Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism

• URL: http://arxiv.org/abs/2505.21316v1
• Date: Tue, 27 May 2025 15:14:04 GMT
• Title: Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism
• Authors: Enam Ahmed Taufik, Antara Firoz Parsa, Seraj Al Mahmud Mostafa,
• Abstract summary: We introduce a transformative two-stage methodology, Mid Point Normalization (MPN) for intelligent image preprocessing.<n>Our classification pipeline achieves 93% accuracy while maintaining exceptional class-wise balance.<n>For segmentation tasks, we seamlessly integrate identical attention blocks within U-Net architecture using MPN-enhanced inputs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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