Detecting Weligama Coconut Leaf Wilt Disease in Coconut Using UAV-Based Multispectral Imaging and Object-Based Classification

Weligama Coconut Leaf Wilt Disease (WCLWD), a major threat to the coconut industry in Sri Lanka, has resulted in large economic losses (reduced productivity and high mortality rate) among infected palm. Early diagnosis is challenging and unreliable due to the low sensitivity of conventional disease detection methods like visual inspections and laboratory testing. In order to overcome these constraints, this study used object-based image analysis (OBIA) in combined with multispectral imaging using an unmanned aerial vehicle (UAV) to identify and categorize WCLWD in coconut palms. To differentiate between healthy and infected trees, Support Vector Machine (SVM) classification was used to analyze UAV images taken in five spectral bands: red, green, blue, red edge, and near infrared. The four band combination of 'blue', 'green', 'red-edge' and 'near infrared' was found to be the best of those tested, with an accuracy of 79.25% and a moderate agreement, based on the kappa coefficient of 0.493. The accuracy of this was then validated against a field survey ground truth data. Results show that overland biomass detection using OBIA methods with UAV multispectral imaging offers a feasible means to identify WCLWD, but that further classifier work and extra sources of data can improve accuracy. Results show the possibility of advanced remote sensing technologies for improve the detection of coconut WCLWD and support for managing the spread of disease in coconut plantations.