Automated Vision-Based Diagnosis of Cassava Mosaic Disease

Abstract. Cassava Mosaic Disease (CMD) has been an increasing concern to all countries in sub-Saharan Africa that depend on cassava for both commercial and local consumption. Information about the countrywide spread of this disease is difficult to obtain due to logistics and human resource issues in these countries. The objective of this study was to assess the feasibility of automated computer vision based diagnosis of CMD. Images of healthy and CMD-infected cassava leaves were taken at Namulonge Crop Resources Research Institute, Uganda. We performed classification on these images based on shape and colour features, using a set of standard classification methods (na¨ıve Bayes, two-layer MLP networks, support vector machines, k-nearest neighbour and divergencebased learning vector quantization). We find near-perfect classification to be attainable for leaf images captured under ideal conditions, and outline a method for performing this classification on natural, cluttered image...

• Type: Conference
• Year: 2010
• Where: INCDM
• Authors: Jennifer R. Aduwo, Ernest Mwebaze, John A. Quinn
• Keywords: Cassava Mosaic Disease, CMD-infected Cassava Leaves, Data Mining, INCDM 2010, Two-layer Mlp Networks