Ensemble Learning-based Potato Leaf Disease Classification Using DenseNet201 and MobileNetV2

Burhan Ahmad — 2026-01-26

Early and late blight are major threats to potato crops and can cause significant losses for farmers. Early disease classification is essential for quick and appropriate treatment. This study proposes an ensemble learning approach by combining DenseNet201 and MobileNetV2 architectures to classify potato leaf diseases from digital images. The dataset used consists of 2,152 potato leaf images and is processed through normalization, augmentation, and image resizing stages. The ensemble model was trained with optimized parameters and evaluated using accuracy, precision, recall, and F1-score. The test results showed an accuracy of 99.56%, with precision, recall, and F1- score values of 99.56% each. Demonstrated improved performance compared to single CNN models on the evaluated dataset, and offers an accurate and efficient solution for disease detection in the agricultural sector.

Ensemble Deep Learning: A State-Of-The-Art Comprehensive Review

Mahmoud Abdel Samie — 2026-01-26

Ensemble learning has been a cornerstone of machine learning, providing improved predictive performance and robustness by combining multiple models. However, in the era of deep learning, the landscape of ensemble techniques has rapidly evolved, influenced by advances in neural architectures, training models, and practical application requirements. This review provides a state-of-the-art survey of ensemble deep learning approaches, focusing on recent developments of ensemble methods. We introduce a classification of ensemble strategies based on model diversity, fusion mechanisms, and task alignment, and highlight emerging techniques such as attention-based ensemble fusion, neural architecture search-based ensembles, and large ensembles of language or vision models. The review also examines theoretical foundations, practical tradeoffs, and domain-specific adaptations in some fields. Compiling state-of-the-art benchmarks, we evaluate ensemble performance in terms of accuracy, efficiency, robustness, and interpretability. We also identify key challenges such as scalability, overfitting, and deployment limitations and present open research directions, including ensemble learning for continuous learning, federated learning, and learning from scratch. By connecting key insights with current trends, this review aims to guide researchers and practitioners in designing and implementing ensemble deep learning systems to address the next generation of AI challenges.

Journal of Information System Exploration and Research

Ensemble Learning-based Potato Leaf Disease Classification Using DenseNet201 and MobileNetV2

Ensemble Deep Learning: A State-Of-The-Art Comprehensive Review