  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.
