Authors - Suphawatchara Malanond, Pongsarun Boonyopakorn Abstract - In the food supply industry, differentiating between cultivated and weedy rice is crucial since the latter interferes with production and competes for essential resources. This research utilizes the YOLOv8 object detection model to automate the classification of rice grains to improve the separation process. The dataset was gathered during the harvesting phase and annotated utilizing a typical bounding-box methodology. Multiple configurations were evaluated with different model sizes (nano, small, medium) and training epochs. The optimal results attained a precision of 0.845, a recall of 0.779, and a mAP@50 of 0.822. These findings indicate that YOLOv8 enables near real-time identification at the grain level, diminishing dependence on manual verification. The study yielded a lightweight prototype developed to demonstrate and reflect the application of the trained model for rapid, image-based screening by non-technical users. The significance of the study lies in its support for more effective rice quality management and its contribution to strengthening food security and sustainable agriculture.
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