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- Title
Few‐shot learning for plant disease recognition: A review.
- Authors
Sun, Jianqiang; Cao, Wei; Fu, Xi; Ochi, Sunao; Yamanaka, Takehiko
- 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. Core Ideas: Generally, machine learning requires a large‐scale dataset with supervised information annotated by specialists.Few‐shot learning (FSL) is a new paradigm machine learning that enables machines to learn from small samples.FSL has attracted research attention in plant disease recognition.This review introduces FSL with its applications in plant disease recognition.
- Subjects
DEEP learning; COMPUTER vision; DATA augmentation; CROP quality; MACHINE learning; PLANT collecting; PLANT diseases
- Publication
Agronomy Journal, 2024, Vol 116, Issue 3, p1204
- ISSN
0002-1962
- Publication type
Article
- DOI
10.1002/agj2.21285