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

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.

Journal

JOURNAL OF AGRICULTURAL SCIENCES-TARIM BILIMLERI DERGISI

Year

2024

Volume

30

Issue

2

Pages

216-228

ISSN

1300-7580

eISSN

2148-9297

DOI

10.15832/ankutbd.1339516

WoS UID

WOS:001198336600017