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.

该研究调查了从 YOLOv1 到最新的 YOLOv10 变种在农业进展方面的转变潜力，旨在阐述这些尖端目标检测模型如何重振和优化从作物监测到畜牧管理等农业各个方面，为精准农业和可持续农业提供有价值的洞见。