Early detection of rice bacterial leaf blight disease using canopy hyperspectral data and machine learning

Bacterial leaf blight (BLB) remains a major biotic constraint to global rice production, requiring faster and scalable detection methods than conventional field scouting to minimise yield losses. Early detection of BLB is essential for timely intervention and yield protection, yet very few studies have examined the use of remote sensing, particularly during the early growth phase of the rice crop. This study evaluated the potential of canopy-level hyperspectral data for identifying BLB in early vegetative phase. Field trials were conducted at the International Rice Research Institute, where canopy hyperspectral reflectance was collected from healthy and inoculated plots at the stem elongation and booting stages of the rice crop. Biophysical and biochemical parameters were also collected during the stem elongation stage. Four spectral transformation techniques, including first and second derivatives (FD, SD), continuum removal (CR), and continuous wavelet transform (CWT), were applied to canopy spectral data to identify BLB-sensitive features. To reduce the high dimensionality of hyperspectral features, the top 5 % most separable features were extracted, and redundancies were removed using hierarchical clustering combined with variance inflation factor filtering. Support vector machines (SVM), random forests (RF), and XGBoost classifiers were trained, and their performance on BLB detection was evaluated. Statistical tests revealed that transformed spectra provided greater class separability than original reflectance data, particularly in identifying bands within the near-infrared (NIR, 710 nm to 1333 nm) and shortwave infrared (SWIR, 1451 nm to 2344 nm) regions. The SVM model using 20 selected features achieved the highest F1-score on an independent test set (73 %), outperforming RF and XGBoost (67 % and 68 %, respectively), and yielded a recall/sensitivity of 98 % for BLB identification. Notably, the first-derivative feature at 1076 nm was the most informative predictor for BLB detection, as determined by the separability test and absolute SVM coefficient. Most of the identified sensitive wavelengths correspond to changes in chlorophyll, leaf area index (LAI), and fresh biomass. Using a controlled experiment, this proof-of-concept study demonstrates the potential of canopy hyperspectral data, combined with spectral transformations and machine learning algorithms, to support early BLB detection and provides a methodological basis for future validation in complex regional-scale environments.