Remote Sensing, Vol. 10, Pages 917: Supervised Classification of RGB Aerial Imagery to Evaluate the Impact of a Root Rot Disease (Remote Sensing)

Aerial imaging provides a landscape view of crop fields that can be utilized to monitor plant diseases. Phymatotrichopsis root rot (PRR) is a serious root rot disease affecting several dicotyledonous hosts, including the perennial forage crop alfalfa. PRR disease causes stand loss by spreading as circular to irregular diseased areas that increase over time, but disease progression in alfalfa fields is poorly understood. The objectives of this study were to develop a workflow to produce PRR disease maps from sets of high-resolution red, green and blue (RGB) images acquired from two different platforms and to assess the feasibility of using these PRR disease maps to monitor disease progression in alfalfa fields. Aerial RGB images, two from unmanned aircraft systems (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014–2015 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classification of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform-specific and manned-aircraft platform-specific. Our results showed that the UAS-platform-specific spectral signature was most efficient for classifying images acquired with the UAS, with accuracy ranging from 90 to 96%. In contrast, manned-aircraft-acquired images classified using image-specific spectral signatures yielded 95 to 100% accuracy. The effect of hue, saturation and value color space transformations (HSV and Hrot60SV) on classification accuracy was determined, but the accuracy estimates showed no improvement in their efficiency compared to the RGB color space. Finally, the data showed that the classification of the bare ground increased by 74% during the study period, indicating the extent of alfalfa stand loss caused by PRR disease. Thus, this study showed the utility of high-resolution RGB aerial images for monitoring PRR disease spread in alfalfa.