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.

Technological Innovations for Improving Cassava Production in Sub-Saharan Africa

“…Image analysis has similarly been used ( Garcia-Oliveira et al, 2020 ; Nakatumba-Nabende et al, 2020 ). Analysis of field images combined with various algorithms, including K mean clustering algorithms ( Anderson et al, 2015 ), artificial neutral network ( Abdullakasim et al, 2011 ), and more recently, machine learning techniques and convolutional neutral network ( Owomugisha and Mwebaze, 2016 ; Sambasivam and Opiyo, 2020 ) have provided a more accurate and objective assessment of disease severity and incidence. The smart phone-based diagnostic system (NURU-AI) is being developed to support remote diagnosis by smallholder farmers in Africa for real-time prediction of the state of cassava health ( Owomugisha and Mwebaze, 2016 ; Ramcharan et al, 2017 , 2019 ).…”