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All Journal Komunikasi Fisika Indonesia Science, Technology, and Communication Journal
Rabin, Mohammad Fisal
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Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

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Relation of reflectance intensity and chemical contents of oil palm fresh fruit bunches using multispectral imaging Arpyanti, Nisa; Shiddiq, Minarni; Setiadi, Rahmondia Nanda; Rabin, Mohammad Fisal; Harmailil, Ihsan Okta; Dasta, Vicky Vernando
Indonesian Physics Communication Vol 22, No 2 (2025)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.22.2.149-156

Abstract

Multispectral imaging has been widely used for the classification of fruits and vegetables. This technique offers both spectral and spatial resolution, enabling the evaluation of fruit quality based on its chemical properties. This study aims to analyze the relationship between reflectance intensity obtained from multispectral imaging and the chemical composition of oil palm fresh fruit bunches (FFBs), specifically oil content and free fatty acid (FFA) levels, measured using the Soxhlet extraction method. The multispectral imaging system consists of a monochrome camera and an LED light source with eight wavelengths ranging from 680 nm to 900 nm. FFB images were processed using Python scripts to extract reflectance intensity. The Python scripts were also used to analyze the correlation between reflectance intensity and both oil content and FFA levels. A total of 15 unripe and 15 ripe FFB samples were used. Correlation analysis was focused on the 780 nm wavelength due to its high reflectance intensity. The results showed that the correlation coefficient between reflectance intensity and oil content was r = -0.39 for unripe fruits and r = 0.29 for ripe fruits, while the combined data yielded a strong correlation of r = 0.92. For FFA, the correlation was r = -0.41 for unripe fruits, r = -0.34 for ripe fruits, and r = 0.72 for the combined dataset. These findings demonstrate that multispectral imaging is a promising non-destructive method for classifying the ripeness of oil palm FFBs based on oil content and FFA levels.
Physical properties of oil palm fresh fruit bunch varieties Shiddiq, Minarni; Hamzah, Yanuar; Nasir, Zulfa; Amanullah, Farid; Rabin, Mohammad Fisal; Dasta, Vicky Vernando
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

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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.
Co-Authors Amanullah, Farid Arpyanti, Nisa Dasta, Vicky Vernando Harmailil, Ihsan Okta Minarni Shiddiq, Minarni Nasir, Zulfa Rahmondia N. Setiadi Yanuar Hamzah