Coffee Disease Detection Using Convolutional Neural Network An Image Processing Approach

Coffee is one of the most important products in Ethiopia. Coffee has a great contribution in Ethiopia economy since it increases foreign currency of the country; and is the source of daily income earning for farmers. Therefore, controlling coffee diseases and ensuring quality of coffee product is the major issue for the country. Currently disease identified manually by experts and they identify by eye, so this is makes challenged and expert’s not available in everywhere in production area and other researchers don’t see Cercospora leaf spot and coffee berry disease. The aim of this research is therefore detecting common coffee diseases using digital image processing and deep learning technique. rnIn this study, we consider the most common coffee diseases such as Cercospora leaf spot, coffee phoma disease and coffee berry disease. Convolutional Neural Network has showed its efficiency and accuracy on image processing in representing images and creating patterns to identify coffee diseases. This research proposed Convolutional Neural Network technique to detect coffee leaf and coffee beans diseases. This study follows experimental research methodology. 552 coffee leaf and coffee beans images dataset captured by HD camera and Motorola Phone from popular coffee production areas of Ethiopia, such as Jimma (agaro) and Bonga (kefa) zone farm and 5334 coffee images collected from Jimma Agricultural Research Center (JARC) and Bonga Agricultural Research Center (BARC) database. We have used four-classes for classification; namely, Cercospora leaf spot, coffee phoma disease, coffee berry disease and Healthy coffee. The total number of data sets used for experimentation is 5886. From the total data sets, 80% is used for training and the remaining 20% for testing purpose. rnExperimental result shows that the proposed model detects the disease with 96.1 % accuracy. This is a promising result towards designing a model that can be used for automatic coffee disease detection. As a future work, we would like to recomendede the model to recognize other coffee parts stems and roots with large amount of images.

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