Detection and Classification of Areca Nuts using Machine Vision

Author(s)	Jyoti Kammar, A. V. Kolaki
Country	India
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.
Field	Engineering
Published In	Volume 6, Issue 6, November-December 2024
Published On	2024-12-13
Cite This	Detection and Classification of Areca Nuts using Machine Vision - Jyoti Kammar, A. V. Kolaki - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32344
DOI	https://doi.org/10.36948/ijfmr.2024.v06i06.32344
Short DOI	https://doi.org/g8wkps

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International Journal For Multidisciplinary Research