Lightweight Vision Transformer Compression for On-Device Plant Disease Detection in Resource-Constrained Agricultural Field Conditions

Mahadev Sunil Kumar, Bhavika Gondi, Desaisetty Venkata Satya Sai Swapnith, Gangireddy Rahul Jogi, Sudheesh Manalil et al.|September 4, 2026arXiv

Key Takeaway

Combining multiple compression techniques (pruning, quantization, distillation) can dramatically shrink Vision Transformers for on-device deployment, but simpler approaches sometimes match performance at lower computational cost.

Summary

This paper presents a method to compress Vision Transformers for plant disease detection on mobile devices. The researchers combine three compression techniques—pruning, quantization, and knowledge distillation—to reduce model size by 54.5x while maintaining 95% accuracy on chilli disease classification, enabling deployment in resource-limited agricultural settings.

efficiency applications training

Key Terms

model-compression quantization knowledge-distillation pruning vision-transformer