Seeing Through the Dark: Adaptive Switch-Guided Knowledge Distillation Via Heterogeneous Teachers for Robust Plant Disease Diagnosis

Computational cost during model deployment can be reduced through knowledge distillation (KD) which transfers knowledge from a teacher model to a lightweight student model while maintaining performance. However, KD methods based on a single teacher often provide limited knowledge diversity and may transfer less informative features. Furthermore, the application of KD for plant disease classification in low-light noisy environments has received little attention. To address these limitations, we propose a KD framework using heterogeneous teachers with adaptive feature alignment (KDHT-AFA) that integrates an adaptive feature distillation switch (AFDS) to selectively align intermediate representations. For effective knowledge transfer, we propose a lightweight custom student model with 5.16 million parameters. The model incorporates a regional statistical hybrid attention (RSHA) to enhance important features semantically while increasing sensitivity to local structure and contrast. Additionally, we introduce a new self-collected real low-light plant disease dataset (ReLL-PDD v1) acquired under realistic agricultural conditions. For performance evaluation, we considered two open-source datasets, PlantVillage and the Potato Leaf Disease dataset, along with the ReLL-PDD v1 dataset. Our model achieved a mean accuracy of 86.88