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...

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Autores principales:	Abdullah Ahmad, Dedy Hartama, Agus Perdana Windarto, Anjar Wanto
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Ikatan Ahli Informatika Indonesia 2025-10-01
Colección:	Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Materias:	cnn model optimization mobilenet rice classification restnet shufflenet
Acceso en línea:	https://jurnal.iaii.or.id/index.php/RESTI/article/view/6411
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