mlsanityData to 7 Oct 2026

Comparative Analysis of YOLOv9, YOLOv10 and RT-DETR for Real-Time Weed Detection

arXiv 2412.13490 ↗First posted 18 Dec 2024cs.CVcited by 7

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Abstract (arXiv)

This paper presents a comprehensive evaluation of state-of-the-art object detection models, including YOLOv9, YOLOv10, and RT-DETR, for the task of weed detection in smart-spraying applications focusing on three classes: Sugarbeet, Monocot, and Dicot. The performance of these models is compared based on mean Average Precision (mAP) scores and inference times on different GPU and CPU devices. We consider various model variations, such as nano, small, medium, large alongside different image resolutions (320px, 480px, 640px, 800px, 960px). The results highlight the trade-offs between inference time and detection accuracy, providing valuable insights for selecting the most suitable model for real-time weed detection. This study aims to guide the development of efficient and effective smart spraying systems, enhancing agricultural productivity through precise weed management.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 14 of its 37 references that are in the map. First posted on arXiv 18 Dec 2024.

Builds on

14 of 37 references.

References · 14

  1. 2017Attention Is All You Needcited by 59,932
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  7. 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,829
  8. 2015Fast R-CNNcited by 2,930
  9. 2023DETRs Beat YOLOs on Real-time Object Detectioncited by 526
  10. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 508
  11. 2024YOLOv9: Learning What You Want to Learn Using Programmable Gradient Informationcited by 422
  12. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
2 more
  1. 2022Slicing Aided Hyper Inference and Fine-tuning for Small Object Detectioncited by 101
  2. 2018A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detectorcited by 56

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Assessing the Capability of YOLO- and Transformer-based Object Detectors for Real-time Weed Detectioncited by 5
  2. 2025Exploring Model Quantization in GenAI-based Image Inpainting and Detection of Arable Plantscited by 2
  3. 2025Improving Lightweight Weed Detection via Knowledge Distillationcited by 1
  4. 2026Resource-Constrained UAV-Based Weed Detection for Site-Specific Management on Edge Devicescited by 1
  5. 2025Robotic System with AI for Real Time Weed Detection, Canopy Aware Spraying, and Droplet Pattern Evaluationcited by 0
  6. 2025Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systemscited by 0
  7. 2026Detection of On-Ground Chestnuts Using Artificial Intelligence Toward Automated Pickingcited by 0