Article Per Year (5 Year)
p-Index From 2020 - 2025
0.61
P-Index
This Author published in this journals
All Journal Komunikasi Fisika Indonesia Bulletin of Electrical Engineering and Informatics Science, Technology, and Communication Journal
Dasta, Vicky Vernando
Unknown Affiliation
Computer Science & IT Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Physics

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection Shiddiq, Minarni; Saktioto, Saktioto; Salambue, Roni; Wardana, Fiqra; Dasta, Vicky Vernando; Harmailil, Ihsan Okta; Rabin, Mohammed Fisal; Arpyanti, Nisa; Wahyudi, Dilham
Bulletin of Electrical Engineering and Informatics Vol 13, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i6.8120

Abstract

Oil palm fresh fruit bunches (FFBs) are the raw material of crude palm oil (CPO) on which ripeness levels of FFBs are essential to obtain good quality CPO. Most palm oil mills use experienced graders to evaluate FFB ripeness levels. Researchers have developed rapid and non-destructive methods for ripeness detection using computer vision (CV) and deep learning. However, most of the experiments used color cameras, such as a webcam or a smartphone, limited to visible wavelengths, and used still FFBs on–trees or on the ground. This study developed a light-emitting diode (LED)-based multispectral imaging system with deep learning for rapid and real-time ripeness detection of oil palm FFBs on a moving conveyor. The ripeness levels used were unripe and ripe. We also evaluated the spectrum of reflectance intensities for the ripeness levels. The ripeness detection system employed a two-class you only look once version 4 (YOLOv4) detection model using a dataset of 2000 annotated unripe and ripe FFB multispectral images and a video of 30 moving FFBs for real-time testing. The results show a promising method to detect oil palm FFB ripeness with an average accuracy of 99.66% and a speed range of 3.32-3.62 frame per second (FPS).
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

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.
Co-Authors Amanullah, Farid Arpyanti, Nisa Harmailil, Ihsan Okta Minarni Shiddiq, Minarni Nasir, Zulfa Rabin, Mohammad Fisal Rabin, Mohammed Fisal Rahmondia N. Setiadi Roni Salambue Saktioto Saktioto Wahyudi, Dilham Wardana, Fiqra Yanuar Hamzah