Non-destructive Prediction of Nicotine Content in Tobacco Using Hyperspectral Image–Derived Spectra and Machine Learning-Reference-Cited by-同舟云学术

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Non-destructive Prediction of Nicotine Content in Tobacco Using Hyperspectral Image–Derived Spectra and Machine Learning

Published:2022-03-18 Issue:2 Volume:47 Page:106-117
ISSN:1738-1266
Container-title:Journal of Biosystems Engineering
language:en
Short-container-title:J. Biosyst. Eng.

Author:

Divyanth L. G.,Chakraborty Somsubhra,Li Bin,Weindorf David C.,Deb Prithwiraj,Gem Carol Jacob

Funder

ITC Ltd

Publisher

Springer Science and Business Media LLC

Subject

Computer Science Applications,Mechanical Engineering,Engineering (miscellaneous),Agricultural and Biological Sciences (miscellaneous)

Link

https://link.springer.com/content/pdf/10.1007/s42853-022-00134-0.pdf

Reference52 articles.

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2. Barbedo, J. G. A., Guarienti, E. M., & Tibola, C. S. (2018). Detection of sprout damage in wheat kernels using NIR hyperspectral imaging. Biosystems Engineering, 175, 124–132. https://doi.org/10.1016/j.biosystemseng.2018.09.012

3. Barra, I., Haefele, S. M., Sakrabani, R., & Kebede, F. (2021). Soil spectroscopy with the use of chemometrics, machine learning and pre-processing techniques in soil diagnosis: Recent advances–A review. Trends in Analytical Chemistry, 135, 116166. https://doi.org/10.1016/j.trac.2020.116166

4. Benelli, A., Cevoli, C., Fabbri, A., & Ragni, L. (2021). In press. Ripeness evaluation of kiwifruit by hyperspectral imaging. https://doi.org/10.1016/j.biosystemseng.2021.08.009

5. Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324

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