IFDA-EMA-YOLOv9: Wheat Disease Detection Integrating Flow Optimization and Auxiliary Supervision

To address the challenges of feature extraction in complex field environments, the limited sensitivity of YOLOv9 to subtle disease features, and the lack of adaptive hyperparameter optimization, this paper proposes an improved high-precision detection model, named IFDA-EMA-YOLOv9. First, to enhance feature extraction capabilities, an Efficient Multi-scale Attention (EMA) mechanism and residual connections are incorporated into the network architecture. This integration effectively suppresses background noise interference and significantly improves the model's ability to aggregate and represent multi-level features of wheat disease lesions. Second, to tackle localization deviations caused by the irregular geometric shapes of disease lesions, an auxiliary box mechanism is integrated into the Complete Intersection over Union (CIoU) loss function, optimizing the regression process to improve the fit of detection boxes. Furthermore, an Improved Flow Direction Algorithm (IFDA) is employed to perform global optimization of the critical model hyperparameters, thereby avoiding the blindness of manual tuning and the local optimum trap. Experimental results on the LWDCD2020 dataset demonstrate that the proposed IFDA-EMA-YOLOv9 significantly outperforms current state-of-the-art (SOTA) methods, achieving substantial improvements in Precision, Recall, and mAP@0.5 by 6.4%, 5.94%, and 6.66%, respectively. These results demonstrate the effectiveness and robustness of the proposed method for wheat disease and pest detection.

@article{Fan2026,
  title     = {IFDA-EMA-YOLOv9: Wheat Disease Detection Integrating Flow Optimization and Auxiliary Supervision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Jiaxiang Fan and Leixiao Li},
  doi       = {10.14569/IJACSA.2026.0170435},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170435}
}