Tobacco Leaves Maturity Classification Based on Deep Learning and Proximal Hyperspectral Imaging

Accurately measuring tobacco harvest maturity is crucial for optimizing quality and crop management. Traditional methods heavily rely on qualitative evaluation and chemical experiments, which introduce subjectivity and inherent limitations. This study proposes a novel method that combines deep learning with proximal hyperspectral imaging (HSI) to achieve precise recognition of tobacco leaf maturity. Unlike traditional techniques, HSI captures rich spectral and spatial data, enabling comprehensive analysis. It adopts a pixel-level annotation strategy to annotate each pixel based on its maturity level, thereby preserving spectral complexity. The dataset comprises 3000 randomly extracted cubes (5×5×176) from 150 original hyperspectral images, encompassing five maturity levels. Spectral and spatial features are extracted from hyperspectral data using a three-dimensional convolutional neural network (3D-CNN) architecture. This method effectively leverages complex spectral patterns for maturity recognition. During testing, the model demonstrated an impressive average accuracy of 99.93%. Visual predictions vividly illustrate the model's proficiency in maturity recognition, affirming its practical utility. This study pioneers the integration of deep learning and hyperspectral near-end sensing technology in tobacco maturity assessment, mitigating the constraints of traditional methods, and establishing the groundwork for real-time monitoring and quality control of tobacco.