part, the production quality can be better controlled and a consistent product can be provided.

[0073] In steps S6 to S8, combined with the robot arm positioning and cutting operations, tobacco leaves can be automatically cut and high-quality parts suitable for the development of high-grade cigarettes can be selected, thereby improving the efficiency of tobacco processing and quality control capabilities.

[0074] The high-quality automatic tobacco leaf cutting method provided by the embodiment of the present invention adopts the hyperspectral camera method in machine vision to scan the contour of the tobacco leaf, distinguish the main vein, branch veins and tobacco leaf parts of the tobacco leaf, and determine the chemical composition and moisture characteristics of each part of the tobacco leaf based on the different spectral information provided by the hyperspectral technology; then a cutting device is used, combined with the mechanical arm positioning technology, to automatically locate and cut the main vein and branch veins of the tobacco leaf, so as to separate the higher quality tobacco leaf parts, which can be used for the development of high-grade cigarettes; by adopting the hyperspectral camera method in machine vision and the automated cutting equipment, combined with precise positioning technology, it is possible to achieve fast and accurate cutting of each part of the tobacco leaf, so as to obtain high-quality tobacco leaves for the manufacture of high-grade cigarettes, which not only improves the efficiency of tobacco processing, but also can effectively control the tobacco leaf cutting quality and improve the overall quality of cigarettes; the combination of tobacco stem and tobacco leaf cutting technology with hyperspectral technology provides a more accurate and advanced technical means for cigarette production, which can further improve the quality and market competitiveness of cigarettes.

[0075] So far, various embodiments of the present disclosure have been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0076] Although some specific embodiments of the present disclosure have been described in detail by way of examples, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the present disclosure. It should be understood by those skilled in the art that the above embodiments may be modified or some technical features may be replaced by equivalents without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A high-quality tobacco leaf automatic cutting method, characterized in that: include: Data collection: Scan tobacco leaves with a hyperspectral camera system to obtain hyperspectral image data; Region extraction: using computer vision technology to locate and extract the main veins, side veins and tobacco leaves of tobacco leaves; Spectral information extraction, extracting the spectral information of each pixel from the hyperspectral image data, and performing band selection and reconstruction to obtain spectral curves within different wavelength ranges; Chemical composition analysis: Based on the extracted spectral curves, the chemical composition content of each part is predicted by chemometric methods; Moisture signature analysis, non-destructive detection and prediction of moisture content in various parts of tobacco leaves using a pre-built regression model that characterizes the relationship between spectral signals and moisture content; Robotic arm positioning: Based on the results of tobacco leaf area extraction and the pre-set cutting plan, the robot arm accurately locates the tobacco leaf part to be cut; Cutting operation: according to the positioning information of the robot arm, the cutting equipment is used to automatically cut the main vein, branch veins and high-quality parts; Quality selection, comparing the cut tobacco leaf parts with pre-established chemical composition models and moisture characteristic models to evaluate the quality of the cut tobacco leaf parts and select high-quality tobacco leaf parts suitable for high-grade cigarette development.

2. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The data acquisition, scanning the tobacco leaves by a hyperspectral camera system to obtain hyperspectral image data, specifically includes: The tobacco leaves are scanned using a professional hyperspectral camera system to provide hyperspectral image data in multiple bands. The professional hyperspectral camera system includes Specim FX series or Hyper-Cam.

3. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The region extraction uses computer vision technology to locate and extract the main vein, branch veins and tobacco leaves of the tobacco leaf, specifically including: Use shape recognition algorithms or deep learning methods to extract the main veins, side veins and tobacco leaves.

4. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The spectral information extraction extracts the spectral information of each pixel from the hyperspectral image data, and performs band selection and reconstruction to obtain spectral curves within different wavelength ranges, specifically including: Hyperspectral data processing software is used to perform band selection, reconstruction and sample extraction on the collected hyperspectral images to obtain the spectral curve of each pixel.

5. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The chemical composition analysis, based on the extracted spectral curve, predicts the chemical composition content of each part by a chemometric method, specifically includes: Based on the spectral curves, a chemometrics software package was used to apply principal component analysis or partial least squares method to model and analyze the quantitative relationship between the spectrum and chemical composition.

6. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The moisture characteristic analysis uses a pre-built regression model to perform non-destructive detection and prediction of the moisture content of each part of the tobacco leaf, specifically including: According to the correlation between the spectral curve and the moisture content, a regression model was constructed using a machine learning algorithm.

7. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The robot arm positioning, based on the tobacco leaf area extraction result and the pre-set cutting plan, accurately positions the tobacco leaf part to be cut by the robot arm, specifically including: According to the results of tobacco leaf area extraction, a precision visual navigation system is used to achieve high-precision tobacco leaf positioning through robotic arm positioning technology.

8. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The cutting operation, based on the positioning information of the robot arm, uses the cutting device to automatically cut the main vein, branch veins and high-quality parts, specifically includes: According to the positioning information of the robotic arm, select professional equipment suitable for tobacco leaf cutting to plan the cutting path, and perform automated cutting operations through programming and control of the equipment.

9. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: The quality selection compares the cut tobacco leaf parts with the pre-established chemical composition model and moisture characteristic model to evaluate the quality of the cut tobacco leaf parts and select high-quality tobacco leaf parts suitable for the development of high-grade cigarettes, specifically including: Using data analysis software, the collected spectral information is compared and predicted with the pre-established chemical composition model and moisture characteristic model, thereby evaluating the quality of the cut tobacco leaf part.

10. The high-quality tobacco leaf automatic cutting method according to claim 1, characterized in that: After the data collection and before the area extraction, the high-quality tobacco leaf automatic cutting method further includes: Image preprocessing, preprocessing the collected hyperspectral image, the preprocessing includes: performing at least one operation of spatial correction, background elimination, noise filtering and color calibration on the collected hyperspectral image.

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