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.

翻译：本综述探讨了从YOLOv1到最先进的YOLOv10等多种YOLO变体在农业进步背景下的变革潜力。其主要目标是阐明这些尖端目标检测模型如何为农业的各个方面（从作物监测到牲畜管理）注入新活力并实现优化。本文旨在达成若干关键目标，包括识别农业领域的当代挑战、详细评估YOLO模型的渐进式进展，以及探究其在农业中的具体应用。这是首批涵盖最新YOLOv10模型的综述之一，为人工智能与自动化时代下的精准农业和可持续农业实践提供了全新视角。此外，本综述对YOLO模型的性能进行了批判性分析，综合了现有研究成果并展望了未来趋势。通过审视YOLO变体所具备的独特能力及其实际应用，本综述为理解YOLO变体与农业之间不断演进的关系提供了宝贵见解。研究结果有助于深化对精准农业与可持续农业实践潜力的细致理解，标志着先进目标检测技术与农业领域融合的重要进展。