@inproceedings{b1825d4b4c1f4c2a9766caddf57433ac,
title = "Crop Detection and Tracking Oriented for Embedded Weeding Based on YOLO-DeepSORT",
abstract = "Mechanical weeding has grown in popularity and importance in intelligent agriculture as technology and research develop and people become more concerned with environmental preservation and organic farming. The crop's location is crucial for the technology, which includes object detection and trajectory tracking. The former is responsible for determining the crop's accurate location, while the latter is in charge of reducing the impact from the environment. In this research, we developed a collection of intelligent weeding equipment based on the YOLOv5 object detection model. To optimize the performance of detection while applying our model to embedded device for real-time weeding application, we incorporated DeepSORT trajectory tracking into YOLO method. We also built a collection of datasets based on corn seedling centers to train the model work properly in the actual world. The results of the investigation demonstrate that occlusion could be resolved and weeding can be implemented more effectively by incorporating trajectory tracking into the object detection model.",
keywords = "mechanical weeding, object detection, trajectory tracking",
author = "Yushuo Hu and Qiang Wang and Zhanqiang Xing",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 51st Annual Conference of the IEEE Industrial Electronics Society, IECON 2025 ; Conference date: 14-10-2025 Through 17-10-2025",
year = "2025",
doi = "10.1109/IECON58223.2025.11221616",
language = "英语",
series = "IECON Proceedings (Industrial Electronics Conference)",
publisher = "IEEE Computer Society",
booktitle = "IECON 2025 - 51st Annual Conference of the IEEE Industrial Electronics Society",
address = "美国",
}