Class‐specific data augmentation for plant stress classification
Abstract Data augmentation is a powerful tool for improving deep learning‐based image classifiers for plant stress identification and classification. However, selecting an effective set of augmentations from a large pool of candidates remains a key challenge, particularly in imbalanced and confoundi...
Saved in:
| Main Authors: | Nasla Saleem, Aditya Balu, Talukder Zaki Jubery, Arti Singh, Asheesh K. Singh, Soumik Sarkar, Baskar Ganapathysubramanian |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-12-01
|
| Series: | Plant Phenome Journal |
| Online Access: | https://doi.org/10.1002/ppj2.20112 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Multi‐sensor and multi‐temporal high‐throughput phenotyping for monitoring and early detection of water‐limiting stress in soybean
by: Sarah E. Jones, et al.
Published: (2024-12-01) -
Zero‐shot insect detection via weak language supervision
by: Benjamin Feuer, et al.
Published: (2024-12-01) -
Optimizing navigation and chemical application in precision agriculture with deep reinforcement learning and conditional action tree
by: Mahsa Khosravi, et al.
Published: (2025-12-01) -
Persistent monitoring of insect-pests on sticky traps through hierarchical transfer learning and slicing-aided hyper inference
by: Fateme Fotouhi, et al.
Published: (2024-11-01) -
Genomic and phenomic prediction for soybean seed yield, protein, and oil
by: Liza Van der Laan, et al.
Published: (2025-03-01)