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

• URL: http://arxiv.org/abs/2406.10139v1
• Date: Fri, 14 Jun 2024 15:48:43 GMT
• Title: YOLOv1 to YOLOv10: A comprehensive review of YOLO variants and their application in the agricultural domain
• Authors: Mujadded Al Rabbani Alif, Muhammad Hussain,
• Abstract summary: This survey investigates the transformative potential of various YOLO variants, from YOLOv1 to the state-of-the-art YOLOv10. The primary objective is to elucidate how these cutting-edge object detection models can re-energise and optimize diverse aspects of agriculture. The findings contribute towards a nuanced understanding of the potential for precision farming and sustainable agricultural practices.
• Score: 0.5639904484784127
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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