Improved Enset Disease Detection with Image Processing and Convolutional neural network

Abstract

Enset (Ensete ventricosum Cheesman) is a perennial and herbaceous plant which widely distributed and cultivated in southern and southwestern Ethiopia as staple food for more than 20 million people in mixed subsistence farming systems. There are several diseases which tries to decline the yield with quality. However bacterial wilt and mealybug diseases of Enset are widespread in the country‟s major Enset growing regions causing losses of up to 100% destruction of farm fields in extreme cases. This paper looks into the use of deep learning to detect bacterial wilt disease and Enset mealybug, where data is obtained in small amounts and collected under minimally controlled conditions. Data augmentation is employed to get over the limits of the dataset size