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

Creative Commons License

Nie J., Yuan Y., Li Y., Wang H., Li J., Wang Y., ...Daha Fazla

Tarim Bilimleri Dergisi, cilt.30, sa.2, ss.216-228, 2024 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 30 Sayı: 2
  • Basım Tarihi: 2024
  • Doi Numarası: 10.15832/ankutbd.1339516
  • Dergi Adı: Tarim Bilimleri Dergisi
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, Food Science & Technology Abstracts, Veterinary Science Database, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.216-228
  • Anahtar Kelimeler: Data augmentation, Few-shot learning, Fine-tune, Intelligent agriculture, Meta-learning, Metric learning
  • Atatürk Üniversitesi Adresli: Evet

Özet

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.