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COMPARATIVE ANALYSIS OF YOLOv8x AND YOLOv11x MODELS FOR ROTARY TEDDER FAULTS DETECTION
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DOI：10.65731/ama/2026-0008.png)
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Abstract
En 中文
This article presents a study on applying artificial intelligence, specifically YOLOv8x and YOLOv11x (You Only Look Once) models, for detecting three types of faults (healthy, broken, and missing teeth) in rotary tedders under data-constrained conditions. We trained both models on three progressively augmented datasets (V1-V3) derived from limited video footage, applying rotations, color alterations and filtering to simulate variance. Our results show a clear correlation between data volume/quality and model performance: mean Average Precision (mAP0.5) improved from 62-67% (Dataset V1) to 82-85% (Dataset V3). While YOLOv11x achieved marginally better overall accuracy (85% vs. 82%), both architectures struggled with background confusion, particularly for the healthy tooth class. This work demonstrates that with strategic data augmentation, off-the-shelf YOLO models can achieve promising detection accuracy even with limited initial data, providing a practical baseline for real-time agricultural fault monitoring systems.
Keywords:
YOLOv8x
YOLOv11x
damage detection
agriculture machine
object classification
neural network
Journal
A
IF:
1.1
Papers:
60
Citations:
411
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Cited Papers