Identification of tobacco leaf diseases using hyperspectral imaging and machine learning with SHAP interpretability analysis.

Abstract

Tobacco leaf diseases significantly affect yield and quality, underscoring the need for rapid and non-destructive diagnostic tools. Although hyperspectral imaging (HSI) has been applied in tobacco pathology, most existing studies focus on single diseases and lack generalized, interpretable frameworks for multi-class identification. In this study, hyperspectral images of healthy leaves and four major diseases-brown spot, wildfire, Tobacco Mosaic Virus (TMV), and Potato virus Y (PVY)-were collected to construct a balanced, leaf-independent dataset. Pixels were grouped by leaf ID, and the entire dataset was strictly partitioned at the leaf level to prevent pixel-level data leakage and ensure generalization to unseen leaves. Multiple preprocessing techniques, wavelength-selection methods, and machine-learning classifiers were systematically compared. A compact ANN model integrating Savitzky-Golay preprocessing and SPA-based wavelength selection achieved the best overall performance while requiring only a small number of informative wavelengths. A Transformer model provided slightly stronger predictive capacity but depended on full-spectrum inputs and substantially higher computational cost. Pixel-level predictions enabled lesion-area-based severity estimation for the two leaf-spot diseases. SHAP analysis highlighted physiologically meaningful spectral regions associated with pigment absorption and structural variation. Overall, this study presents an efficient and interpretable HSI framework for multi-disease tobacco diagnosis, supporting the development of practical hyperspectral or multispectral systems.

Plant phenotyping relevance

タバコ葉の病徴をハイパースペクトル画像と機械学習で識別し、病斑面積に基づく重症度推定まで行う診断フレームワークの開発が中心である。

Code and data availability

The supplied blocks contain no data availability statement or public deposit of the paper's hyperspectral tobacco dataset, images, code, or trained models. The only URL mentioned is the generic x-transformers library, a cited third-party dependency, not a paper-specific asset.

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