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 <PRE_TAG>YOLOv10</POST_TAG>, 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 <PRE_TAG>YOLOv10</POST_TAG>, offering a fresh perspective on its implications for <PRE_TAG>precision farming</POST_TAG> 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 <PRE_TAG>precision farming</POST_TAG> and sustainable agricultural practices, marking a significant step forward in the integration of advanced object detection technologies within the agricultural sector.