Convolutional Neural Network-Based Ground Coffee Bean Classification in the Philippines

Abstract

While existing classification methods rely primarily on visual inspection or limited technological approaches, this research introduced a Convolutional Neural Network model specifically designed to address the challenges of classifying four major coffee varieties found in the Philippines: Arabica, Excelsa, Liberica, and Robusta. A comprehensive dataset of 1,817 ground coffee bean images captured in different lighting conditions, background colors, camera angles, and elevations was collected. To mitigate these challenges, advanced preprocessing and augmentation techniques were employed, including strategic resizing, flipping, and normalization to enhance the model's generalizability. The dataset was strategically divided into 80% training, 10% validation, and 10% testing sets to ensure efficient model performance. Utilizing TensorFlow and Keras on Kaggle, the CNN model was developed and subsequently deployed via a web-based application using Flask and HTML, offering an innovative, user-friendly interface for coffee bean classification. The model achieved a high overall classification accuracy of 96%, with Robusta and Arabica varieties demonstrating perfect classification. Thus, CNNs can effectively support the Philippine coffee industry by automating bean classification. Future work may focus on expanding the dataset to capture greater variability, refining the model—particularly for Excelsa and Liberica varieties, exploring advanced machine learning techniques to improve consistency and real-world deployment, and integrating the model into real-time classification systems to support broader adoption in the coffee industry.