YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain
<|reference_start|>YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain: This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10, in the context of agricultural advancements. The primary objective is to elucidate how these cutting-edge object detection models can re-energise and optimize diverse aspects of agriculture, ranging from crop monitoring to livestock management. It aims to achieve key objectives, including the identification of contemporary challenges in agriculture, a detailed assessment of YOLO's incremental advancements, and an exploration of its specific applications in agriculture. This is one of the first surveys to include the latest YOLOv10, offering a fresh perspective on its implications for precision farming and sustainable agricultural practices in the era of Artificial Intelligence and automation. Further, the survey undertakes a critical analysis of YOLO's performance, synthesizes existing research, and projects future trends. By scrutinizing the unique capabilities packed in YOLO variants and their real-world applications, this survey provides valuable insights into the evolving relationship between YOLO variants and agriculture. The findings contribute towards a nuanced understanding of the potential for precision farming and sustainable agricultural practices, marking a significant step forward in the integration of advanced object detection technologies within the agricultural sector.<|reference_end|>
@article{alif2024yolov1,
    title={YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their
  application in the agricultural domain},
    author={Mujadded Al Rabbani Alif, Muhammad Hussain},
    journal={arXiv preprint arXiv:2406.10139},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.10139},
    primaryClass={cs.CV}
}