mlsanityData to 7 Oct 2026

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

arXiv 2406.10139 ↗First posted 14 Jun 2024cs.CVcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 24 of its 115 references that are in the map. First posted on arXiv 14 Jun 2024.

Builds on

24 of 115 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  4. 2016Densely Connected Convolutional Networkscited by 6,691
  5. 2016Feature Pyramid Networks for Object Detectioncited by 5,021
  6. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  7. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  8. 2015Fast R-CNNcited by 2,930
  9. 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
  10. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  11. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 978
  12. 2021RepVGG: Making VGG-style ConvNets Great Againcited by 520
12 more
  1. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  2. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  3. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  4. 2019CSPNet: A New Backbone that can Enhance Learning Capability of CNNcited by 381
  5. 2021Oriented R-CNN for Object Detectioncited by 230
  6. 2022DAMO-YOLO : A Report on Real-Time Object Detection Designcited by 38
  7. 2015Fast and Accurate Recurrent Neural Network Acoustic Models for Speech Recognitioncited by 26
  8. 2020Cross-Iteration Batch Normalizationcited by 20
  9. 2024YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Imagescited by 10
  10. 2018Recent Advances in Object Detection in the Age of Deep Convolutional Neural Networkscited by 7
  11. 2019PAN: Path Integral Based Convolution for Deep Graph Neural Networkscited by 5
  12. 2021Structured Model Pruning of Convolutional Networks on Tensor Processing Unitscited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 8; 2025: 4; 2026: 2.

14 papers.

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  1. 2025YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Reviewcited by 15
  2. 2024YOLOv5, YOLOv8 and YOLOv10: The Go-To Detectors for Real-time Visioncited by 12
  3. 2024YOLOv11 for Vehicle Detection: Advancements, Performance, and Applications in Intelligent Transportation Systemscited by 9
  4. 2024A Comparative Analysis of YOLOv5, YOLOv8, and YOLOv10 in Kitchen Safetycited by 4
  5. 2024Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysiscited by 2
  6. 2024Enhancing Construction Site Safety: A Lightweight Convolutional Network for Effective Helmet Detectioncited by 1
  7. 2024What is YOLOv6? A Deep Insight into the Object Detection Modelcited by 1
  8. 2026YOLO Object Detectors for Robotics -- a Comparative Studycited by 1
  9. 2024Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challengescited by 0
  10. 2024Morphological Detection and Classification of Microplastics and Nanoplastics Emerged from Consumer Products by Deep Learningcited by 0
  11. 2025Improving Object Detection Performance through YOLOv8: A Comprehensive Training and Evaluation Studycited by 0
  12. 2025A Low-Cost Machine Learning Approach for Timber Diameter Estimationcited by 0
2 more
  1. 2025Real-Time Fish Detection in Indonesian Marine Ecosystems Using Lightweight YOLOv10-nano Architecturecited by 0
  2. 2026YOLOBirDrone: Dataset for Bird vs Drone Detection and Classification and a YOLO based enhanced learning architecturecited by 0