Integration of UAV Remote Sensing and Machine Learning for Taro Blight Monitoring

Taro blight is a major disease affecting taro cultivation. Traditional methods for disease prevention rely on manual identification, which is limited by subjectivity and scope. An unmanned aerial vehicle (UAV) was utilized to capture spectral images of natural taro fields, facilitating the efficient monitoring of taro blight. Field survey data were integrated with these images to develop a model for monitoring taro blight severity. The back propagation neural network (BPNN) model showed optimal performance during the early and middle stages of taro formation when hyperspectral parameters were used as input variables. In the early stage, the BPNN model achieved a coefficient of determination (R2) of 0.92 and an RMSE of 0.054 on the training set, and it obtained an R2 of 0.89 with a root mean square error (RMSE) of 0.074 on the validation set. The random forest regression (RFR) model performed best during the early stage of taro formation with multispectral vegetation indices as input variables. The models exhibited robust predictive capabilities across various stages, especially during the early stage of taro formation. The results demonstrate that UAV remote sensing, combined with characteristic parameters and disease indices, presents a precise taro blight monitoring method that can substantially improve disease management in taro cultivation.