Extracting knowledge from limited data: An updated review of data-driven and model-driven few-shot learning for agriculture

Deep learning has demonstrated considerable success in agricultural applications. However, its conventional implementations heavily depend on large-scale labelled datasets—a requirement that is often impractical in agriculture due to data scarcity, high annotation costs, or environmental variability. While insufficient training data can significantly limit the performance of standard deep learning models, Few-Shot Learning (FSL) has emerged as a transformative paradigm, enabling robust model training with minimal labelled samples by utilising limited data for training instead. Despite its potential, a critical review assessing how FSL addresses expert system challenges in agriculture remains notably absent. This paper attempts to fill this void by presenting an updated comprehensive review of FSL's applications in agriculture. We categorise FSL methodologies into two primary approaches: data processing-driven and model learning-driven. Data processing–driven approaches address data scarcity by enriching representational diversity through synthetic samples generated with models such as generative adversarial networks, or by transferring knowledge from related domains to improve generalisation. In contrast, model learning–driven strategies confront the same challenge through specialised architectures and optimisation techniques that enable effective generalisation from limited samples. Within this taxonomy, data processing–driven paradigms include transfer learning and generative artificial intelligence, while model learning–driven paradigms cover metric learning methods such as Siamese or prototypical networks, together with model-based and optimisation approaches designed for efficient generalisation. Our analysis pinpoints cutting-edge technologies within each sector, shedding light on overlooked areas and opportunities where FSL can harness limited data to yield promising outcomes when used to solve problems in agriculture.