YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain

TL;DR

This survey reviews YOLO object detection variants from YOLOv1 to YOLOv10, highlighting their advancements and applications in agriculture to enhance precision farming and sustainability.

Contribution

It provides one of the first comprehensive analyses of YOLOv10's role in agricultural applications, assessing performance and future trends.

Findings

YOLO variants significantly improve crop and livestock monitoring.

YOLOv10 offers state-of-the-art accuracy in agricultural detection tasks.

The survey identifies challenges and future directions for AI in agriculture.

Abstract

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.