Tobacco Leaves Maturity Classification Based on Deep Learning and Proximal Hyperspectral Imaging
Author:
Affiliation:
1. School of Mechanical Engineering, Guizhou University, Guiyang, China
2. Guizhou Academy of Tobacco Science, Guiyang, China
Funder
Guizhou Province Science and Technology Innovation Base Construction Project
Application Basic Research Program of Guizhou Academy of Tobacco Science
Guizhou Provincial Key Technology R&D Program
Publisher
Informa UK Limited
Subject
Electrochemistry,Biochemistry (medical),Clinical Biochemistry,Spectroscopy,Biochemistry,Analytical Chemistry
Link
https://www.tandfonline.com/doi/pdf/10.1080/00032719.2023.2284834
Reference34 articles.
1. Identification and diagnosis of whole body and fragments of Trogoderma granarium and Trogoderma variabile using visible near infrared hyperspectral imaging technique coupled with deep learning
2. Spectral signatures in the UV range can be combined with secondary plant metabolites by deep learning to characterize barley–powdery mildew interaction
3. Sugariness prediction of Syzygium samarangense using convolutional learning of hyperspectral images
4. Discrimination of Fresh Tobacco Leaves with Different Maturity Levels by Near-Infrared (NIR) Spectroscopy and Deep Learning
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