Authors:
Razib Iqbal
;
Kyle Sargent
and
Laszlo Kovacs
Affiliation:
College of Natural and Applied Sciences, Missouri State University, Springfield, MO, U.S.A.
Keyword(s):
Background Removal, Downy Mildew, Grape Leaf, HSV Masking, Image Analysis.
Abstract:
Downy and powdery mildews are the most serious diseases of the grapevine. A sustainable way to control these pathogens is the breeding and deployment of resistant grape cultivars. For breeding efforts to be effective, accurate quantification of the resistance phenotype is essential. In this paper, we present a computer-based image recognition, processing, and analysis technique for enhancing the detection and quantification of Plasmopara viticola and Erysiphe necator the causal agents of downy and powdery mildew, respectively. We propose a multi-step approach that utilizes background removal and Hue-Saturation-Value (HSV) masking as opposed to multi-faceted color channel breakdowns, photo texture evaluations, or classification-based algorithms for the detection of mildew. Our experimental results show that our method provides reliable results and fast performance.