Comparison of deep learning models in automatic classification of coffee bean species - Adem Korkmaz; Tarık Talan; Selahattin Koşunalp; Teodor Iliev (PeerJ Computer Science)
As one of the most widely consumed beverages worldwide, coffee is characterized by its diverse flavor profiles and complex production processes. In this study, deep learning-based image processing techniques are employed for the automatic classification of coffee bean species with high accuracy. To achieve this, images of three different coffee bean species (Starbucks Pike Place, Espresso, and Kenya) were classified using five CNN-based models: Xception, DenseNet201, InceptionV3, InceptionResNetV2, and DenseNet121. The dataset comprises 1,554 coffee bean images. Cross-validation was applied to assess the models’ performance, and classification accuracy was evaluated using performance metrics. Among the tested models, InceptionV3 achieved the highest classification accuracy (93%) and precision (95%), with the lowest loss rate (0.12), making it the most effective model in this study. As a result of the experiments, the average classification success rates of the models were determined as follows: 93% for InceptionV3, 92% for DenseNet121, 91% for Xception, 91% for InceptionResNetV2, and 90% for DenseNet201. These findings indicate that InceptionV3 demonstrates the highest performance. It is anticipated that this study will make significant contributions to applications in coffee bean classification.