Semi-supervised few-shot learning approach for plant diseases recognition

Background Learning from a few samples to automatically recognize the plant leaf diseases is an attractive and promising study to protect the agricultural yield and quality. The existing few-shot classification studies in agriculture are mainly based on supervised learning schemes, ignoring unlabele...

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Veröffentlicht in:	Plant methods Jg. 17; H. 1; S. 68
Hauptverfasser:	Li, Yang, Chao, Xuewei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 27.06.2021 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Accelerating image-based plant phenotyping and pattern recognition: deep learning or few-shot learning? Agricultural production Agriculture Biological Techniques Biomedical and Life Sciences Classification confidence interval Confidence intervals data collection Datasets Deep learning Diagnosis domain Domains Labeling Learning Leaves Life Sciences Machine learning Plant diseases Plant Sciences Plants Recognition Self-adaption Semi-supervised learning Supervised learning Transfer learning Unmanned aerial vehicles China Deep learning Self-adaption Transfer learning Classification
ISSN:	1746-4811, 1746-4811
Online-Zugang:	Volltext
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