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

TL;DR

This paper presents a novel two-stage approach combining Mid Point Normalization and attention mechanisms to improve leaf disease classification and segmentation, achieving high accuracy and efficiency.

Contribution

The paper introduces a new methodology integrating Mid Point Normalization with attention mechanisms for enhanced plant disease detection and segmentation.

Findings

Achieved 93% classification accuracy.

Attained 72.44% Dice score in segmentation.

Produced computationally efficient lightweight models.

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.