Potato late blight severity monitoring based on the relief-mRmR algorithm with dual-drone cooperation

Abstract

Late Blight in Potato, a prevalent potato disease, significantly impacts potato yield and stands as one of the principal afflictions affecting the potato crop. Timely and effective monitoring of late blight severity in potato and its spatial distribution has become an immediate priority. In this study, we employed a dual-drone collaborative approach, utilizing the multispectral drone from DJI as well as an ultra-high-resolution RGB drone. By integrating the vegetation index derived from the multispectral UAV, the texture index from the RGB drone, and the estimated crown coverage feature, we combined the relief-mRmR technique with machine learning modeling algorithms to monitor late blight in potato. We compared and constructed severity monitoring models for late blight in potato using Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbors (KNN) classification algorithms. The results indicate that the RF model exhibited the highest accuracy. For the training dataset, the overall accuracy (OA) and kappa coefficient were 92.50% and 0.90, respectively, while for the independent validation dataset, the OA and kappa coefficient reached 97.50% and 0.96, respectively. The findings also demonstrate that augmenting the vegetation index and texture index with the estimated high-resolution plant crown coverage information significantly enhances the accuracy of the late blight in potato monitoring model.