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
