Automated tobacco leaf grading using visible and near-infrared spectral images

Abstract

The grading of tobacco leaves is a pivotal step in the tobacco production pipeline. Traditionally, this grading has been performed manually by seasoned experts, a practice both time-intensive and reliant on subjective assessments. In recent years, advancements have been made towards automating this procedure through the analysis of visible light images of the leaves. However, due to the high visual similarity among visible light images at different grades, a single visible light image is insufficient for achieving accurate grading. It is known that chemical composition serves as a pivotal metric for evaluating tobacco leaf quality, where the near-infrared spectral image of tobacco leaves probably harbors valuable information pertinent to grading. Inspired by this, we propose an end-to-end multispectral feature enhancement network for automated tobacco leaf grading. In addition to utilizing common visible light images, the network integrates a principal component from the near-infrared spectral image to encapsulate chemical information. Detailed experimental results demonstrate that our method achieves a high grading accuracy of 91.94% with the inclusion of near-infrared spectral images, surpassing the performance of existing methods reliant only on visible light images. The network showcases high accuracy while maintaining swift inference speed, offering innovative insights for the future design of automated tobacco leaf grading systems.