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Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods
Research Article | Published: 18 December 2025
Digital Intelligence in Agriculture Volume 1, Issue 2, 2025: 79-95
Volume 1, Issue 2, Digital Intelligence in Agriculture
Volume 1, Issue 2, 2025
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Digital Intelligence in Agriculture, Volume 1, Issue 2, 2025: 79-95

Open Access | Research Article | 18 December 2025
Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods
by
Prabhat Chandra Shrivastava Prabhat Chandra Shrivastava 1
Mohammad Saheeb Masood Mohammad Saheeb Masood 1
Aman Kumar Aman Kumar 1
Ramesh Kumar Bhukya Ramesh Kumar Bhukya 2 *
Harmanjit Kaur Harmanjit Kaur 3
Rupesh Kumar Rupesh Kumar 4
Derrick Mario Denis Derrick Mario Denis 5
1 Department of Electronics and Communication, University of Allahabad, Prayagraj, Uttar Pradesh 211002, India
2 Department of Electronics and Communication Engineering, Indian Institute of Information Technology, Allahabad, Prayagraj, Uttar Pradesh 211015, India
3 Department of Botany, University of Allahabad, Prayagraj, Uttar Pradesh 211002, India
4 Yamaha Motor Solution Private Limited, Faridabad, Haryana 121003, India
5 Department of Irrigation and Drainage Engineering, Vaugh Institute of Agricultural Engineering and Technology, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, Uttar Pradesh 211007, India
* Corresponding Author: Ramesh Kumar Bhukya, [email protected]
DOI: 10.62762/DIA.2025.672386
ARK: ark:/57805/dia.2025.672386
Received: 23 October 2025, Accepted: 23 November 2025, Published: 18 December 2025  
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Abstract
Maize productivity in India, a major global producer, is severely threatened by leaf diseases. Accurate identification of Common Rust (CR), Northern Corn Leaf Blight (NCLB), and Gray Leaf Spot (GLS) remains challenging with traditional methods. This study evaluated traditional and ensemble-based classifiers for classifying these diseases alongside healthy (HL) leaves. Using accuracy, precision, recall, and F1-score, we assessed k-NN, DT, RF, ETs, AdaBoost, SGD, GB, XGBoost, LightGBM, and a Stacking model on a four-class dataset. Ensemble methods demonstrated clear superiority. The Stacking model achieved the highest accuracy (98.50%), followed by LightGBM (98.46%) and XGBoost (98.01%). Among conventional models, ETs (97.38%) and RF (96.93%) outperformed others. While HL was consistently identified, GLS proved most challenging, especially for non-ensemble methods. The results underscore the robustness and superior generalization capability of tree-based ensemble methods for imbalanced multi-class disease classification.
Graphical Abstract
Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods
Keywords
maize leaf disease detection
image-based disease classification
plant pathology informatics
agricultural data analytics
artificial intelligence in agriculture
precision agriculture
machine learning
ensemble learning
Data Availability Statement
The dataset used in this study is an organized subset of the publicly available PlantVillage dataset hosted on Kaggle (https://www.kaggle.com/datasets/abdallahalidev/plantvillage-dataset). The complete PlantVillage dataset, originally introduced by Hughes and Salathé, contains approximately 217,000 images that span 38 categories of healthy and diseased plant leaves, and can be accessed via the original source at https://github.com/spMohanty/PlantVillage-Dataset.
Funding
This work was supported by the Telecommunications Consultants India Limited (TCIL), a Government of India company under administrative control of the Department of Telecommunications (DoT), as part of the prestigious “100 5G Use Case Labs” initiative.
Conflicts of Interest
The authors declare no conflicts of interest. Rupesh Kumar is an employee of Yamaha Motor Solution Private Limited, Faridabad, Haryana 121003, India.
Ethical Approval and Consent to Participate
Not applicable.
