Attentive Self-supervised Contrastive Learning (ASCL) for plant disease classification
Deep-learning plays a crucial role in large-scale health monitoring of agricultural plants. One of the challenges in plant disease classification is the limited availability of annotated training data, where supervised deep feature learning typically excels. However, traditional deep learning backbo...
Saved in:
Main Authors: | Getinet Yilma, Mesfin Dagne, Mohammed Kemal Ahmed, Ravindra Babu Bellam |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
|
Series: | Results in Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025000106 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Supervised contrastive pre-training models for mammography screening
by: Zhenjie Cao, et al.
Published: (2025-02-01) -
Sequential recommendation based on contrast enhanced time-aware self-attention mechanism
by: YU Yang, et al.
Published: (2025-01-01) -
Graph Knowledge Structure for Attentional Knowledge Tracing With Self-Supervised Learning
by: Zhaohui Liu, et al.
Published: (2025-01-01) -
SSL-MBC: Self-Supervised Learning With Multibranch Consistency for Few-Shot PolSAR Image Classification
by: Wenmei Li, et al.
Published: (2025-01-01) -
Weakly Supervised Nuclei Segmentation with Point-Guided Attention and Self-Supervised Pseudo-Labeling
by: Yapeng Mo, et al.
Published: (2025-01-01)