An object detection–based multimedia system for intelligent classification and grading of unmilled local rice

Abstract

This paper presents an object detection–based multimedia system for intelligent classification and grading of unmilled local rice using mobile imaging and deep learning. Unlike conventional image-level classification, the study reformulates rice grading as a multi-object detection problem, enabling grain-level localization and classification of clean, unclean, and wet grains within a single image. A YOLOv11-based model is integrated into a cloud-assisted architecture to support realtime inference in field conditions. The system was trained and evaluated on a balanced dataset of 1,350 annotated images collected from multiple rice varieties under realistic acquisition settings. Results demonstrate strong detection performance, achieving F1-scores between 0.97 and 0.99 across all classes. While F1-score is emphasized, the study acknowledges the need for additional metrics such as mean Average Precision (mAP) and confidence intervals for more comprehensive evaluation. YOLOv11 is selected for its efficiency in real-time deployment; however, future work will include benchmarking against models such as YOLOv8 and Faster R-CNN. The relatively modest dataset size is also recognized as a limitation. User evaluation using UTAUT indicates high usability and practical relevance. Overall, the system provides a scalable and transparent solution for preliminary rice grading and decision support.