DEEP LEARNING BASED FABA BEANS LEAF DISEASES DETECTION AND CLASSIFICATION

Abstract

Faba bean (Vicia Faba L.) is believed to be originated from the Near East and now days spread throughout the world. It’s one of the most domesticly legume in the world next to chickpea and pea. Ethiopia is the second leading producer of Faba beans next to China in the world. It shares 6.96% of world production and 40.5% with of Africa. Faba bean is grown primarily for its edible seeds that are used for human consumption. It also used for keeping human healthy and sustaining the productivity of the farming system through the fixation of nitrogen. However, most of the time it is affected by different diseases that result in reduction of quality and quantity of the Faba bean production. Those diseases are caused by fungus, virus, and bacteria. Usually Faba bean diseases appear on the leaf, flower, pods, seed, and stem a step by step and makes the crop out of usage. Mainly, leaf of Faba bean is more affected by diseases than other parts. It attacks both inside and outside of the leaves. Leaf plays an important role during the growing period of Faba bean. Without leaf there is no flower, without flower there is no pod, without pod there is no seed. Traditionally, farmers and experts detect and identify plant diseases by naked eyes. This method is inaccurate and expensive, because there are numerous diseases. Detection by using image processing techniques has been more accurate and fast. Therefore, we need to develop automatic deep learning based Faba bean leaf diseases detection and classification model. We designed Faba bean leaf disease model architecture using convolutional neural network for Faba bean leaf diseases detection and classification. CNN become accurate and precise method for the detection and classification of plant diseases. The study can be conducted in the plantation area of Faba bean in Oromia region, Arsi zone, D/Xijo Woreda, from the farmer plantation land particular reference to Bucho Silase kebele, Ethiopia, where the dataset has been collected. Leaves of healthy and infected crops are collected and labeled. Processing of image has been performed with pixel wise operations to enhance the image. It is followed with feature extraction the classification of patterns of captured leaves in order to identify Faba bean plant leaf diseases. Four classifier labels are used as ascochyta blight, chocolate spot- botrytis, rust, and Healthy leaf. The features extracted are fit into the neural network with the dataset was spilt into training set, validation set and testing set, 80%, 10%, and 10% respectively, with the batch size 32 and using Adam optimizer. Faba bean leaf diseases detection and classification model achieved the overall accuracy 99.58%