AgriKD: Cross-Architecture Knowledge Distillation for Efficient Leaf Disease Classification

ArXi:2605.01355v1 Announce Type: new Automated leaf disease classification is critical for early disease detection in resource-constrained field environments. Vision Transformers (ViTs) provide strong representation capability by modeling long-range dependencies and inter-class relationships; however, their high computational cost makes them impractical for deployment on edge devices. As a result, existing approaches struggle to effectively transfer these rich representations to lightweight models. This paper.