Optimization of Accuracy Improvement through Modified ShuffleNet Architecture in Rice Classification

Accurate rice classification is essential to determine the quality and market value of rice. Traditional methods of rice classification are often time-consuming and error-prone, so a more efficient and accurate solution is needed. This study aims to optimize rice classification using Convolutional N...

Descripció completa

Dades bibliogràfiques

Autors principals:	Abdullah Ahmad, Dedy Hartama, Agus Perdana Windarto, Anjar Wanto
Format:	Artigo
Idioma:	Inglês
Publicat:	Ikatan Ahli Informatika Indonesia 2025-10-01
Col·lecció:	Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Matèries:	cnn model optimization mobilenet rice classification restnet shufflenet
Accés en línia:	https://jurnal.iaii.or.id/index.php/RESTI/article/view/6411
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!