A Comprehensive Review of Few-Shot Learning Approaches for Cross-Region Crop Type Recognition

In this paper, a comprehensive survey of few-shot cross-region crop type recognition based on time-series remote sensing data is introduced. Emphasizes the integration of few-shot learning, prototype-based adaptation, and Time-Series Foundation Models for dealing with domain shift and few labeled data in agricultural applications. The survey covers the traditional machine learning, deep learning, and transformer-based approaches to crop classification using multi-temporal satellite images. It also reviews publicly available data, metrics for evaluation and recent advances in self-supervised and foundation model methods. Different challenges like computational complexity, lack of annotations, domain shift, and lack of benchmarks are discussed. In conclusion, the future research directions encompass efficient integration of TSFM prototype, multimodal learning, and explainable AI in precision agriculture systems to address the challenges of scalability and precision. The purpose of this work is to offer a structured overview to inform researchers to build strong, data-efficient and generalizable crop monitoring models for real-world agricultural decision-making across a variety of environmental conditions and to aid sustainable food security and policy planning around the world.