Rice Maturity Level Segmentation in Paddy Fields Based on UAV Aerial Imagery Using the YOLOv8 Algorithm
DOI:
https://doi.org/10.55606/jeei.v6i2.7207Keywords:
Computer Vision, Image Segmentation, Rice Maturity, UAV, YOLOv8Abstract
Accurate identification of rice maturity is an important factor in determining the optimal harvest period and maintaining grain quality. Conventional field observations are often influenced by subjective judg-ment and may produce inconsistent results across different observers. This study proposes an auto-mated approach for rice maturity segmentation by integrating Unmanned Aerial Vehicle (UAV) imagery with the YOLOv8 deep-learning model. A dataset consisting of 682 aerial images was collected from paddy fields and categorized into three classes: unripe, ripe, and unhealthy rice. The images were an-notated using bounding boxes and divided into training, validation, and testing subsets. Model training was performed using YOLOv8n for 100 epochs with a batch size of 16. Performance evaluation em-ployed accuracy, precision, recall, and F1-score metrics derived from the confusion matrix. Experimental results showed that the proposed framework achieved an accuracy of up to 93%, demonstrating its capability to identify rice maturity conditions effectively. The findings suggest that UAV-based moni-toring combined with deep learning can support precision agriculture by providing a faster, more ob-jective, and scalable alternative to manual field assessment.
References
[1] O. G. Ajayi, P. O. Ibrahim, and O. S. Adegboyega, “Effect of hyperparameter tuning on YOLOv8 performance for UAV crop classification,” Applied Sciences, vol. 14, no. 13, p. 5708, 2024, doi: 10.3390/app14135708.
[2] C. M. Badgujar, A. Poulose, and H. Gan, “Agricultural object detection with You Only Look Once (YOLO) algorithm: A bibliometric and systematic literature review,” arXiv, 2024. [Online]. Available: https://arxiv.org/abs/2401.10379
[3] Z. Chen, Y. Fang, J. Yin, S. Lv, F. Sheikh Muhammad, and L. Liu, “A novel lightweight YOLOv8-PSS model for obstacle detection on the path of unmanned agricultural vehicles,” Frontiers in Plant Science, vol. 15, p. 1509746, 2024, doi: 10.3389/fpls.2024.1509746.
[4] S. Dutta, S. Banerjee, S. Mahata, A. Sen, and S. Datta, “A low-cost UAV deep learning pipeline for integrated apple disease diagnosis and fruit detection,” arXiv, 2025. [Online]. Available: https://arxiv.org/abs/2512.22990
[5] J. Jumadi, Y. Yupianti, and D. Sartika, “Pengolahan citra digital untuk identifikasi objek menggunakan metode hierarchical agglomerative clustering,” JST Jurnal Sains dan Teknologi, vol. 10, no. 2, pp. 148–156, 2021.
[6] L. Rahma, H. Syaputra, A. H. Mirza, and S. D. Purnamasari, “Objek deteksi makanan khas Palembang menggunakan algoritma YOLO,” Jurnal Nasional Ilmu Komputer, vol. 2, no. 3, pp. 213–232, 2021.
[7] N. N. Shofi, A. S. Arifianto, and M. Bintoro, “Sistem peramalan waktu masak fisiologis benih padi menggunakan double exponential smoothing,” Jurnal Teknologi Informasi dan Terapan, 2022.
[8] F. Satria and E. Poerwandono, “Implementasi YOLOv8 untuk deteksi dan klasifikasi tingkat kematangan buah mangga berdasarkan citra digital,” STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer, vol. 4, no. 4, pp. 294–301, 2025.
[9] N. Krisdianto, R. K. Atharizq, and W. T. Nurhasanaah, “Pengembangan sistem pengenalan objek multi-kelas berbasis segmentasi citra dengan YOLOv11 dan Streamlit,” IJInf: International Journal of Informatics, vol. 1, no. 1, pp. 1–18, 2025.
[10] R. Iskandar, “Analisis pixel dalam pengolahan citra digital,” Jurnal Informatika dan Multimedia, 2023.
[11] B. Setiawan, “Implementasi pengolahan citra digital untuk analisis gambar,” Jurnal Sains dan Informatika, 2022.
[12] W. Gendy and D. Patel, “Advancements in computer vision: A comprehensive survey of image processing and interdisciplinary applications,” Academic Journal of Science and Technology, vol. 13, no. 2, pp. 28–34, 2024.
[13] J. Wei, R. Wang, S. Wei, X. Wang, and S. Xu, “Recognition of maize tassels based on improved YOLOv8 and UAV RGB images,” Drones, vol. 8, no. 11, p. 691, 2024, doi: 10.3390/drones8110691.
[14] D. Wang, M. Zhao, Z. Li, S. Xu, X. Wu, M. X. Ma, and X. Liu, “A survey of unmanned aerial vehicles and deep learning in precision agriculture,” European Journal of Agronomy, vol. 164, p. 127477, 2025, doi: 10.1016/j.eja.2024.127477.
[15] X. Zhang, Y. Liu, and H. Chen, “Early drought detection in maize using UAV images and YOLOv8+,” Drones, vol. 8, no. 5, p. 170, 2024, doi: 10.3390/drones8050170.
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