Overcoming Overfitting in CNN Models for Potato Disease Classification Using Data Augmentation - Prasetyo, Simeon Yuda (Bina Nusantara University, 2024)
Classification of diseases in potato plants is crucial for agriculture to ensure quality and yield. Potatoes, being staple foods worldwide, are vulnerable to diseases that cause significant production losses. Early and accurate disease identification is essential. This study evaluates the impact of data augmentation on reducing overfitting in deep learning models for potato disease classification. Various CNN architectures, including VGG16, VGG19, Xception, and InceptionV3, were compared in transfer learning and fine-tuning phases. The