Real-Time Sugarcane Disease Prediction and Yield Enhancement Using an Internet of Things-Enabled Deep Learning Framework

The proposed EADLF model, integrating Swin Transformer and ICLO-optimised DenseNet201, achieved a top F1-score of 0.945 and an AUC of 0.93, outperforming all baseline models in sugarcane disease classification.

Authors

Amol Dhumane (ORCID: https://orcid.org/0000-0002-7529-4125)
Tushar Jadhav (ORCID: https://orcid.org/0000-0001-9397-9307)
Parikshit N. Mahalle
Sumit Arun Hirve (ORCID: https://orcid.org/0000-0003-4831-647X)
Satyajit Pangaonkar
Snehlata Wankhade
Ramesh S. Lavhe

Publication Details

Journal: ES Food & Agroforestry
Published: 2025-01-01
DOI: https://doi.org/10.30919/faf1756
Primary Topic: Sugarcane Cultivation and Processing
Type: article
Field-Weighted Citation Impact: 0.00

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article

Real-Time Sugarcane Disease Prediction and Yield Enhancement Using an Internet of Things-Enabled Deep Learning Framework

Amol Dhumane, Tushar Jadhav, Parikshit N. Mahalle, Sumit Arun Hirve et al.

Jan 1, 2025

ES Food & Agroforestry

Sugarcane Cultivation and Processing

article

Real-Time Sugarcane Disease Prediction and Yield Enhancement Using an Internet of Things-Enabled Deep Learning Framework

Amol Dhumane, Tushar Jadhav, Parikshit N. Mahalle, Sumit Arun Hirve, Satyajit Pangaonkar, Snehlata Wankhade, Ramesh S. Lavhe

January 1, 2025article en

Abstract

ES Food & Agroforestry

Openalex Percentile: Top 80%

Sugarcane Cultivation and Processing

DOI Open Access PDF OpenAlex

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Real-Time Sugarcane Disease Prediction and Yield Enhancement Using an Internet of Things-Enabled Deep Learning Framework — Amol Dhumane, Tushar Jadhav, et al. · ES Food & Agroforestry (2025) | TGRS Research Map | TGRS