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APA Style
Shrivastava, P. C., Masood, M. S., Kumar, A., Bhukya, R. K., Kaur, H., Kumar, R., & Denis, D. M. (2025). Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods. Digital Intelligence in Agriculture, 1(2), 79–95. https://doi.org/10.62762/DIA.2025.672386
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TY  - JOUR
AU  - Shrivastava, Prabhat Chandra
AU  - Masood, Mohammad Saheeb
AU  - Kumar, Aman
AU  - Bhukya, Ramesh Kumar
AU  - Kaur, Harmanjit
AU  - Kumar, Rupesh
AU  - Denis, Derrick Mario
PY  - 2025
DA  - 2025/12/18
TI  - Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods
JO  - Digital Intelligence in Agriculture
T2  - Digital Intelligence in Agriculture
JF  - Digital Intelligence in Agriculture
VL  - 1
IS  - 2
SP  - 79
EP  - 95
DO  - 10.62762/DIA.2025.672386
UR  - https://www.icck.org/article/abs/DIA.2025.672386
KW  - maize leaf disease detection
KW  - image-based disease classification
KW  - plant pathology informatics
KW  - agricultural data analytics
KW  - artificial intelligence in agriculture
KW  - precision agriculture
KW  - machine learning
KW  - ensemble learning
AB  - Maize productivity in India, a major global producer, is severely threatened by leaf diseases. Accurate identification of Common Rust (CR), Northern Corn Leaf Blight (NCLB), and Gray Leaf Spot (GLS) remains challenging with traditional methods. This study evaluated traditional and ensemble-based classifiers for classifying these diseases alongside healthy (HL) leaves. Using accuracy, precision, recall, and F1-score, we assessed k-NN, DT, RF, ETs, AdaBoost, SGD, GB, XGBoost, LightGBM, and a Stacking model on a four-class dataset. Ensemble methods demonstrated clear superiority. The Stacking model achieved the highest accuracy (98.50%), followed by LightGBM (98.46%) and XGBoost (98.01%). Among conventional models, ETs (97.38%) and RF (96.93%) outperformed others. While HL was consistently identified, GLS proved most challenging, especially for non-ensemble methods. The results underscore the robustness and superior generalization capability of tree-based ensemble methods for imbalanced multi-class disease classification.
SN  - 3069-3187
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Shrivastava2025Robust,
  author = {Prabhat Chandra Shrivastava and Mohammad Saheeb Masood and Aman Kumar and Ramesh Kumar Bhukya and Harmanjit Kaur and Rupesh Kumar and Derrick Mario Denis},
  title = {Robust Detection of Maize Foliage Fungal Diseases using Tree-Based Ensemble Methods},
  journal = {Digital Intelligence in Agriculture},
  year = {2025},
  volume = {1},
  number = {2},
  pages = {79-95},
  doi = {10.62762/DIA.2025.672386},
  url = {https://www.icck.org/article/abs/DIA.2025.672386},
  abstract = {Maize productivity in India, a major global producer, is severely threatened by leaf diseases. Accurate identification of Common Rust (CR), Northern Corn Leaf Blight (NCLB), and Gray Leaf Spot (GLS) remains challenging with traditional methods. This study evaluated traditional and ensemble-based classifiers for classifying these diseases alongside healthy (HL) leaves. Using accuracy, precision, recall, and F1-score, we assessed k-NN, DT, RF, ETs, AdaBoost, SGD, GB, XGBoost, LightGBM, and a Stacking model on a four-class dataset. Ensemble methods demonstrated clear superiority. The Stacking model achieved the highest accuracy (98.50\%), followed by LightGBM (98.46\%) and XGBoost (98.01\%). Among conventional models, ETs (97.38\%) and RF (96.93\%) outperformed others. While HL was consistently identified, GLS proved most challenging, especially for non-ensemble methods. The results underscore the robustness and superior generalization capability of tree-based ensemble methods for imbalanced multi-class disease classification.},
  keywords = {maize leaf disease detection, image-based disease classification, plant pathology informatics, agricultural data analytics, artificial intelligence in agriculture, precision agriculture, machine learning, ensemble learning},
  issn = {3069-3187},
  publisher = {Institute of Central Computation and Knowledge}
}
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https://www.portico.org/publishers/icck/