Research on tobacco leaf mildew detection method based on multichannel imaging system

Tobacco leaf mildew is a critical issue affecting the quality of tobacco during storage and transportation. Traditional detection methods rely on manual inspection, which suffers from low efficiency and strong subjectivity. This study proposes a non-destructive detection method for tobacco leaf mildew based on multi-channel imaging technology. By constructing a multi-channel imaging system, data from healthy and mildewed tobacco leaves were collected. Data quality was enhanced using preprocessing methods such as Savitzky-Golay (SG) smoothing filter ,Standard Normal Variate (SNV) transformation, and Multiplicative Scatter Correction(MSC). Key spectral features were extracted, and a mildew discrimination model was established. Experimental results demonstrated that the PCA-RF detection model, based on Principal Component Analysis (PCA) and Random Forest (RF), achieved an accuracy rate of over 96%, significantly outperforming traditional manual inspection methods. This approach provides a reliable technical means for early mildew detection during tobacco storage, offering significant value for improving quality control in the tobacco industry.