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Journal : JOIV : International Journal on Informatics Visualization

Optimizing YOLOv8 for Enhanced Melon Maturity Detection with Attention Mechanisms: A Case Study from Puspalebo Orchard Umar, Ubaidillah; Sardjono, Tri Arief; Kusuma, Hendra; Yani, Mohamad; Widyantara, Helmy
JOIV : International Journal on Informatics Visualization Vol 9, No 3 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.3.2942

Abstract

Enhancing fruit maturity detection is crucial in the agricultural industry to ensure product quality and reduce post-harvest losses. However, commonly used maturity detection methods still rely on human visual inspection, which is prone to errors and assessment variability. Challenges like lighting variations, complex backgrounds, and diverse environmental conditions often complicate accurate and efficient detection. This study aims to develop and evaluate an optimized YOLOv8 model with attention mechanisms to detect melon maturity. The dataset was obtained from Puspalebo Orchard in East Java, Indonesia, comprising over a thousand melon images divided into three subsets: 70% for training, 20% for validation, and 10% for testing. The YOLOv8 model was modified to support the integration of attention mechanisms to enhance focus on significant features and detection accuracy. Data augmentation techniques were applied to capture environmental condition variations, improving the model's robustness. Evaluation on the validation subset showed a precision of 0.979 for all classes, recall of 0.962, mAP@50 of 0.981, and mAP@50-95 of 0.941. The model also demonstrated high efficiency for real-time applications with a preprocessing time of 0.1ms, inference time of 0.9ms, and post-process time of 0.9ms per image. The results of this study show advantages in detection detail, adaptability, and real-time efficiency compared to other studies in the past five years. Some weaknesses were identified, such as implementation complexity and the need for a large dataset. The developed YOLOv8 model improves melon maturity detection performance, offering a more accurate, efficient, and adaptive solution for the agricultural industry.
Co-Authors Achmad Yanu Aliffianto Adiputra, Dimas Aditya Prima Suparno, Aditya Prima Afandi, Mas Aly Andi Divangga Pratama , Moch. Andrew Brian Osmond Anifatul Faricha Aufa Ulinuha, Panji Aulia Rahma Annisa Axel Danu Pramudita Basuki Rahmat Bayu Dadang Pribadi Bernadus Anggo Seno AjiAji Chokoh Setyo Utomo Dewa Nusantara Murdoko Putra Djoko Purwanto Dominggo Bayu Baskara Dwi Edi Setyawan DWI SURYANTO Dwi Wahyu Saputra FADHLAN, FATHURROZAQ Farah Zakiyah Rahmanti Galih Kusuma Wardana Harianto Harianto Hariyanto Hariyanto Hariyanto, Muhammad Dwi Hendra Kusuma Hendy Briantoro Ignatia Indreswari Ira Puspasari Isa Hafidz Khodijah Amiroh Ma'ruf Firmansyah, Muhammad Madha Christian Wibowo Madha Christrian W. Minto Waluyo Moh. Yani Mohamad Irwan Afandi Mohammad Yanuar Hariyawan Montolalu, Billy Muhammad Adib Kamali Muhammad Iqbal Maulana Muhammad Rafi Irzam Muhammad Rivai Muhammad Rivai Oktavia Ayu Permata Pambudi, Sandhi Yuda Pauladie Susanto Philip Tobianto Daely Purba Daru Kusuma Purnama Anaking Purnama Anaking Rachmawati Oktaria Mardiyanto, Rachmawati Oktaria Ratih Kesuma Dewi Reynanda Shaquille Purwanto Reza Alauddin Albanna Ristanti Akseptori Rizaldy Febry Nugraha Rochmanto, Raditya Artha Seno Adi Putra Shochibah Yatimatul Asmak, Shochibah Yatimatul Sukamto, Ika Sumiyarsi Sukma Ardihantoko, Irdani Therzian Richard Perkasa Tjahyadi, Nathanael Toto Alfian Wahyuono, Toto Alfian Tri Arief Sardjono Tuhu Agung Rachmanto Ubaidilah Umar Ubaidillah Umar, Ubaidillah Wahyu Andy Prastyabudi Wali Satria Bahari Johan, Ahmad Yanuhar Prabowo Yupit Sudianto