A new version of this entry is available:
Loading...
Article
2025
Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection
Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection
Abstract (English)
Spot spraying represents an efficient and sustainable method for reducing herbicide use in agriculture. Reliable differentiation between crops and weeds, including species-level classification, is essential for real-time application. This study compares state-of-the-art object detection models-YOLOv8, YOLOv9, YOLOv10, and RT-DETR-using 5611 images from 16 plant species. Two datasets were created, dataset 1 with training all 16 species individually and dataset 2 with grouping weeds into monocotyledonous weeds, dicotyledonous weeds, and three chosen crops. Results indicate that all models perform similarly, but YOLOv9s and YOLOv9e, exhibit strong recall (66.58 % and 72.36 %) and mAP50 (73.52 % and 79.86 %), and mAP50-95 (43.82 % and 47.00 %) in dataset 2. RT-DETR-l, excels in precision reaching 82.44 % (dataset 1) and 81.46 % (dataset 2) making it ideal for minimizing false positives. In dataset 2, YOLOv9c attains a precision of 84.76% for dicots and 78.22% recall for Zea mays L.. Inference times highlight smaller YOLO models (YOLOv8n, YOLOv9t, and YOLOv10n) as the fastest, reaching 7.64 ms (dataset 1) on an NVIDIA GeForce RTX 4090 GPU, with CPU inference times increasing significantly. These findings emphasize the trade-off between model size, accuracy, and hardware suitability for real-time agricultural applications.
File is subject to an embargo until
This is a correction to:
A correction to this entry is available:
This is a new version of:
Other version
Notes
Publication license
Publication series
Published in
Precision agriculture, 26 (2025), 3, 52.
https://doi.org/10.1007/s11119-025-10246-0.
ISSN: 1573-1618
Other version
Faculty
Institute
Examination date
Supervisor
Cite this publication
Allmendinger, A., Saltık, A. O., Peteinatos, G. G., Stein, A., & Gerhards, R. (2025). Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection. Precision agriculture, 26(3). https://doi.org/10.1007/s11119-025-10246-0
Edition / version
Citation
DOI
ISSN
ISBN
Language
English
Publisher
Publisher place
Classification (DDC)
630 Agriculture
Original object
University bibliography
Standardized keywords (GND)
Sustainable Development Goals
BibTeX
@article{Allmendinger2025,
doi = {10.1007/s11119-025-10246-0},
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/17907},
author = {Allmendinger, Alicia and Saltık, Ahmet Oğuz and Peteinatos, Gerassimos G. et al.},
title = {Assessing the capability of YOLO- and transformer-based object detectors for real-time weed detection},
journal = {Precision agriculture},
year = {2025},
volume = {26},
number = {3},
}