Year: 2024
Type: article
Abstract: 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.
Authors: Jyoti Kammar -, A. V. Kolaki -
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W4405622513