Efficient Leaf Disease Classification and Segmentation using Midpoint Normalization Technique and Attention Mechanism
This addresses the problem of scarce labeled data and complex contexts in agricultural computer vision, offering an incremental improvement for real-world applications.
The paper tackled plant disease detection from leaf imagery by introducing a two-stage method with Midpoint Normalization and attention mechanisms, achieving 93% classification accuracy and segmentation scores of 72.44% Dice and 58.54% IoU.
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.