Discriminant analysis to detect fire blight infection on pear trees using RGB imagery obtained by a rotary wing drone

Fire-blight disease is a kind of contagious disease affecting apples, pears, and some other members of the family Rosaceae. Due to its extremely strong infectivity, once an orchard is confirmed to be infected, all of the orchards located within 100 m must be buried under the ground, and the sites are prohibited to cultivate any fruit trees for 5 years. In South Korea, fire-blight was confirmed for the first time in the Ansung area in 2015, and the infection is still being identified every year. Traditional approaches to detect fire-blight are expensive and require much time, additionally, also the inspectors have the potential to transmit the pathogen, Thus, it is necessary to develop a remote, unmanned monitoring system for fire-blight to prevent the spread of the disease. This study was conducted to detect fire-blight on pear trees using discriminant analysis with color information collected from a rotary-wing drone. The images of the infected trees were obtained at a pear orchard in Cheonan using an RGB camera attached to a rotary-wing drone at an altitude of 4 m, and also using a smart phone RGB camera on the ground. RGB and Lab color spaces and discriminant analysis were used to develop the image processing algorithm. As a result, the proposed method had an accuracy of approximately 75% although the system still requires many flaws to be improved.