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Vicky Vernando Dasta
Department of Physics, Universitas Riau, Pekanbaru 28293, Indonesia
Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology

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Physical properties of oil palm fresh fruit bunch varieties Minarni Shiddiq; Yanuar Hamzah; Zulfa Nasir; Farid Amanullah; Mohammad Fisal Rabin; Vicky Vernando Dasta
Science, Technology, and Communication Journal Vol. 6 No. 1 (2025): SINTECHCOM Journal (October 2025)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i1.336

Abstract

Identification of oil palm fresh fruit bunches (FFB) based on variety is a crucial step in sorting and grading FFBs to produce good-quality crude palm oil (CPO). Most palm oil mills receive two varieties of FFBs at the reception stations, Tenera and Dura, and only a certain percentage of the Dura variety is allowed in a transporting truck. The conventional identification is destructive, cutting several fruits off an FFB bunch and checking for fruit Mesocarp and shell thickness. The method suffers a high increase in free fatty acid (FFA) content. This study is a preliminary study using computer vision and image processing to differentiate the two varieties based on their physical properties. The samples consisted of 20 Dura and 20 Tenera FFBs, 10 unripe and 10 ripe FFBs. The FFB images were acquired for both front and back sides using a color CMOS camera. ImageJ software was used to obtain the number of outer fruits and bunch surface area, used to calculate fruitlet density. Both varieties are also compared based on mass and by red, green, and blue (RGB) intensities. The results were compared to the results measured manually. The results showed that the Tenera variety exhibited higher fruit density, fruitlet count, RGB intensity compared to the Dura variety. Both varieties have higher correlations between fruit density and their masses. These results show the potential of computer vision and image processing methods to differentiate Tenera and Dura varieties, used for sorting and grading oil palm FFBs.
Identification of tenera and dura variety of oil palm fresh fruit bunches based on RGB color and fruit firmness Melisa Zuliana; Minarni Shiddiq; Herman Syahdan; Farid Amanullah; Tiya Novita Sari; Mita Virdina; Ola Noviza; Vicky Vernando Dasta
Science, Technology, and Communication Journal Vol. 6 No. 3 (2026): SINTECHCOM Journal (June 2026)
Publisher : Lembaga Studi Pendidikan dan Rekayasa Alam Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59190/stc.v6i3.381

Abstract

Sorting and grading oil palm fresh fruit bunches (FFBs) by variety in oil palm mills is destructive and inefficient, necessitating a more accurate, rapid, non-destructive approach. This preliminary study aims to develop a computer vision system to identify oil palm FFBs varieties (dura and tenera) using RGB intensities and fruit firmness levels. The study used 20 dura FFBs and 20 tenera FFBs, each with 10 ripe and 10 unripe FFBs, while fruit firmness was measured with a GY-3 needle-type penetrometer. Analysis of RGB intensities showed that ripe tenera had the highest values, while unripe dura had the lowest. In addition to RGB intensity analysis, this study also used principal component analysis (PCA) to visualize the separation patterns of varieties and ripeness levels based on RGB values. The PCA results showed that RGB intensity values clearly distinguished the dura and tenera groups in both ripe and unripe conditions. In terms of firmness, unripe fruits of both varieties had significantly higher firmness values than ripe fruits, with unripe dura showing the highest value of 12.5 kg/cm2 and ripe tenera showing the lowest value of 7.23 kg/cm2, indicating an inverse relationship between ripeness and fruit firmness. This study demonstrates that RGB intensities and fruit firmness levels can serve as potential parameters for distinguishing between the dura and tenera varieties in a computer-vision-based oil palm FFBs sorting system.
Co-Authors Farid Amanullah Herman Syahdan Melisa Zuliana Minarni Shiddiq Mita Virdina Mohammad Fisal Rabin Ola Noviza Tiya Novita Sari Yanuar Hamzah Zulfa Nasir