Optical imaging method for determining symptoms severity of cassava mosaic disease

Abstract

Cassava mosaic disease (CMD) is a major constraint to cassava production in cassava growing regions. Severity of CMD symptoms on cassava leaves is usually assessed visually using an arbitrary scale, which is semi-qualitative, and does not represent the actual surface area of diseased leaf. The objective of this study was to develop a quantitative method of assessing the severity of CMD. A combination of polarimeteric digital colour images, L*a*b* colour model and K-means clustering algorithm were used to determine the areas of CMD symptoms and healthy areas on leaves. The severity of CMD on a leaf is determined by computing the percentage of the CMD symptomatic area to the total leaf area. The analysis provides relatively fast and accurate classification of Cassava mosaic diseased leaves. The proposed method will enable plant scientists to obtain accurate and reliable data, forming the basis for better decision making