Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community.

翻译：视觉是农业领域多项数字技术与工具的核心组成部分。目标检测算法YOLO（You Look Only Once）凭借其卓越性能，在短时间内广泛应用于农业领域。YOLO能够实现具有良好精度的实时检测，现已部署于监测、 surveillance、传感、自动化与机器人等各类农业任务中。YOLO在农业领域的研究与应用正快速推进，但呈现碎片化与多学科交叉特征。此外，该目标检测算法的性能特征（即精度、速度、计算量）直接影响农业技术的实施与采纳速度。本研究旨在通过系统性文献收集，对YOLO在农业目标识别领域的进展与应用进行批判性评估与记录。首先，我们对257篇文献进行文献计量分析，以勾勒YOLO在农业领域的研究图景；其次，对30篇文献开展系统综述，明确当前YOLO面向特定农业任务的知识体系、现有不足与改进方向。本研究重点评估并总结了YOLO端到端学习框架的完整流程，涵盖数据采集、处理、网络优化、系统集成与部署等环节。我们还探讨了为应对农业目标或环境挑战而进行的任务导向型YOLO算法改进与集成。总体而言，集成YOLO的数字工具与技术展现出在实时自动化监测、 surveillance与目标处理方面的潜力，有助于降低劳动力与生产成本、减少环境影响，同时最大化资源利用效率。本研究为YOLO在农业领域的应用提供了详实记录，显著推进了现有知识体系，对科学界具有重要参考价值。