Penggunaan Visi Komputer untuk Mengidentifiksi Jenis Buah dari Gambar

Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.

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Publication Details

Journal
Router
Published
2024-11-24
DOI
https://doi.org/10.62951/router.v2i4.287
Primary Topic
Computer Science and Engineering
Type
article
Field-Weighted Citation Impact
0.00
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article

Penggunaan Visi Komputer untuk Mengidentifiksi Jenis Buah dari Gambar

Supiyandi Supiyandi, Rafif Rasendriya
Router
Computer Science and Engineering
article

Penggunaan Visi Komputer untuk Mengidentifiksi Jenis Buah dari Gambar

Supiyandi Supiyandi, Rafif Rasendriya
article en

Abstract

Computer vision technology has advanced rapidly and made significant contributions across various fields, including object identification in images. This study aims to develop a computer vision-based system to identify fruit types from images. A machine learning model is applied using a dataset of fruit images to train the system for accurate fruit recognition. The primary processes include data acquisition, image preprocessing, feature extraction, model training, and performance evaluation. The results demonstrate a high level of accuracy in identifying specific fruit types, showcasing the potential of this technology in agricultural and commercial applications.

RouterVol. 2(4)
Universitas Islam Negeri Sumatera Utara (ID), Universitas Pembangunan Panca Budi Medan (ID)
Openalex Percentile: Top 73%
Computer Science and Engineering
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