Cross-Architecture Knowledge Distillation from a Vision Foundation Model to a Lightweight Visual State Space Model for Tea Leaf Disease Classification

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 introduces AgriKD, a cross-architecture knowledge distillation framework for efficient edge deployment, which transfers knowledge from a Vision Transformer (ViT) teacher to a compact convolutional student model. To bridge the representational gap between Transformer and CNN architectures, the proposed approach integrates multiple distillation objectives at the output, feature, and relational levels, where each objective captures a different aspect of the teacher knowledge. This enables the student model to better preserve and utilize transformer-derived global representations. Experiments on multiple leaf disease datasets show that the distilled student achieves performance comparable to the teacher while significantly improving efficiency, reducing model parameters by approximately 172 times, computational cost by 47.57 times, and inference latency by 18-22 times. Furthermore, the optimized model is deployed across multiple runtime formats, including ONNX, TFLite Float16, and TensorRT FP16, achieving consistent predictive performance with negligible accuracy degradation. Real-world deployment on NVIDIA Jetson edge devices and a mobile application demonstrates reliable real-time inference, highlighting the practicality of AgriKD for AI-powered agricultural applications in resource-constrained environments.

Running deep models for medical image analysis at the point of care is challenging with standard architectures: convolutional neural networks (CNNs) are often too heavy for edge devices, while Vision Transformers (ViTs) require massive training datasets to compensate for missing inductive biases. This study investigates how far the dual-token distillation used to train data-efficient ViTs can be pushed when applied across architectures, from a CNN-based teacher into a lightweight ViT student. LiteMed-ViT is introduced as a dual-token ViT featuring a convolutional stem and a class-balanced distillation objective; at 0.234M parameters, LiteMed-ViT carries 47.8x fewer parameters than its ResNet18 teacher at a memory footprint of just 0.89 MB. Evaluated against three compact CNN baselines on three MedMNIST subsets of varying modality and class balance, LiteMed-ViT attains higher accuracy than all three baselines on BloodMNIST (95.05%) and two on PathMNIST (89.15%). Under MedMNIST-C corruptions, it is at least as robust as every compact CNN across all subsets, outperforming them on BloodMNIST and PathMNIST without declining in relative standing as severity increases. Particular attention is devoted to identifying the exact conditions where performance plateaus. On DermaMNIST, LiteMed-ViT's balanced accuracy remains up to 1.9 points below the strongest compact CNNs and does not benefit from higher input resolutions up to 224 pixels, where ResNet18 gains 12.6 points. Stronger teachers do not yield a more accurate student, and overconfident ones lower its DermaMNIST balanced accuracy unless calibrated.

Foundation models deliver strong perception but are often too computationally heavy to deploy, and adapting them typically requires costly annotations. We introduce a semi-supervised knowledge distillation (SSKD) framework that compresses pre-trained vision foundation models (VFMs) into compact experts using limited labeled and abundant unlabeled data, and instantiate it for instance segmentation where per-pixel labels are particularly expensive. The framework unfolds in three stages: (1) domain adaptation of the VFM(s) via self-training with contrastive calibration, (2) knowledge transfer through a unified multi-objective loss, and (3) student refinement to mitigate residual pseudo-label bias. Central to our approach is an instance-aware pixel-wise contrastive loss that fuses mask and class scores to extract informative negatives and enforce clear inter-instance margins. By maintaining this contrastive signal across both adaptation and distillation, we align teacher and student embeddings and more effectively leverage unlabeled images. On Cityscapes and ADE20K, our $\approx 11\times$ smaller student improves over its zero-shot VFM teacher(s) by +11.9 and +8.6 AP, surpasses adapted teacher(s) by +3.4 and +1.5 AP, and outperforms state-of-the-art SSKD methods on benchmarks.