AgriNAS: Neural Architecture Search with Adaptive Convolution and Spatial–Time Augmentation Method for Soybean Diseases
Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to...
Saved in:
| Main Authors: | Oluwatoyin Joy Omole, Renata Lopes Rosa, Muhammad Saadi, Demóstenes Zegarra Rodriguez |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | AI |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-2688/5/4/142 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
NAS-CRE: Neural Architecture Search for Context-Based Relation Extraction
by: Rongen Yan, et al.
Published: (2024-11-01) -
Regenerating Regeneration: augmented reality and new models of minor architectural heritage reuse
by: Adolfo F.L. Baratta, et al.
Published: (2018-12-01) -
EGNAS: Efficient Graph Neural Architecture Search Through Evolutionary Algorithm
by: Younkyung Jwa, et al.
Published: (2024-12-01) -
External microstructure of eggs from major owlet moth pests (Lepidoptera: Noctuoidea) associated with Brazilian soybean crops
by: Daniel Ricardo Sosa-Gómez, et al.
Published: (2024-11-01) -
Soybean Looper Chrysodeixis includens (Walker) (Insecta: Lepidoptera: Noctuidae)
by: Ethan T. Carter, et al.
Published: (2018-01-01)