Beyond supervision: Harnessing self-supervised learning in unseen plant disease recognition

Deep learning models have demonstrated great promise in plant disease identification. However, existing approaches often face challenges when dealing with unseen crop-disease pairs, limiting their practicality in real-world settings. This research addresses the gap between known and unknown (unseen) plant disease identification. Our study pioneers the exploration of the zero-shot setting within this domain, offering a new perspective to conceptualizing plant disease identification. Specifically, we introduce the novel Cross Learning Vision Transformer (CL-ViT) model, incorporating self-supervised learning, in contrast to the previous state-of-the-art, FF-ViT, which emphasizes conceptual feature disentanglement with a synthetic feature generation framework. Through comprehensive analyses, we demonstrate that our novel model outperforms state-of-the-art models in both accuracy performance and visualization analysis. This study establishes a new benchmark and marks a significant advancement in the field of plant disease identification, paving the way for more robust and efficient plant disease identification systems. The code is available at https://github.com/abelchai/Cross-Learning-Vision-Transformer-CL-ViT . • Our proposed CL-ViT sets a new benchmark in unseen plant disease identification. • Self-supervised learning minimizes domain gaps in both seen and unseen data. • CL-ViT’s self-supervised learning surpasses FF-ViT’s supervisory approach. • CL-ViT’s qualitative analysis shows it discriminates between classes effectively.