Evaluation of Different Few-Shot Learning Methods in the Plant Disease Classification Domain
Early detection of plant diseases is crucial for agro-holdings, farmers, and smallholders. Various neural network architectures and training methods have been employed to identify optimal solutions for plant disease classification. However, research applying one-shot or few-shot learning approaches,...
Saved in:
Main Author: | Alexander Uzhinskiy |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-01-01
|
Series: | Biology |
Subjects: | |
Online Access: | https://www.mdpi.com/2079-7737/14/1/99 |
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) -
Few-Shot Contrail Segmentation in Remote Sensing Imagery With Loss Function in Hough Space
by: Junzi Sun, et al.
Published: (2025-01-01) -
Enhancing Essay Scoring: An Analytical and Holistic Approach With Few-Shot Transformer-Based Models
by: Tahira Amin, et al.
Published: (2025-01-01) -
Attention-enhanced corn disease diagnosis using few-shot learning and VGG16
by: Ruchi Rani, et al.
Published: (2025-06-01) -
LC-Protonets: Multi-Label Few-Shot Learning for World Music Audio Tagging
by: Charilaos Papaioannou, et al.
Published: (2025-01-01)