Prediction of Rice Brown Spot Disease Using Spectral Indices Derived from UAVs and Machine Learning Models in Lambayeque and Cajamarca, Peru

Rice brown spot, caused by Bipolaris oryzae, is an important constraint for rice production and requires timely field-scale monitoring. This study evaluated the use of multispectral bands acquired with a UAV-mounted sensor, together with vegetation indices, combined with machine-learning models to estimate rice brown spot severity under field conditions in Lambayeque and Cajamarca, Peru. A total of 37 sampling observations were collected across the vegetative, flowering, and milk-ripening stages. Spectral variables were extracted from UAV orthomosaics and related to field-based disease severity assessments. The strongest correlations with severity were observed for NDRE (r = −0.83) and NPCI (r = 0.77). Three regression models were evaluated using leave-one-out cross-validation (LOOCV): support vector regression with radial basis function kernel (SVR-rbf), support vector regression with linear kernel (SVR-linear), and Random Forest (RF). The SVR-linear model showed the lowest prediction error using NDRE, GREEN, and BLUE as predictors (R2_CV = 0.76; RMSE_CV = 1.31), although its performance was very similar to that of SVR-rbf and RF. These results indicate that UAV-derived multispectral information can support plot-level estimation of rice brown spot severity. However, model performance should be interpreted cautiously because of the small dataset, heterogeneous disease conditions, and moderate prediction accuracy. Further studies with larger and independent datasets are needed to improve robustness and transferability.