Quality prediction of air-cured cigar tobacco leaf using region-based neural networks combined with visible and near-infrared hyperspectral imaging.

ABSTRACT: Visible and Near-infrared hyperspectral imaging (VNIR-HSI) combined with machine learning has shown its effectiveness in various detection applications. Specifically, the quality of cigar tobacco leaves undergoes subtle changes due to environmental differences during the air-curing phase. This study aims to evaluate the feasibility of deep learning methods in overcoming data limitations to develop a VNIR-HSI prediction model for the quality of cigar tobacco leaves at different air-curing levels. The moisture, chlorophyll, total nitrogen, and total sugar content in cigar tobacco leaves were predicted across various air-curing stages and light conditions. Results showed that the Diversified Region-based Convolutional Neural Network (DR-CNN) achieved the best performance, with a root mean squ

SUBMITTER: Yin J 

PROVIDER: S-EPMC11682218 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature