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All Journal Komunikasi Fisika Indonesia Bulletin of Electrical Engineering and Informatics
Vicky Vernando Dasta
Universitas Riau
Earth & Planetary Sciences Electrical & Electronics Engineering Energy Materials Science & Nanotechnology Physics

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ANOTASI CITRA BERBASIS PYTHON UNTUK RANCANG BANGUN PERANGKAT LUNAK DETEKSI OBJEK PADA TANDAN BUAH SEGAR KELAPA SAWIT CACAT Minarni Shiddiq; Muhammad Ikhsan Hamid; Vicky Vernando Dasta; Yohanes Dwi Saputra; Dewi Anjarwati Mahmudah; Dinda Kamia Evkha Putri; Annisya Madani; Ihsan Okta Harmailil
Indonesian Physics Communication Vol 20, No 2 (2023)
Publisher : Universitas Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31258/jkfi.20.2.135-140

Abstract

Object detection can determine the existence of an object, scope and image. Object detection begins with the introduction of an object. This method can be used to automate the process of sorting and grading oil palm fresh fruit bunches (FFB) at palm oil mills, which are still done manually. Image annotations are needed in building the software so that the software can identify object features in an image, especially imager in video frames. This study aims to annotate images of oil palm FFB into 2 categories, namely normal palm and abnormal palm. This category is the standard regulation of the Minister of Agriculture No. 14 of 2013. Image acquisition is carried out by varying the position of each oil palm FFB with the top and bottom position of the fruit which is then augmented 4 times which function to multiply the image data model to be annotated. Annotation is done using the python program application, namely Labelimg. The amount of image data that has been annotated is 200 images consisting of 100 normal palm images and 100 abnormal palm images.
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 Annisya Madani Dewi Anjarwati Mahmudah Dilham Wahyudi Dinda Kamia Evkha Putri Fiqra Wardana Ihsan Okta Harmailil Minarni Minarni Mohammed Fisal Rabin Muhammad Ikhsan Hamid Nisa Arpyanti Roni Salambue Saktioto Saktioto Yohanes Dwi Saputra