Hyperspectral remote sensing for tobacco quality estimation, yield prediction, and stress detection: A review of applications and methods

Frontiers in Plant Science

Plant Science

Xiaohe Gu¹  Qingzhen Zhu²  Mingzheng Zhang³  Chunjiang Zhao⁴  Cong Wang⁵  Dong Chen⁵  Tian’en Chen⁵  Wenbiao Wu⁵ 

[1] Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China;Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China;School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China;School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China;Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China;School of Agricultural Engineering, Jiangsu University, Zhenjiang, Jiangsu, China;Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China;Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China;Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China;Technology Center, Nongxin Smart Agricultural Research Institute, Nanjing, Jiangsu, China;Information Engineering Department, National Engineering Research Center for Information Technology in Agriculture, Beijing, China;Research Center of Information Technology, Beijing Academy of Agriculture and Forestry Science, Beijing, China;

关键词: tobacco;    hyperspectral remote sensing;    quality estimation;    yield prediction;    stress detection;    vegetation index;    machine learning;   

DOI  :  10.3389/fpls.2023.1073346

 received in 2022-10-18, accepted in 2023-02-21,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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Copyright © 2023 Zhang, Chen, Gu, Chen, Wang, Wu, Zhu and Zhao