A transformer-based few-shot learning pipeline for barley disease detection from field-collected imagery

Masoud Rezaei, Dean Diepeveen, Hamid Laga, Sanjiv Gupta, Michael G. K. Jones, Ferdous Sohel

Abstract: Highlights•A few-shot learning method is developed to address data scarcity problems.•Results on the collected plant disease data warrant the model’s potential.•Cutting-edge transformers, e.g. Swin-B, perform well given only five training images•Meta-training and transfer learning significantly improve performance.•Apparent disease symptoms can be detected and used in various applications.