The Application of Hyperspectral Images in the Classification of Fresh Leaves’ Maturity for Flue-Curing Tobacco

Abstract

The maturity of tobacco leaves directly affects their curing quality. However, no effective method has been developed for determining their maturity during production. Assessment of tobacco maturity for flue curing has long depended on production experience, leading to considerable variation. In this study, hyperspectral imaging combined with a novel algorithm was used to develop a classification model that could accurately determine the maturity of tobacco leaves. First, tobacco leaves of different maturity levels (unripe, under-ripe, ripe, and over-ripe) were collected. ENVI software was used to remove the hyperspectral imaging (HSI) background, and 11 groups of filtered images were obtained using Python 3.7. Finally, a full-band-based partial least-squares discriminant analysis (PLS-DA) classification model was established to identify the maturity of the tobacco leaves. In the calibration set, the model accuracy of the original spectrum was 88.57%, and the accuracy of the de-trending, multiple scattering correction (MSC), and standard normalization variable (SNV) treatments was 91.89%, 95.27%, and 92.57%, respectively. In the prediction set, the model accuracy of the de-trending, MSC, and SNV treatments was 93.85%, 96.92%, and 93.85%, respectively. The experimental results indicate that a higher model accuracy was obtained with the filtered images than with the original spectrum. Because of the higher accuracy, de-trending, MSC, and SNV treatments were selected as the candidate characteristic spectral bands, and a successive projection algorithm (SPA), competitive adaptive reweighted sampling (CASR), and particle swarm optimization (PSO) were used as the screening methods. Finally, a genetic algorithm (GA), PLS-DA, line support vector machine (LSVM), and back-propagation neural network (BPNN) classification and discrimination models were established. The combination SNV-SPA-PLS-DA model provided the best accuracy in the calibration and prediction sets (99.32% and 98.46%, respectively). Our findings highlight the efficacy of using visible/near-infrared (ViS/NIR) hyperspectral imaging for detecting the maturity of tobacco leaves, providing a theoretical basis for improving tobacco production.

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

“…The transmission image input simulates the tactile aspect of manual grading, establishing a cross-modal enhancement network. Lu et al [12] adopt visible and near-infrared spectral images and develop a full-band-based partial least-squares discriminant analysis classification model for determining the maturity of tobacco leaves. Considering that the chemical composition typically serves as a fundamental criterion for evaluating tobacco leaf quality, and the near-infrared spectral images carry features closely related to chemical constituents [13,14].…”

“…Introducing near-infrared spectral images into the automated tobacco leaf grading systems promises a further enhancement in accuracy. However, previous efforts [12] have a limited number of classification categories. The integration of visible light and spectral information was not fully realized, leaving room for improvement in grading accuracy.…”

In-Field Tobacco Leaf Maturity Detection with an Enhanced MobileNetV1: Incorporating a Feature Pyramid Network and Attention Mechanism

“…Conversely, Chen et al [10] amalgamated near-infrared (NIR) spectroscopy with convolutional neural network (CNN) deep learning to achieve maturity recognition of the upper, middle, and lower parts of the tobacco leaves, reaching accuracies of 96.18%, 95.2%, and 97.31%, respectively. Concurrently, Lu et al [11] utilized hyperspectral imaging technology combined with the SNV-SPA-PLS-DA model, successfully increasing the accuracy of the tobacco leaf maturity classification validation set and prediction to 99.32% and 98.46%, respectively. These findings underscore the efficacy of utilizing visible light/NIR hyperspectral imaging technology to detect tobacco leaf maturity.…”