  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.
