Proximal hyperspectral imaging for early detection and disease development prediction of Septoria Leaf Blotch in wheat using spectral-temporal features

This study explores the potential of hyperspectral imaging (HSI) combined with advanced machine learning for early detection of Septoria Leaf Blotch (SLB) in wheat, employing LeafSpec (Wheat Version), a custom-developed handheld hyperspectral scanner optimized for this purpose. Utilizing a temporal-spectral modelling approach with NDVI heatmaps and PCA for disease visualization, the research analyses HSI data from two rounds of experiments, wheat samples across four treatment groups with images collected at different time points from 3 to 19 days after inoculation (DAI) with 1280 images collected in total. The models, developed using Partial Least Squares Regression (PLSR) and Partial Least Squares Discriminant Analysis (PLS-DA), were tested against average spectra from 3 to 17 DAI. Results indicate that the disease can be detected seven days earlier and before visual symptoms appearance estimated by human observation, with the PLS-DA model achieving 96.97 % overall accuracy in temporal classification. Furthermore, images from 12 DAI predict disease progression with an R2 value of approximately 0.7. These findings demonstrate the potential of HSI combined with machine learning to significantly advance early diagnosis and treatment strategies for SLB, suggesting that similar approaches may be beneficial for other crop diseases.