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Title: Supervised classification of RGB aerial imagery to evaluate the impact of a root rot disease

Author
item MATTUPALLI, CHAKRADHAR - Noble Research Institute
item Moffet, Corey
item SHAH, KUSHENDRA - Noble Research Institute
item YOUNG, CAROLYN - Noble Research Institute

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/6/2018
Publication Date: 6/10/2018
Citation: Mattupalli, C., Moffet, C., Shah, K., Young, C. 2018. Supervised classification of RGB aerial imagery to evaluate the impact of a root rot disease. Remote Sensing. 10(6):917. https://doi.org/10.3390/rs10060917.
DOI: https://doi.org/10.3390/rs10060917

Interpretive Summary: Root rot disease severely limits alfalfa production in southern Oklahoma and north Texas. Alfalfa fields infested with the fungi causing the root rot will often have reduced stand life and productivity. The extent of disease spread occurring in a growing season greatly affects alfalfa stand longevity, but is little understood. Through this study we provided a framework for obtaining high-resolution aerial images from either an unmanned aerial vehicle or manned aircraft platforms and the subsequent workflow to reduce the extent of PRR disease spread. Understanding the loss of alfalfa stand area reported from aerial images could help a producer make informed management choices such as replanting or site-specific fungicide applications to slow down the disease spread.

Technical Abstract: 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 in 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 system (UAS) and four images from a manned aircraft platform were acquired at different time points during the 2014-15 growing seasons from a center-pivot irrigated, PRR-infested alfalfa field near Burneyville, OK. Supervised classifications of images acquired from both platforms were performed using three spectral signatures: image-specific, UAS-platform specific and manned aircraft 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%. Likewise, 95 to 100% accuracy was obtained classifying manned aircraft acquired images using image-specific spectral signatures. 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.