VCPC: virtual contrastive constraint and prototype calibration for few-shot class-incremental plant disease classification

VCPC: virtual contrastive constraint and prototype calibration for few-shot class-incremental plant disease classification

Abstract Deep learning demonstrates strong generalisation capabilities, driving substantial progress in plant disease recognition systems. However, current methods are predominantly optimised for offline implementation. Real-time crop surveillance systems encounter streaming images containing novel...

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Bibliographic Details
Main Authors: Lunhong Lou, Jianwu Lin, Lin You, Xin Zhang, Tomislav Cernava, Hanyu Lu, Xiaoyulong Chen
Format: Article
Language:English
Published: BMC 2025-07-01
Series:Plant Methods
Subjects:
Deep learning
Plant disease classification
FSCIL
Virtual contrastive class constraints
Prototype calibration embedding
Online Access:https://doi.org/10.1186/s13007-025-01423-3
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https://doi.org/10.1186/s13007-025-01423-3

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