Hyperspectral Imaging Analysis for the Early Detection of Tomato Bacterial Leaf Spot Disease

Abstract Recent advancements in hyperspectral imaging (HSI) for early disease detection have shown promising results, yet there is a lack of HSI data representing the responses of plants at different disease stages as measured by in planta pathogen density. To address this question, we used bacterial leaf spot of tomato as a model and collected hyperspectral images and in planta pathogen populations for seven days. Machine learning models were trained using data from leaf-level full spectra, vegetation index (VI), and pixel-level full spectra at four disease stages. The results suggest that HSI can detect tomato foliar disease at pre-symptomatic stages and differentiate bacterial disease spots from abiotic leaf spots. Using VI data as features for machine learning improved overall classification performance by 26-37% as compared to the direct use of raw data. Critical wavelength bands and VIs varied across disease progression stages, suggesting that pre-symptomatic disease detection relied more on changes in leaf water content and plant defense responses rather than changes in leaf pigments or internal structure, which are crucial during symptomatic stages. In conclusion, this study reveals the potential benefits of leaf structure segmentation and VI group pattern analysis in using HSI for the early detection of leaf diseases.