Few-shot crop disease recognition using sequence- weighted ensemble model-agnostic meta-learning
Diseases pose significant threats to crop production, leading to substantial yield reductions and jeopardizing global food security. Timely and accurate detection of crop diseases is essential for ensuring sustainable agricultural development and effective crop management. While deep learning-based...
Saved in:
| Main Authors: | Junlong Li, Quan Feng, Junqi Yang, Jianhua Zhang, Sen Yang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-08-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2025.1615873/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
From laboratory to field: cross-domain few-shot learning for crop disease identification in the field
by: Sen Yang, et al.
Published: (2024-12-01) -
Hybrid attentive prototypical network for few-shot action recognition
by: Zanxi Ruan, et al.
Published: (2024-08-01) -
Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition
by: Nguyen Anh Tu, et al.
Published: (2024-01-01) -
A Review on the Few-Shot SAR Target Recognition
by: Junjun Yin, et al.
Published: (2024-01-01) -
Ensemble-Based Model-Agnostic Meta-Learning with Operational Grouping for Intelligent Sensory Systems
by: Mainak Mallick, et al.
Published: (2025-03-01)