CD Skripsi

Klasifikasi Tandan Buah Segar Kelapa Sawit Varietas Dura Dan Tenera Berdasarkan Densitas Buah Menggunakan Computer Vision Dan Image

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Conventional identification of oil palm varieties, particularly Dura and Tenera, is still carried out destructively and subjectively, making it less suitable for large-scale applications. This study proposes a non-destructive classification method based on computer vision using ImageJ software. A total of 40 fresh fruit bunches (FFB), consisting of 20 Dura and 20 Tenera, were imaged from both the front and back sides under standardized conditions using a CMOS camera. Digital image analysis with a resolution of 1024 × 768 pixels was performed to extract morphological parameters, including fruitlet count, bunch area, mean RGB intensity, fruitlet density (fruitlets/cm²), as well as bunch mass and its relationship with density. The results showed that the Tenera variety consistently exhibited higher density, fruitlet count, RGB intensity, and bunch mass compared to Dura. Meanwhile, the Dura variety demonstrated a stronger correlation between bunch mass and fruitlet density. These findings confirm that ImageJ-based computer vision is a rapid, objective, cost-effective, and non-destructive method for oil palm variety classification, with strong potential to support quality assessment and the optimization of CPO production.
Keywords: Computer vision, ImageJ, oil palm, fresh fruit bunch, variety, fruitlet density, classification.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

210313478

Penerbit

Pekanbaru : Universitas Riau FMIPA Fisika., 2025

Deskripsi Fisik

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Bahasa

Indonesia

ISBN/ISSN

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Klasifikasi

210313478

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

FISIKA

Info Detail Spesifik

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Pernyataan Tanggungjawab

Mutia

Versi lain/terkait

Lampiran Berkas

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