Identification of symptoms related to potato Verticillium wilt from UAV-based multispectral imagery using an ensemble of gradient boosting machines

Potato Verticillium wilt is a harmful and widespread disease which causes losses between 20% and 50% of crop production in Colombia. Diagnostics of Verticillium wilt in potato crops is challenging due that the known symptoms are often associated with other diseases. While recent studies have explored the potential of high spatial resolution imagery for detection of diseases and physiological disorders in potato crops, results are notconclusive yet. Thus, to contribute to improving identification of Verticillium wilt symptoms, we evaluate a machine learning method to classify disease severity from multidate UAV-based multispectral images. It uses an ensemble of gradient boosting machines (GBMs) to differentiate, at grid level, six levels of Verticillim wilt disease severity. The proposed method was tested in a commercial potato crop in Cundinamarca, one of the top potato production regions in Colombia, using multispectral images acquired in four different field campaigns conducted between 99 and 141 days after planting. Accuracy assessment of results indicates that classification of severity levels of Verticillium wilt infection was satisfactory with F1 scores of 82% for the training dataset and 84% for the testing dataset. This study results shows both potential and challenges of using machine learning ensemble methods for identification of Verticillium wilt symptoms in potato crops from high spatial resolution multispectral images.