Few-shot learning for plant disease recognition: A review

Abstract

Monitoring plant diseases is essential for farmers to secure crop quantity and quality. Deep learning has recently been applied to plant disease recognition to help farmers take prompt and proper actions to prevent reductions in crop quantity and quality. Generally, deep learning requires a large-scale dataset with supervised information annotated often by specialists. However, because collecting plant disease images in natural environments is difficult and obtaining proper annotations from specialists is costly, deep learning is infeasible for plant disease recognition tasks. Few-shot learning (FSL) is an alternative for plant disease recognition using prior knowledge. Although FSL has attracted considerable attention, comprehensive reports on the application of FSL methods for plant disease recognition are required. Here, we introduce FSL with its applications in plant disease recognition. We begin with an overview of computer vision tasks using machine learning and FSL. We provide practical examples of FSL applications. Utilizing these practical examples, we describe different approaches for data augmentation and FSL methods of embedding, multitask learning, transfer learning, and meta-learning. Further, we summarize how models are optimized for performance with reference to existing studies. Finally, the advantages and disadvantages are discussed, along with potential challenges for FSL applications in plant disease recognition.