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All Journal Tekno : Jurnal Teknologi Elektro dan Kejuruan Jurnal Edukasi Elektro Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Jurnal Teknologi dan Sistem Komputer International Journal of Artificial Intelligence Research Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Sains dan Informatika ILKOM Jurnal Ilmiah J-Dinamika: Jurnal Pengabdian Kepada Masyarakat JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Abdi Dosen : Jurnal Pengabdian Pada Masyarakat Journal of Robotics and Control (JRC) Indonesian Journal of Electrical Engineering and Computer Science Community Development Journal: Jurnal Pengabdian Masyarakat AbdiTechno (Jurnal Pengabdian Masyarakat) Prosiding Seminar Nasional Pengabdian Kepada Masyarakat JURNAL PENGABDIAN PENDIDIKAN MASYARAKAT (JPPM) JOINCS (Journal of Informatics, Network, and Computer Science) Bulletin of Social Informatics Theory and Application Prosiding Seminar Nasional Pengabdian kepada Masyarakat (SINAPMAS)
Mokh Sholihul Hadi
Universitas Negeri Malang
Religion Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Mechanical Engineering Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
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.
A dataset for computer-vision-based fig fruit detection in the wild with benchmarking you only look once model detector Che Ani, Adi Izhar; Hamdani Mohammad Farid, Mohammad Afiq; Firdhaus Kamaruzaman, Ahmad Shukri; Ahmad, Sharaf; Hadi, Mokh Sholihul
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.5705

Abstract

The image datasets that are most widely used for training deep learning models are specifically developed for applications. This study introduces a novel dataset aimed at augmenting the existing data for the identification of figs in their natural habitats, specifically in the wilderness. In the present study, researchers have generated numerous image datasets specifically for object detection focus on applications in agriculture. Regrettably, it is exceedingly difficult for us to obtain a specialized dataset specifically designed for detecting figs. To tackle this issue, a grand total of 462 photographs of fig fruits were gathered. The augmentation technique was utilized to substantially increase the size of the dataset. Ultimately, we conduct an examination of the dataset by doing a baseline performance study for bounding-box detection using established object detection methods, specifically you only look once (YOLO) version 3 and YOLOv4. The performance obtained on the test photos of our dataset is satisfactory. For farmers, the capacity to identify and oversee fig fruits in their natural or developed environments can be highly advantageous. The detecting device offers instantaneous data regarding the quantity of mature figs, facilitating decision-making procedures.
Co-Authors A.N. Afandi Achmad Safii Adi Izhar Che Ani Agung Witjoro Ahmad Fuadi Ahmad Sariful Anwar Ahmad Shukri Firdhaus Kamaruzaman Ahmad, Sharaf Aji Prasetya Wibawa Alfin Firmansyah Andriana Kusuma Dewi Anik Nur Handayani Annisa Firly Aprilia Putri Arfienda Miawa Tyassilva Arfiyansyah, Rizky Argeshwara, Dityo Kreshna Aripriharta - Arisatya Bharotoyakti Arrohman, Maulana Ludfi Aswin Rosadi Busaeri, Siti Rahbiah Che Ani, Adi Izhar Choirul Tri Fandiansyah Deva Putri Lestari Dito Valentino Dityo Kreshna Argeshwara Dityo Kreshna Argeshwara Dwi Arini Mufarichah Dwi Puri Fatmala Dyah Lestari Faidzin, Ilham Fatma Cahyaningrum Fattah, Muhammad Hattah Febryan, Febryan Fido Arya Kusuma Firdhaus Kamaruzaman, Ahmad Shukri Hamdani Mohammad Farid, Mohammad Afiq Hari, Nirwana Haidar Hasanuddin, Tasrif I Made Wirawan Irvan, Mhd Joumil Aidil Saifuddin Lisma Hafifatul Aprilia M Syamsul Huda M. Alfian Mizar M. Farrel Akbar Firzatullah M. Rodzi Faiz Mhd. Irvan, Mhd. Irvan Moh. Zainul Falah Mohammad Afiq Hamdani Mohammad Farid Mohd Ikmal Fitri Maruzuki Muhammad Afnan Habibi Muhammad Yazid Muhiban Syabani Muladi Pradipta Adi Nugroho Ramdan Satra Ratna Juwita Resi Sari Dwijayanti Kartikasari Riski Achmad Fauzi Rizky Asillia P Sari Rosnani Rosnani Ryan Harris Abdillah Samsul Setumin Satia Nur Maharani Setumin, Samsul Shidiqi, Maulana Ahmad As Shidiqi, Maulana As Siti Juliana Abu Bakar Siti Sendari Siti Zubaidah Soenar Soekopitojo Soraya Norma Mustika Sugiono, Bhima Satria Rizki Sugiono, Bhima Satria Rizky Sujito Sujito Sujito Sujito Sumarno . Sunaryono Susilo, Suhiro Wongso Syafiq Ubaidilah Syaiful Anwar Tiara Windrias Putri Yandhika Surya Akbar Gumilang Yuli Agustina, Yuli Zaeni, Ilham Ari Elbaith Zulkham Umar Rosyidin Zulkham Umar Rosyidin