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Taufik Fuadi Abidin
Department Of Informatics, Faculty Of Mathematics And Natural Sciences, Universitas Syiah Kuala, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Astronomy Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Mathematics Medicine & Pharmacology Neuroscience Physics Public Health Social Sciences Other

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Ripeness Detection and Shelf Life Prediction of Avocados Using YOLOv8 and Hybrid Machine Learning Ulfa, Maria; Abidin, Taufik Fuadi; Muchtar, Kahlil
Journal of Information Systems Engineering and Business Intelligence Vol. 12 No. 1 (2026): February
Publisher : Universitas Airlangga

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Abstract

Background: Post-harvest ripening for Hass avocados is hard because their ripening pattern is unpredictable. Recent studies have explored deep learning applications for ripening stages classification. However, it needs further development to achieve better results that can run effectively on low-end devices like smartphones. Objective: The study aims to achieve three main goals that involve developing an efficient YOLOv8 object detection model that works with local avocado varieties to precisely determine ripening stages, creating a hybrid machine learning model to improve classification performance, and predicting storage duration (shelf life). The study also aims to develop a practical mobile application for post-harvest use. Methods: The YOLOv8 model was trained first on the Hass avocado dataset for transfer learning, followed by a second training on the local avocado dataset for domain adaptation. Mean Average Precision (mAP) was used to evaluate a standalone YOLOv8 object detection model, while accuracy and F1-score were used to evaluate classification. The hybrid YOLOv8 and machine learning approach includes selecting the optimal YOLOv8 layer for feature extraction, using Random Forest for feature selection, applying SMOTE to handle data imbalance, and classifying with Logistic Regression, SVM, and XGBoost. Storage duration was performed using a standalone YOLOv8 and hybrid ML classification results, along with a linear regression formula. Results: The standalone YOLOv8 model achieved an mAP50 of 0.93 and a classification accuracy of 0.90 on the local avocado dataset. The hybrid method achieved a classification accuracy of 0.96. The storage estimation results showed that the hybrid approach produced an MAE of 0.43 days, while the standalone approach produced 0.44 days. The results were better than previous studies, achieving an error rate of 0.96 days. Conclusion: The research achieved its goal of developing an improved approach to identify avocados and determine their storage duration. The hybrid model functions as a working solution that improves post-harvest operations using Android-based applications.   Keywords: YOLOv8, Machine Learning, Avocado Ripening, Shelf-life Estimation
Co-Authors . Khalisna Abbas Adam AzZuhri Cut Morina Zubainur Dalila Husna Yunadi Diandita, Elly Rizki Elly Rizki Diandita Firman Maulana Adhari Fitra Riyanda Fitri Arnia Gan, Connie Cai Ru Husnul Khatimah Imran Imran Juanda BJ Kemaladina, Intan Khalisna Khalisna Kurnia Saputra M. Ikhsan Maria Ulfa Muchtar, Kahlil Muhammad Furqan Muhammad Yani, Muhammad Muslim Muslim Nurul Ayuni Rahmad Dawood Rahmah Johar Rahmah Johar Rahmah Johar Rahmah Johar Ramzi Adriman Reshi Haryanzi Ridha Ferdhiana Riza, Hammam Rizka Puspitasari, Rizka Syahrul Syahrul Tandi, Teuku Yusransyah Zahrina, Zahrina