Digital Disease Phenotyping

Abstract

Precise and rapid methods of plant disease detection and evaluation are key factors to accelerate resistant variety development in the rice breeding program. Conventional methods for the disease detection and evaluation is mainly carried out using standard visual estimation by trained experts which is slow and prone to high level of subjectivity. Rigorous research has recently recognized innovative, sensor-based methods for the detection and evaluation of plant diseases. Among different type of sensors, aerial multispectral imaging provides a fast and nondestructive way of scanning plants in diseased regions and has been used by various researchers to classify symptom levels on the spectral profile of a plant. In this paper, we developed machine learning models to classify rice breeding lines infected by rice Hoja Blanca virus (RHBV) using multispectral images collected from UAV (unmanned aerial vehicle). Our results revealed that, the Support Vector Machine (SVM) and Random Forest (RF) methods were not significantly different in their ability to separate susceptible from non-susceptible classes, but SVM best classifiers showed a better sensitivity rates 0.74 (SVM) versus 0.71 (RF). The tool developed from this study will allow rice breeders to characterize Hoja Blanca virus resistant varieties considerably earlier, and subsequent in reduced costs.