IMAGE BASED BARLEY LEAF FUNGAL DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK

Abstract

Barley is one of the most grown crops in the east Arsi, west Arsi, and Bale zones of Oromia Region. It is one of the main sources of food and income in these and other areas of Ethiopia. However, barley crop was affected by fungal disease which reduces the production and main cause for the economic losses in agricultural industries in Ethiopia. For the betterment of human health, fungal diseases in leaf of barley crops must be controlled and effectively monitored. The earlier researchers have used hand-crafted-features for image classification and recognition with machine learning approach. Nowadays, the development in Deep Learning has allowed researchers to drastically improve the accuracy of object detection and classification. In this thesis, the researcher used a deep-learning approach which is Convolutional Neural Network algorithm to detect fungal disease of barley crop using leaf images collected from Arsi zone of Kulumsa research center and other images captured directly from different Barley farms. The researcher dataset contains two categories of barley crop: leaf rust and normal. The dataset contains 10,224 healthy and diseased images. From this, 80% of the images are used for training and the rest for testing the model. During training, data augmentation is used to generate more images to fit the proposed model. Additionally, many researchers agree that using data augmentation can also increase the performance of the model. The designed model is trained and tested using the collected dataset and compared with two pre-trained convolutional neural network models namely Mobile Net and InceptionV3. The model obtained 99.53% accuracy and it can be used as a practical tool for farmers to protect barley crop, against fungal diseases