FABA BEAN LEAF DISEASE CLASSIFICATION USING DEEP CONVOLUTIONAL NEURAL NETWORK

Abstract

Faba Bean is among a crucial crop for countries like Ethiopia due to its high protein content and high rate of production. It also improves soil fertility with the help of bacteria that forms nodules on its root. Ethiopia is the world’s second largest producer of Faba Beans next to China however, the yield is generally low according to researches. Among the constraints which constitute the low productivity of the crop, disease is the primary one. Even though there are countermeasures, such as chemical sprays, which can be taken to reduce the damage or eliminate it before harvest, if possible, an effective disease identification technique is required. Chocolate Spot, Faba Bean Gal, and Rust are the most common diseases in the Faba Bean growing areas. A total of 8010 representative sample images were used to develop an automatic Faba Bean leaf disease classification system using a convolutional neural network (CNN) following a design science research methodology. First, we developed our segmentation algorithm based on Canny edge detector and removed the image background from our region of interest. Afterward, we have designed a new CNN network architecture and a modified version of AlexNet architecture to fit our classification task. In addition, ResNet50V2 and InceptionV3 pre-trained models were chosen due to their better performance in such tasks. The FBDCNet model is trained with both raw images and segmented images. When it is trained with raw images an overall testing accuracy of 90% under the given test set is achieved. Compared to AlexNet, InceptionV3, and ResNet50 the classification accuracy exceeds by 2%, 6%, and 1% respectively. In the same way training of FBDCNet model with segmented images give a 98% testing accuracy under the given test set. Compared to AlexNet, InceptionV3 and ResNet50, the classification accuracy increased by 5%, 2%, and 1% respectively. The experimental results demonstrate that the proposed Canny edge detection based segmentation technique improves the total testing accuracy of the FBDCNet model by 8%. Thus, it can be seen that the proposed model effectively identifies Faba Bean leaf diseases with the proposed segmentation technique. Meanwhile, this study explores a new approach for the accurate classification of leaf diseases that provides a theoretical foundation for the application of deep learning in the field of agricultural information.