Attention-enhanced corn disease diagnosis using few-shot learning and VGG16
Plant Disease Detection in the early stage is paramount. Traditionally, it was done manually by the farmers, which is a laborious and time-intensive task. With the advent of AI, Machine Learning and Deep Learning methods are used to detect and categorize plant diseases. However, they rely on extensi...
Saved in:
Main Authors: | Ruchi Rani, Jayakrushna Sahoo, Sivaiah Bellamkonda, Sumit Kumar |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-06-01
|
Series: | MethodsX |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016125000202 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
EMNet: A Novel Few-Shot Image Classification Model with Enhanced Self-Correlation Attention and Multi-Branch Joint Module
by: Fufang Li, et al.
Published: (2025-01-01) -
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging
by: Charilaos Papaioannou, et al.
Published: (2025-01-01) -
Optimization of VGG-16 Accuracy for Fingerprint Pattern Imager Classification
by: Agus Andreansyah, et al.
Published: (2025-01-01) -
A Few-Shot Knowledge Graph Completion Model With Neighbor Filter and Affine Attention
by: Hongfang Gong, et al.
Published: (2025-01-01) -
Multilevel support-assisted prototype optimization network for few-shot medical segmentation of lung lesions
by: Yuan Tian, et al.
Published: (2025-01-01)