Identification of the onset of disease within a potato crop using a UAV equipped with un-modified and modified commercial off-the-shelf digital cameras

This paper investigates the effectiveness of using a UAV with dual commercial off-the-shelf (COTS) cameras, one un-modified and one modified to sense near infra-red (NIR) wavelengths to identify the onset of disease within a trial crop of potatoes. The trial was composed of 2 plots of 16 drills containing 12 tubers exposed to the blackleg disease-causing bacterial pathogen (Pectobacterium atrosepticum) in order to demonstrate best practise tuber storage and haulm destruction methods. Eleven sets of aerial data were gathered between 27/5/2016~29/7/2016 and compared with ground truth data collected on 14/7/2016. Visual analysis of the data could only detect the onset of disease and not the specific infection and resulted in a user accuracy (UA) of 83% and producer accuracy (PA) of 78%, with a total accuracy (TA) of 91% and Kappa coefficient (K) of 0.75. The building blocks of an automated classification routine have been constructed using pixel and object based image analysis (OBIA) methods, which have shown promising first results (UA 65%, PA 73%, TA 87%, K 0.61) but requires further refinement to achieve an equivalent level of accuracy as that of the visual analysis.