Peer-reviewedCC BY-NC-ND 4.0Scientific Reports
Explainable plant disease classification and severity assessment using MobileNetV3-large for crop health monitoring
Related
- Circadian Derived Features for Early Discrimination Across Insomnia Severity Levels: At Least 8 Weeks of Monitoring Are Needed for Clinically Meaningful Assessment
- SeverYX: An ensemble framework for robust variant severity classification
- A hybrid SE-ResNet50 deep learning framework for high-accuracy and explainable cotton leaf disease classification
- Large Language Model Agents for Evidence Based Genetic Disease Severity Classification
- Early prediction of moderate-to-severe BPD or death in preterm infants using minute-level respiratory monitoring and explainable machine learning
BibTeX
@article{gaur2026explainable,
title = {{Explainable plant disease classification and severity assessment using MobileNetV3-large for crop health monitoring}},
author = {Kuntal Gaur and Ritik Mittal and Bisant Chowdhury and Ravi Raj and Niyaz Ahmad Wani and Pradeep Kumar},
journal = {Scientific Reports},
year = {2026},
month = oct,
doi = {10.1038/s41598-026-72759-6},
url = {https://www.nature.com/articles/s41598-026-72759-6},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Explainable plant disease classification and severity assessment using MobileNetV3-large for crop health monitoring
AU - Kuntal Gaur
AU - Ritik Mittal
AU - Bisant Chowdhury
AU - Ravi Raj
AU - Niyaz Ahmad Wani
AU - Pradeep Kumar
PY - 2026
DA - 2026/10/09
JO - Scientific Reports
DO - 10.1038/s41598-026-72759-6
UR - https://www.nature.com/articles/s41598-026-72759-6
SN - 2045-2322
ER -