From Bean to Bar: Using CNNs for Automated White and Black Chocolate Variety Classification

Within the contemporary chocolate industry, the categorization and distinction of different chocolate varieties—especially white and dark chocolate—are essential for evaluating product quality, differentiating offerings, and satisfying customer needs. This research uses Convolutional Neural Networks (CNNs) to offer an automated method for categorizing white and dark chocolate variations. The suggested technique examines photos of chocolate samples and correctly classifies them into their appropriate classes by utilising CNNs' potent feature extraction capabilities. The first step in the procedure is gathering a large dataset of high-resolution photos of samples of dark and white chocolate from various sources. To ensure uniformity throughout the collection, these photos have undergone preprocessing to improve their quality and normalize their characteristics. The gathered dataset is then used to develop and train a CNN architecture, with the necessary modifications made to maximize its performance for chocolate classification tasks. In the chocolate sector, the automated classification of white and dark chocolate variations using CNNs presents a viable approach for improving consumer experiences, manufacturing workflow optimization, and quality control procedures. The results of this study show how deep learning techniques have the potential to transform the production and marketing of chocolate while also advancing computer vision techniques for food classification.