ENSET DISEASE DETECTION AND CLASSIFICATION USING DEEP LEARNING TECHNIQUES

Abstract

Ethiopians, especially those in Sidama and Central Ethiopia, are the main consumers of enset. Enset is thought to be a staple food source for 20 million people in Ethiopia. Ethiopians mostly employ enset plants as a staple food crop. For many Ethiopians, the plant's root and stems are a major source of energy due to their high fiber and carbohydrate content. Typically, stems are picked, cleaned, and fermented to produce kocho and bulla, which are food items that are similar to bread. This research investigates the application of deep learning techniques, specifically Convolutional Neural Networks (CNNs), for the detection and classification of diseases affecting Enset leaves and stems. By employing sophisticated image processing tools and methodologies, the study aims to improve the accuracy and efficiency of disease identification in Enset plants. Experimental findings underscore the effectiveness of the proposed CNN models in achieving notable accuracy rates in disease detection, showcasing the potential of deep learning in revolutionizing agricultural practices. The study not only emphasizes the importance of advanced image processing in agricultural contexts but also underscores the necessity for further research in crop disease detection to enhance agricultural sustainability and productivity. We collected from the Central Ethiopia Region (Wonago and Dilla) and the Sidama Region (Hawassa Zuria, Boricha, Yirgalem, and Aletawondo) to use a 5,000 image dataset. There are 1000 images in each class. A total of 700 training, 200 validation, and 100 testing images were chosen. We used pre-trained models, MobileNetV3Small and EfficientNetB7, to compare the results with the newly developed model. Bacterial wilt, Mosaic Virus, Bacterial leaf spot, Insect pest, and Healthy Leaves are the disease classes. The Nadam optimizer, 32 batch sizes, 65 epochs, and 0.001 learning rate are the selected hyperparameters. The model was stable at epoch 65 and has an accuracy rate of 99.30%. EfficientNetB7 and MobileNetV3Small, the pre trained models, have accuracy rates of 95.32% and 93.08%, respectively. The developed model's output has a high degree of accuracy in identifying and classifying diseases in Enset leaves