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@article{205923,
        author = {Miss. Kale Shweta Ramesh and Prof. Dr. Sushil Venkatesh Kulkarni},
        title = {Plant Leaf Disease Detection Using Ensemble Learning and Explainable AI},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {8805-8812},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205923},
        abstract = {Agriculture plays a vital role in ensuring food security and economic stability worldwide. Plant diseases significantly affect crop productivity and quality, leading to substantial financial losses for farmers. Traditional disease diagnosis methods rely heavily on manual inspection by agricultural experts, which can be time-consuming, subjective, and inefficient for large-scale farming applications. This paper presents an intelligent plant leaf disease detection framework based on Ensemble Learning and Explainable Artificial Intelligence (XAI). The proposed system combines the predictive capabilities of multiple deep learning models, including CNN to enhance classification accuracy and robustness. Ensemble learning integrates the strengths of individual models while minimizing their weaknesses, resulting in improved disease recognition performance. Furthermore, Explainable AI techniques such as Gradient-weighted Class Activation Mapping (Grad-CAM) are incorporated to visualize and interpret the decision-making process of the ensemble model, thereby increasing transparency and user trust. Experimental evaluation is conducted using a publicly available plant leaf disease dataset comprising multiple crop species and disease categories. The results demonstrate that the proposed ensemble framework achieves superior classification accuracy, precision, recall, and F1-score compared to individual deep learning models. The integration of explainability further assists farmers and agricultural experts in understanding disease symptoms and validating model predictions.},
        keywords = {Plant Disease Detection, Ensemble Learning, Explainable Artificial Intelligence (XAI), Deep Learning, Convolutional Neural Networks, Grad-CAM, Precision Agriculture, PlantVillage Dataset, Crop Health Monitoring, Image Classification.},
        month = {June},
        }