| Issue |
EPJ Web Conf.
Volume 380, 2026
International Conference on Information Systems and Communication Technologies (ICISCT’25)
|
|
|---|---|---|
| Article Number | 02012 | |
| Number of page(s) | 11 | |
| Section | Artificial Intelligence, Advanced Control Systems, and Energy Management | |
| DOI | https://doi.org/10.1051/epjconf/202638002012 | |
| Published online | 03 August 2026 | |
https://doi.org/10.1051/epjconf/202638002012
Enhancing Leaf Disease Classification with Feature Fusion and Ensemble Learning
1 IRDA Team, IRDA Laboratory, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco
2 Department of Computer Science, Rabat IT Center, ENSIAS, Mohammed V University in Rabat, Rabat, Morocco
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Published online: 3 August 2026
Abstract
Accurate and efficient detection of plant diseases is vital for enhancing agricultural production and safeguarding food safety. In this insightful study, we dive into a cutting-edge learning technique that integrates various features extracted from leaf images. By employing support vector machines (SVM) alongside the innovative Bootstrap aggregating (bagging) technique, we meticulously assess the contribution of each feature and explore the power of probability-weighted fusion.
Our findings reveal that embracing probability fusion significantly elevates the model's overall performance, surpassing the traditional method of merely equalizing features. This approach underscores the profound impact that multi-feature weighted fusion can have, paving the way for a more robust and effective classification model.
Key words: Plant disease classification / weighted Probability Fusion / Feature Fusion / Support Vector Machine / Bagging
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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