Comparative Analysis of YOLOv8x and YOLOv11x Models For Rotary Tedder Faults Detection
By: Kamil Felter and Arkadiusz Mystkowski
References
- Mystkowski A, Wolniakowski A, Idzkowski A, Ciężkowski M, Ostaszewski M, Kociszewski R, et al. Measurement and diagnostic system for detecting and classifying faults in the rotary hay tedder using multilayer perceptron neural networks. Engineering Applications of Artificial Intelligence. 2024. Available from: https://doi.org/10.1016/j.engappai.2024.108513
- Sewioło M, Mystkowski A, Berghout T, Khamari D, Wolszczak P, Litak G. Fault detection in rotary agricultural machinery using genetic algorithm optimized multiple input – parallel – convolutional neural networks. 2023 International Conference on Electrical Engineering and Advanced Technology (ICEEAT). 2023;1–6. Available from: https://doi.org/10.1109/iceeat60471.2023.10425835
- Krizhevsky A, Sutskever I, Hinton GE. ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems; 2012. Available from: http://books.nips.cc/papers/files/nips25/NIPS2012_0534.pdf
- Sharma N, Jain V, Mishra A. An analysis of convolutional neural networks for image classification. Procedia Computer Science. 2018. Available from: https://doi.org/10.1016/j.procs.2018.05.198
- Tensmeyer C, Martinez T. Analysis of Convolutional Neural Networks for Document Image Classification. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) . 2017. Available from: https://doi.org/10.1109/icdar.2017.71
- Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY et al. SSD: Single Shot MultiBox Detector. In: Lecture notes in computer science. 2016; 21–37. Available from: https://doi.org/10.1007/978-3-319-46448-0_2
- Liu S, Jin Y, Ruan Z, Ma Z, Gao R, Su Z. Real-Time detection of seedling maize weeds in sustainable agriculture. Sustainability. 2022. Available from: https://doi.org/10.3390/su142215088
- Yao J, Song B, Chen X, Zhang M, Dong X, Liu H, et al. Pine-YOLO: a method for detecting pine wilt disease in unmanned aerial vehicle remote sensing images. Forests. 2024. Available from: https://doi.org/10.3390/f15050737
- Tarasiuk K., Mystkowski A., Ostaszewski M., Majka A., Czarnigowski J., Performance Comparison of YOLO Setups for Agriculture Machine Surrounding Monitoring, 26th International Carpathian Control Conference (ICCC). Starý Smokovec, Słowacja. 19-21.05.2025. https://doi.org/10.1109/ICCC65605.2025.11022828
- Sharma A, Kumar V, Longchamps L. Comparative performance of YOLOv8, YOLOv9, YOLOv10, YOLOv11 and Faster R-CNN models for detection of multiple weed species. Smart Agricultural Technology; 2024. Available from: https://doi.org/10.1016/j.atech.2024.100648
- Han B, Zhang J, Almodfer R, Wang Y, Sun W, Bai T, et al. Research on innovative Apple Grading technology driven by intelligent vision and machine learning. Foods. 2025. Available from: https://doi.org/10.3390/foods14020258
- Khanam R, Hussain M. YOLOv11: An Overview of the Key Architectural Enhancements. ArXiv abs/2410.17725. 2024. Available from: https://api.semanticscholar.org/CorpusID:273532028
- Hidayatullah P, Syakrani N, Sholahuddin M, Gelar T, Tubagus R. YOLOv8 to YOLO11: A Comprehensive Architecture In-depth Comparative Review. ArXiv abs/2501.13400. 2025. Available from: https://api.semanticscholar.org/CorpusID:275820077
- Ultralytics. Models supported by ultralytics [Internet]. Ultralytics YOLO Docs. 2025. Available from: https://docs.ultralytics.com/models/
- Build Vision Models with Roboflow | Roboflow Docs. Available from: https://docs.roboflow.com/
- PyTorch documentation — PyTorch 2.7 documentation. Available from: https://pytorch.org/docs/stable/index.html
- CUDA Toolkit Documentation 12.9. Available from: https://docs.nvidia.com/cuda/
- Boufares O, Boussif M, Saadaoui W, Miraoui I. Moving object detection: a new method combining background subtraction, fuzzy entropy thresholding and differential evolution optimization. Acta Mechanica et Automatica. 2025;19(1):106–16. Available from: https://doi.org/10.2478/ama-2025-0013
- Niyongabo J, Zhang Y, Ndikumagenge J. Bearing fault detection and diagnosis based on densely connected convolutional networks. Acta Mechanica Et Automatica. 2022;16(2):130–5. Available from: https://doi.org/10.2478/ama-2022-0017
- Powroznik P, Skublewska-Paszkowska M, Rejdak R, Nowomiejska K. Automatic Method of macular Diseases detection using deep CNNGRU network in OCT images. Acta Mechanica Et Automatica. 2024;18(4):197–206. Available from: https://doi.org/10.2478/ama-2024-0074
Language: English
Page range: 68 - 78
Submitted on: May 6, 2025
Accepted on: Dec 29, 2025
Published on: Jul 16, 2026
Published by: Bialystok University of Technology
In partnership with: Paradigm Publishing Services
Keywords:
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© 2026 Kamil Felter, Arkadiusz Mystkowski, published by Bialystok University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.