Few-shot Learning in Intelligent Agriculture: A Review of Methods and Applications

Authors : Jing Nie, Yichen Yuan, Yang Li, Huting Wang, Jingbin Li, Yi Wang, Kangle Song, Sezai Ercisli

Pages : 216-228

Doi:10.15832/ankutbd.1339516

View : 845 | Download : 351

Publication Date : 2024-03-26

Article Type : Review Paper

Abstract :Due to the high cost of data acquisition in many specific fields, such as intelligent agriculture, the available data is insufficient for the typical deep learning paradigm to show its superior performance. As an important complement to deep learning, few-shot learning focuses on pattern recognition tasks under the constraint of limited data, which can be used to solve practical problems in many application fields with data scarcity. This survey summarizes the research status, main models and representative achievements of few-shot learning from four aspects: model fine-tuning, meta-learning, metric learning and data enhancement, and especially introduces the few-shot learning-driven typical applications in intelligent agriculture. Finally, the current challenges of few-shot learning and its development trends in intelligent agriculture are prospected.
Keywords : Few shot learning, Intelligent agriculture, Meta learning, Metric learning, Fine tune, Data augmentation

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