Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation

TL;DR

This paper enhances plant leaf disease classification by integrating adversarial training for robustness, explainability for transparency, and knowledge distillation for model compression, balancing accuracy, robustness, and efficiency.

Contribution

It introduces a comprehensive approach combining adversarial training, explainability, and knowledge distillation for improved leaf disease classification.

Findings

Adversarial training reduces accuracy by 3%-20% on regular tests.

Robustness against adversarial attacks increases by 50%-70%.

Student models achieve 15-25 times efficiency with slight performance loss.

Abstract

This work focuses on plant leaf disease classification and explores three crucial aspects: adversarial training, model explainability, and model compression. The models' robustness against adversarial attacks is enhanced through adversarial training, ensuring accurate classification even in the presence of threats. Leveraging explainability techniques, we gain insights into the model's decision-making process, improving trust and transparency. Additionally, we explore model compression techniques to optimize computational efficiency while maintaining classification performance. Through our experiments, we determine that on a benchmark dataset, the robustness can be the price of the classification accuracy with performance reductions of 3%-20% for regular tests and gains of 50%-70% for adversarial attack tests. We also demonstrate that a student model can be 15-25 times more computationally efficient for a slight performance reduction, distilling the knowledge of more complex models.