Abstract :

With The Plant Diseases Pose A Significant Threat To Global Agriculture, Leading To Reduced Crop Yield And Economic Losses. Traditional Disease Detection Methods, Such As Manual Inspection And Chemical Analysis, Are Time-consuming, Labor-intensive, And Require Expert Knowledge. To Address These Limitations, This Research Presents A Plant Leaf Disease Detection System Using Ensemble Learning And Explainable AI (XAI). The Proposed Model Combines Multiple Machine Learning Classifiers, Such As Convolutional Neural Networks (CNNs), Random Forest, And Support Vector Machines (SVMs), To Enhance Classification Accuracy And Robustness. By Leveraging Ensemble Learning, The System Ensures Improved Generalization And Adaptability Across Different Plant Species And Disease Types. Additionally, XAI Techniques, Such As Gradient-weighted Class Activation Mapping (Grad-CAM) And SHapley Additive ExPlanations (SHAP),** Are Integrated To Provide Interpretability, Allowing Users To Understand The Decision-making Process Of The Model. The Dataset Used Includes Images Of Diseased And Healthy Leaves, And Performance Evaluation Is Conducted Using Accuracy, Precision, Recall, And F1-score. Experimental Results Demonstrate That The Ensemble Learning Approach Outperforms Single Models, Achieving High Classification Accuracy While Maintaining Explainability. The Proposed System Has Applications In Precision Agriculture, Automated Disease Monitoring, And Decision Support For Farmers. Future Enhancements Include Real-time Deployment On Mobile Applications And Integration With IoT-based Smart Farming Solutions.

Published:

27-3-2026

Issue:

Vol. 26 No. 3 (2026)

Page Nos:

783-787

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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