Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism
Abstract
Enhancing plant disease detection from leaf imagery remains a persistent challenge due to scarce labeled data and complex contextual factors. We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing, coupled with sophisticated attention mechanisms that dynamically recalibrate feature representations. Our classification pipeline, merging MPN with Squeeze-and-Excitation (SE) blocks, achieves remarkable 93% accuracy while maintaining exceptional class-wise balance. The perfect F1 score attained for our target class exemplifies attention's power in adaptive feature refinement. For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs, delivering compelling performance gains with 72.44% Dice score and 58.54% IoU, substantially outperforming baseline implementations. Beyond superior accuracy metrics, our approach yields computationally efficient, lightweight architectures perfectly suited for real-world computer vision applications.
Cite
@article{arxiv.2505.21316,
title = {Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism},
author = {Enam Ahmed Taufik and Antara Firoz Parsa and Seraj Al Mahmud Mostafa},
journal= {arXiv preprint arXiv:2505.21316},
year = {2025}
}
Comments
Accepted in 2025 IEEE International Conference on Image Processing (ICIP)