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

Mujadded Al Rabbani Alif, Muhammad Hussain

TL;DR

The paper addresses the need for scalable, real-time object detection in agriculture by surveying YOLO variants from v1 to v10. It systematizes architectural evolutions, training strategies, and performance trends across crops, pests, disease, and farm management scenarios, highlighting improvements in speed and accuracy and the move toward anchor-free and NMS-free approaches. Key contributions include a cross-version agricultural performance synthesis, critical discussion of deployment challenges (data generalization, hardware constraints, environmental variability, small-object detection), and a roadmap for multi-modal data fusion, explainability, real-time adaptation, and human-AI collaboration. The practical impact lies in guiding researchers and practitioners toward effective, field-ready YOLO-based solutions for precision farming, enabling faster, data-driven decisions to enhance yield, sustainability, and resource use.

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.

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

TL;DR

The paper addresses the need for scalable, real-time object detection in agriculture by surveying YOLO variants from v1 to v10. It systematizes architectural evolutions, training strategies, and performance trends across crops, pests, disease, and farm management scenarios, highlighting improvements in speed and accuracy and the move toward anchor-free and NMS-free approaches. Key contributions include a cross-version agricultural performance synthesis, critical discussion of deployment challenges (data generalization, hardware constraints, environmental variability, small-object detection), and a roadmap for multi-modal data fusion, explainability, real-time adaptation, and human-AI collaboration. The practical impact lies in guiding researchers and practitioners toward effective, field-ready YOLO-based solutions for precision farming, enabling faster, data-driven decisions to enhance yield, sustainability, and resource use.

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.
Paper Structure (35 sections, 9 equations, 8 figures, 11 tables)

This paper contains 35 sections, 9 equations, 8 figures, 11 tables.

Table of Contents

  1. Introduction
  2. Survey Objective
  3. Organization of Paper
  4. Convolutional Neural Networks (CNN)
  5. Object Detection
  6. YOLO Architecture Background
  7. YOLOv1
  8. YOLOv2
  9. YOLOv3
  10. YOLOv4
  11. YOLOv5
  12. YOLOv6
  13. YOLOv7
  14. YOLOv8
  15. YOLOv9
  16. ...and 20 more sections

Figures (8)

  • Figure 1: The General structure of a CNN, highlighting convolutional layers, pooling, and fully connected layers.
  • Figure 2: Single and multiple objects in an image: Classification, Localization, Segmentation.
  • Figure 3: Abstract architecture of single-stage object detectors.
  • Figure 4: Multi-scale Detection Architecture
  • Figure 5: Path Aggregation YOLOv4 (a) Addition (b) Concatenation
  • ...and 3 more figures