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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES) Bulletin of Electrical Engineering and Informatics
Siti Juliana Abu Bakar
Universiti Teknologi MARA
Economics, Econometrics & Finance Electrical & Electronics Engineering

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Systematic literature review: application of deep learning processing technique for fig fruit detection and counting Ahmad Shukri Firdhaus Kamaruzaman; Adi Izhar Che Ani; Mohammad Afiq Hamdani Mohammad Farid; Siti Juliana Abu Bakar; Mohd Ikmal Fitri Maruzuki; Samsul Setumin; Mokh. Sholihul Hadi
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4455

Abstract

Deep learning has shown much promise in target identification in recent years, and it's becoming more popular in agriculture, where fig fruit detection and counting have become important. In this study, a systematic literature review (SLR) is utilised to evaluate a deep learning algorithm for detecting and counting fig fruits. The SLR is based on the widely used 'Reporting Standards for Systematic Evidence Synthetics' (ROSES) review process. The study starts by formulating the research questions, and the proposed SLR approach is critically discussed until the data abstraction and analysis process is completed. Following that, 33 relevant research involving the agriculture sector, fruit, were selected from many studies. IEEE, Scopus, and Web of Sciences are three databases to investigate. Due to the lack of fig fruit research, fruit and vegetable studies have been included because they use similar methods and processes. The SLR found that various deep learning algorithms can count fig fruit in the field. Furthermore, as most approaches obtained acceptable results, deep learning's performance is acceptable in F1-score and average precision (AP), higher than 80%. Moreover, improvements can be produced by enhancing the existing deep learning model with the personal dataset.
Development of magnetic levitation system with position and orientation control Siti Juliana Abu Bakar; Koay J-Shenn; Patrick Goh; Nur Syazreen Ahmad
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 12, No 2: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v12.i2.pp287-296

Abstract

This work demonstrates the design and development of a magnetic levitation (MagLev) system that is able to control both the position and orientation of the levitated object. For the position control, a pole placement method was exploited to estimate parameters of the proportional integral derivative (PID) controller. In addition, the MagLev was constructed using a pair of electromagnets, two infrared (IR) receiver-emitter pairs and a servo motor to allow the orientation of the object to be controlled. The proposed controller was programmed in a LabVIEW environment, which was then compiled and deployed into an embedded NI myRIO board. Experimental results demonstrated that the proposed method was able to achieve a zero steady-state orientation error when the object was rotated from 0 ◦ to ±90◦ , a steady-state position error of 0.3 cm without rotation, and steady-state position errors of no greater than 1.2 cm with rotation.
Co-Authors Adi Izhar Che Ani Ahmad Shukri Firdhaus Kamaruzaman Koay J-Shenn Mohammad Afiq Hamdani Mohammad Farid Mohd Ikmal Fitri Maruzuki Mokh Sholihul Hadi Nur Syazreen Ahmad Patrick Goh Samsul Setumin