Mobile-Based Real-Time Ornamental Rose Classification System Using YOLOv8 Algorithm on Digital Imagery
Abstract
This research introduces a mobile-based system for real-time identification of ornamental rose varieties using the YOLOv8 deep learning algorithm. Motivated by the growing interest in ornamental plants during the COVID-19 pandemic and the high penetration of smartphone users in Indonesia, the study aims to create an efficient and accessible flower recognition tool. A dataset of 813 labeled rose images—red, white, yellow, orange, and pink—was collected from the Roboflow platform and processed using data augmentation techniques to improve model generalization. The YOLOv8 model was trained with 100 epochs, a batch size of 16, and the SGD optimizer, then converted to TensorFlow Lite for mobile deployment through the Flutter framework. Experimental results achieved a mean average precision (mAP50–95) of 0.581, with strong detection performance across most classes. The system successfully operated offline, delivering real-time classification accuracy despite dataset imbalance, particularly in the orange rose class. These findings demonstrate that YOLOv8 can be effectively adapted for mobile horticultural applications, offering practical benefits for flower sorting, crop management, and educational use. Future studies are recommended to expand dataset diversity, enhance environmental testing, and explore cloud-based integration for scalable deployment.
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Information Systems Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
Information Systems Study Program, Sekolah Tinggi Ilmu Komputer Cipta Karya Informatika, East Jakarta City, Special Capital Region of Jakarta, Indonesia.
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Total: 15 References- Andalas. (2024). Digital transformation and smartphone adoption in Indonesia. Kominfo Press.
- Chen, Y., Wang, L., & Xu, D. (2024). Optimizing TensorFlow Lite inference for mobile computer vision applications. IEEE Access, 12, 44520–44533.
- Hidayatullah, A., Sari, P., & Rahman, D. (2025). Traffic density detection using YOLOv8 and edge devices. International Journal of Intelligent Systems and Applications, 14(1), 77–90.
- Islam, F., Ahmad, S., & Khan, M. (2023). Drone-based vegetation monitoring using YOLO models. Computers and Electronics in Agriculture, 205, 107622.
- Jocher, G. (2023). YOLOv8: Ultralytics object detection framework. https://github.com/ultralytics/yolov8
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