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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) Jurnal Sains dan Seni ITS Jurnal Reka Lingkungan IPTEK Journal of Proceedings Series WAKTU: Jurnal Teknik UNIPA Jurnal Abdimas PHB : Jurnal Pengabdian Masyarakat Progresif Humanis Brainstorming Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informatika dan Rekayasa Elektronik Nusantara Science and Technology Proceedings Best : Journal of Applied Electrical, Science and Technology Seminar Nasional Hasil Riset dan Pengabdian (SNHRP) Inovasi Fisika Indonesia (IFI) SinarFe7 Journal FORTEI-JEERI Faraday: Journal of Fundamental Physics, Research, and Applied Science
Akbar Sujiwa
Physics Study Program, Faculty Of Engineering And Science, Pembangunan Nasional “Veteran” Jawa Timur University, Surabaya 60294, Indonesia
Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Other

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Deep Learning Approach for Identifying Fresh and Rotten Chili Peppers using YOLO Sujiwa, Akbar; Hasan, Nailul; Timur, Fajar; Rizkiarna, Reffany C.
BEST Vol 8 No 1 (2026): BEST
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/4gmxqj65

Abstract

Abstract—In Indonesia, chili peppers are a vital agricultural commodity, widely used in various local dishes. Chili peppers are widely sold in modern markets and traditional markets. In some traditional markets, sellers sometimes do not store them in suitable conditions, thereby affecting the quality of the chili peppers. Sometimes, buyers are unable to select the specific chilies being sold wholesale, so they often end up with chilies of inferior quality. Therefore, this study proposes a deep learning-based computer vision system using the You Only Look Once (YOLO) framework to distinguish between fresh and rotten chili peppers. The model that we use was trained using 400 datasets from web-crawled images. To simulate real-world scenarios, during testing, some synthetic images were arranged in both regular and random patterns, as well as various chili orientations. Experimental results demonstrate that the proposed model effectively localizes and classifies chili peppers with confidence scores ranging from 0.55 to 0.90. Quantitative evaluation results achieved an accuracy of 0.5048, precision of 0.5516, recall of 0.4580, and mAP@0.5 of 0.4662, indicating moderate detection and classification performance under varying visual conditions. The result of the experiment shows that the system success for distinguishes between fresh and rotten chilli pepper in some conditions of image variation, such as the angular orientation of the chilli peppers and their proximity. This shows that the approach using YOLO provides very promising results for application in the agricultural market.
Co-Authors Adi Winarno Afif Nuril Musthofa Aini, Rasyida Shabihah Zukro Akbar, Musafa Akhmad Solikin Arif Yachya Arif, Irfan Aruan, Nenni Mona Atmiasri Az Zahra, Ladya Cheril Dian Majid Dimas Ricky Saputra Dira Ernawati Edi Purwanto Edi Purwanto Endarko Endarko Fajar Timur Febrianto, Muhammad Akbar Hadi Pri Prasetyo Hadiwikarta, Oktavianus Hasan, Nailul Immanuel Siburian Irfan Arif Irwan Puji Raharjo Isabella, Adinda Iwan Santoso Kristianto Wahyu Lestari Maniani, Billy Sopater Marta, Bayu Tirta Moch. Shofwan Mochamad Arif Nurochman Muhammad Ubaydillah Nabilla Nabilla Nailul Hasan Nenni Mona Aruan Nia Dwi Puspitasari Oktavianus Hadiwikarta Parama Diptya Widayaka Prasetya, Bayu Prasetyo, Hadi Pri Primasari Cahya Wardhani Raharjo, Irwan Puji Reffany Choiru Rizkiarna Risqi Rahmat Dianto Rizkiarna, Reffany C. Rizky Setyobudi Sagita Rochman Santika, Arum Sinda Satrio, Eko Nurhadi Sudrajat, Oka Akbar Suhadata Syahrul Ramadhan Ardiansyah Ubaydillah, Muhammad Widodo Winarno F. Bastari Winarno Fadjar Bastari