Ensemble-based sesame disease detection and classification using deep convolutional neural networks (CNN).

This study presents an ensemble-based approach for detecting and classifying sesame diseases using deep convolutional neural networks (CNNs). Sesame is a crucial oilseed crop that faces significant challenges from various diseases, including phyllody and bacterial blight, which adversely affect crop yield and quality. The objective of this research is to develop a robust and accurate model for identifying these diseases, leveraging the strengths of three state-of-the-art CNN architectures: ResNet-50, DenseNet-121, and Xception. The proposed ensemble model integrates these individual networks to enhance classification accuracy and improve generalization across diverse datasets. A comprehensive dataset of sesame leaf images, representing healthy, phyllody, and bacterial blight conditions was utilized to train and evaluate the models. The ensemble approach achieved an impressive overall accuracy of 96.83%, demonstrating superior performance in accurately classifying the different leaf conditions. The results highlight the effectiveness of combining multiple deep learning models, which allows for the extraction of diverse feature representations and decision-making strategies. This thesis also discusses the advantages of the ensemble methodology, including improved robustness to variations in disease symptoms and enhanced adaptability to changing agricultural practices. The findings of this research have significant implications for precision agriculture. They offer a reliable tool for the early detection and classification of sesame diseases. By enabling timely interventions, this ensemble-based framework can contribute to the sustainability and productivity of sesame cultivation, ultimately supporting food security and agricultural resilience.