Estimating Anthracnose Damage by Colletotrichum theae-sinensis (Miyake) Yamamoto in Tea Fields Using Aerial Image Data from Multirotor-type UAV (Drone)

Abstract

We estimated the densities of Anthracnose damaged leaves by Colletotrichum theae-sinensis (Miyake) Yamamoto using aerial image data of tea fields, taken by a multirotor-type UAV (Drone) . We used DJI Phantom 4 for aerial photography and the commercial photo editing software, Adobe photoshop element 13, to analyze the image data. The aerial photographs of the tea field where Anthracnose occurred after autumn skiffing clearly showed the points where the disease severely occurred. We analyzed the correlation between the densities of diseased leaves and the values of several arithmetic expressions combining mean values of three primary colors (RGB), luminescence (Y), and normalized RGB (NR, NG, and NB) extracted from the image data. There was a significantly high correlation (absolute value of r > 0.75) between the eight types of calculated values, NR, (R+G)/G, R/Y, G-R, NG-NR, (G-R)/Y, (G-R)/(G+R), and G/R, and the densities of diseased leaves. Furthermore, at other field points, we verified the suitability of the eight linear regression equations obtained from the relationship between the calculated values and the densities of diseased leaves. Our results suggest the potential use of drones to collect aerial image data in tea fields for estimating the densities of Anthracnose damaged leaves.