Paddy Seeds Classification using Machine Learning Models for Public Health

The paper establishes an automated computer vision classification baseline for public health and quality control across ten distinct rice cultivars utilizing transfer learning. By training and testing deep state-of-the-art models (GoogleNet, ResNet-50, and VGG-16) on automated photographic seed datasets provided directly by the Bangladesh Rice Research Institute (BRRI), the framework achieves a stable classification accuracy up to 78% without relying on time-consuming manual feature extraction.