Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods
Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior qualit...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2023.1073346/full |
| _version_ | 1811158357849931776 |
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| author | Mingzheng Zhang Mingzheng Zhang Tian’en Chen Tian’en Chen Tian’en Chen Xiaohe Gu Xiaohe Gu Dong Chen Dong Chen Dong Chen Cong Wang Cong Wang Cong Wang Wenbiao Wu Wenbiao Wu Wenbiao Wu Qingzhen Zhu Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao |
| author_facet | Mingzheng Zhang Mingzheng Zhang Tian’en Chen Tian’en Chen Tian’en Chen Xiaohe Gu Xiaohe Gu Dong Chen Dong Chen Dong Chen Cong Wang Cong Wang Cong Wang Wenbiao Wu Wenbiao Wu Wenbiao Wu Qingzhen Zhu Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao |
| author_sort | Mingzheng Zhang |
| collection | DOAJ |
| description | Tobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works. |
| first_indexed | 2024-04-10T05:23:16Z |
| format | Article |
| id | doaj.art-1fdcc2919fd446f5ac43f494087a03f3 |
| institution | Directory Open Access Journal |
| issn | 1664-462X |
| language | English |
| last_indexed | 2024-04-10T05:23:16Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj.art-1fdcc2919fd446f5ac43f494087a03f32023-03-08T05:29:40ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2023-03-011410.3389/fpls.2023.10733461073346Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methodsMingzheng Zhang0Mingzheng Zhang1Tian’en Chen2Tian’en Chen3Tian’en Chen4Xiaohe Gu5Xiaohe Gu6Dong Chen7Dong Chen8Dong Chen9Cong Wang10Cong Wang11Cong Wang12Wenbiao Wu13Wenbiao Wu14Wenbiao Wu15Qingzhen Zhu16Chunjiang Zhao17Chunjiang Zhao18Chunjiang Zhao19Chunjiang Zhao20School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaSchool of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, ChinaTechnology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, ChinaInformation Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, ChinaResearch Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, ChinaTobacco is an important economic crop and the main raw material of cigarette products. Nowadays, with the increasing consumer demand for high-quality cigarettes, the requirements for their main raw materials are also varying. In general, tobacco quality is primarily determined by the exterior quality, inherent quality, chemical compositions, and physical properties. All these aspects are formed during the growing season and are vulnerable to many environmental factors, such as climate, geography, irrigation, fertilization, diseases and pests, etc. Therefore, there is a great demand for tobacco growth monitoring and near real-time quality evaluation. Herein, hyperspectral remote sensing (HRS) is increasingly being considered as a cost-effective alternative to traditional destructive field sampling methods and laboratory trials to determine various agronomic parameters of tobacco with the assistance of diverse hyperspectral vegetation indices and machine learning algorithms. In light of this, we conduct a comprehensive review of the HRS applications in tobacco production management. In this review, we briefly sketch the principles of HRS and commonly used data acquisition system platforms. We detail the specific applications and methodologies for tobacco quality estimation, yield prediction, and stress detection. Finally, we discuss the major challenges and future opportunities for potential application prospects. We hope that this review could provide interested researchers, practitioners, or readers with a basic understanding of current HRS applications in tobacco production management, and give some guidelines for practical works.https://www.frontiersin.org/articles/10.3389/fpls.2023.1073346/fulltobaccohyperspectral remote sensingquality estimationyield predictionstress detectionvegetation index |
| spellingShingle | Mingzheng Zhang Mingzheng Zhang Tian’en Chen Tian’en Chen Tian’en Chen Xiaohe Gu Xiaohe Gu Dong Chen Dong Chen Dong Chen Cong Wang Cong Wang Cong Wang Wenbiao Wu Wenbiao Wu Wenbiao Wu Qingzhen Zhu Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao Chunjiang Zhao Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods Frontiers in Plant Science tobacco hyperspectral remote sensing quality estimation yield prediction stress detection vegetation index |
| title | Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods |
| title_full | Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods |
| title_fullStr | Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods |
| title_full_unstemmed | Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods |
| title_short | Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods |
| title_sort | hyperspectral remote sensing for tobacco quality estimation yield prediction and stress detection a review of applications and methods |
| topic | tobacco hyperspectral remote sensing quality estimation yield prediction stress detection vegetation index |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2023.1073346/full |
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