مرکز منطقه ای اطلاع رسانی علوم و فناوری Journal of Information Systems and Telecommunication (JIST) 2322-1437 14 1 2026 5 5 Maize Leaf Disease Detection using Deep Learning Models and a DenXNet Ensemble Model Maize Leaf Disease Detection using Deep Learning Models and a DenXNet Ensemble Model 25 38 10.66224/jist.50993.14.1.25 en Meghna Gupta Amity University, Uttar Pradesh, INDIA Sarika Jain HNB Garhwal University Manoj Kumar University of Wollongong, Dubai 2025 7 28 <p>Maize(Corn) is considered an important crop worldwide for global production after wheat and rice. It provides food, ethanol, carbohydrates, vitamins, and other resources, making it essential to human civilization. However, it does face numerous difficulties, including pest infestations, deteriorating soil, scarce water supplies, and climate change, resulting in various yield losses. This research introduces an efficient deep learning framework for the accurate identification of maize leaf disease. Four convolutional neural network architectures, DenXNet- MobileNet, Xception, DenseNet169, and DenseNet201- were trained and evaluated using both original and augmented datasets. To ensure fairness and eliminate data leakage, the original data is divided into train, validation, and test sets, and then augmented, whereas a stratified five-fold cross-validation strategy was applied to non-augmented data. A comprehensive ablation study was conducted to compare model performance with and without augmentation and across different ensemble configurations. The study explored soft ensemble modelling using combinations of two and four base models. Among all configurations, the four-model ensemble, DenXNet, achieved the highest accuracy of 98.46% and consistency across folds, outperforming individual and partial ensembles. The proposed method demonstrates improved precision, reduced over fitting, and strong adaptability for real-world agricultural disease detection tasks.</p> http://jist.ir/fa/Article/Download/50993