Fruit Freshness Classification Based On A Custom Sequential Convolutional Neural Network - Muhammad Fathan Syarif; Didi Juardi; Iqbal Maulana (CV. AFDIFAL MAJU BERKAH, 2026)
To date, merchants and consumers in both traditional and modern markets generally still rely on direct visual observation to determine fruit freshness, a method that is highly subjective and often inconsistent. Deep Learning (DL) offers a relevant automation solution to this problem. This study applies a custom Sequential Convolutional Neural Network (CNN) architecture to simultaneously classify the type and freshness level of apples, bananas, and oranges into six classes. Using a Research and Development (R&D) approach, the model was trained on 8,400 images from Kaggle, divided into 80% training, 10% validation, and 10% testing data. The architecture consists of five convolutional layers (32 to 512 filters), reinforced with a 0.5 dropout rate and an EarlyStopping mechanism to prevent overfitting. The model achieved a test accuracy of 98.92% with a loss value of 0.1404. The trained model was integrated into a web application named