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All Journal Komunikasi Fisika Indonesia Bulletin of Electrical Engineering and Informatics Journal of Aceh Physics Society Jurnal Edu Research Jurnal Penelitian Sains JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) Jurnal Teori dan Aplikasi Fisika Jurnal Kewarganegaraan
Minarni Minarni
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Agriculture, Biological Sciences & Forestry Astronomy Chemistry Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Materials Science & Nanotechnology Mathematics Physics Social Sciences Other

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Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection Minarni Shiddiq; Saktioto Saktioto; Roni Salambue; Fiqra Wardana; Vicky Vernando Dasta; Ihsan Okta Harmailil; Mohammed Fisal Rabin; Nisa Arpyanti; Dilham Wahyudi
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).
Co-Authors Alexander Nasution Annisa Fadillah Annisa Fadlillah Annisya Madani Aswandi Bahar Daeng Ayub Dedi Permana Dewi Anjarwati Mahmudah Dewi Laila Sari Dian Eka Rachmawati Dilham Wahyudi Dina Veranita Dinda Kamia Evkha Putri Dodi Sofyan Arief Feri Candra Fiqra Wardana Hefniati Ishak Herman Herman Ihsan Okta Harmailil Ihsan Okta Harmailil Ikhsan Raahaman Husein Ikhsan Rahman Husein Irfan Cahyadi JR Lessy Eka Putri Lentina Br Sitohang Mailestari Wina Yance Maksi Ginting Mhd Feri Desfri Mohammad Fisal Rabin Mohammad Fisal Rabin Mohammed Fisal Rabin Muhammad Ikhsan Hamid Nadia Zakyyah Yasmin Nadia Zakyyah Yasmin Nisa Arpyanti Nurmaya Sari Purnama - Rahmondia Nanda Setiadi Rakhmawati Farma Ramdani Ramdani Ramma Hayu Fitra Ramma Hayu Fitra Reza Umami Ria Fitriani Roni Salambue Roni Salumbae Saktioto Saktioto Sinta Afria Ningsih Sri Hermonica Titi Maemunaty Tomy Suherly Tri Handoko Vicky Vernando Dasta Walfred Tambuhan Yohanes Dwi Saputra Zilhan Hasbi