Agricultural Object Detection with You Look Only Once (YOLO) Algorithm:
  A Bibliometric and Systematic Literature Review
Chetan M Badgujar Alwin Poulose Hao Gan
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.