Authentication And Classification Of Ethiopian Coffee Beans Using Deep Learning Approach.

Abstract

This research paper presents a comprehensive study on the authentication and classification of Ethiopian coffee beans using deep learning algorithms. The objective is to develop an accurate and reliable model for identifying and categorizing coffee beans based on their origin and quality. The methodology employed in this study incorporates a literature review of existing approaches and leverages deep learning algorithms, namely VGG-16, InceptionNetResNetv2, DenseNet121, and MobileNetV2. These architectures have demonstrated excellent performance in various image recognition tasks. The models were trained and evaluated on a carefully curated dataset of Ethiopian coffee beans. The primary evaluation metric used in this study is accuracy. The hyper parameters are carefully set, and the Adam optimizer is employed to enhance model performance. This study used 11,242 datasets from multiple sources, including bean characteristics and geographical origin. To ensure reliable results, a well-defined dataset split strategy was employed, with 60% of the dataset allocated for training the models, and the remaining 40% reserved for testing and validation. The 60:40 split ratios adopted in this research demonstrated notable improvements in model performance. By training the models on a larger portion of the dataset, they exhibited enhanced accuracy and generalization abilities. The testing and validation sets played a critical role in evaluating the models' performance. The experimental results indicate that the VGG-16 model achieved an accuracy of 89%, InceptionNetResNetv2 achieved 100% accuracy, DenseNet121 achieved 90% accuracy, and MobileNetV2 achieved an accuracy of 94%. These high accuracy rates showcase the effectiveness of the deep learning approach in accurately authenticating and classifying Ethiopian coffee beans. However, it is important to acknowledge certain limitations, such as the sample size and the need for external validation. Future research endeavors should focus on expanding the dataset and incorporating external validation to enhance the reliability and generalizability of the models. This study contributes to the existing literature by showcasing the potential of deep learning algorithms in the field of coffee bean authentication and classification, paving the way for improved quality control and consumer confidence in Ethiopian coffee products