[论文解读] Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review

Chetan Badgujar, Alwin Poulose+1 more|arXiv (Cornell University)|Jan 18, 2024

Smart Agriculture and AI被引用 9

一句话总结

通过对257篇文章的文献计量分析，评估在农业对象检测中使用YOLO的现状，总结研究景观、修改和部署挑战。

ABSTRACT

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.

研究动机与目标

• 通过对257篇文章的文献计量分析，描绘YOLO在农业领域的学术景观。
• 系统性回顾30篇文章，识别当前的知识差距、修改以及面向任务的适应性改动。
• 评估在农业情境下YOLO的端到端学习组件（数据、处理、网络修改、部署）。
• 评估整合与部署挑战，为农业中的实时监测与自动化提供信息。

提出的方法

• 对257篇文章进行了文献计量分析，以绘制YOLO在农业研究中的景观。
• 对30篇文章进行了系统性综述，以提取知识、差距和修改。
• 批判性评估端到端学习方面，包括数据获取、处理、网络修改、集成与部署。
• 讨论了针对农业对象或环境挑战的YOLO特定修改。
• 综合分析对实时监控、监视、自动化与资源效率的影响。

实验结果

研究问题

• RQ1 基于YOLO的农业对象检测当前的学术景观为何（论文数量、刊物、趋势）？
• RQ2 在农业环境中，YOLO的常见任务特定修改和整合策略有哪些？
• RQ3 影响YOLO在农业中性能的数据、处理与部署因素有哪些？
• RQ4 在知识与实践方面存在哪些差距，阻碍基于YOLO的农业系统的部署？
• RQ5 将YOLO集成工具如何影响农业中的劳动、成本和环境足迹？

主要发现

• YOLO驱动的农业在实时监控、监视和自动化处理方面具有潜力。
• 文献显示在监测、传感、自动化和农业机器人等任务中有广泛应用。
• 存在针对农业环境量身定制的显著修改与整合策略。
• 端到端学习方面（数据、处理、部署）对性能与采用率至关重要。
• 使用YOLO的数字工具可以降低劳动与成本，同时提高资源效率，并讨论了环境效益。