Deployable Deep Learning for Cross-Domain Plant Leaf Disease Detection via Ensemble Learning, Knowledge Distillation, and Quantization
Accurate leaf disease detection via smartphone-based deep learning holds immense potential for mitigating global crop losses. However, significant deployment challenges persist when transitioning from controlled laboratory environments to real-world agricultural conditions. Despite recent advances,...
Saved in:
| Main Authors: | Mohammad Junayed Hasan, Suvodeep Mazumdar, Sifat Momen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11107438/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Ensemble Learning with Highly Variable Class-Based Performance
by: Brandon Warner, et al.
Published: (2024-09-01) -
An interpretable dynamic ensemble selection multiclass imbalance approach with ensemble imbalance learning for predicting road crash injury severity
by: Kamran Aziz, et al.
Published: (2025-07-01) -
Ensemble transfer learning meets explainable AI: A deep learning approach for leaf disease detection
by: Hetarth Raval, et al.
Published: (2024-12-01) -
The Impact of the SMOTE Method on Machine Learning and Ensemble Learning Performance Results in Addressing Class Imbalance in Data Used for Predicting Total Testosterone Deficiency in Type 2 Diabetes Patients
by: Mehmet Kivrak, et al.
Published: (2024-11-01) -
Distilling Diverse Knowledge for Deep Ensemble Learning
by: Naoki Okamoto, et al.
Published: (2025-01-01)