Detection and Classification of Areca Nuts using Machine Vision

The classification of areca nuts into distinct quality grades is a critical task within the agricultural sector, significantly impacting market pricing and consumer satisfaction. The conventional manual classification techniques are often subjective time-consuming and prone to inconsistencies. This project attempts to overcome these obstacles by leveraging the Vision Transformer (ViT) model, a powerful deep learning architecture renowned for its exceptional outcomes in challenges involving image classification.