A dataset for A Statistically Validated Stacking Ensemble of CNNs and Vision Transformer for Robust Maize Disease Classification

This dataset provides the resources to a statistically validated stacking ensemble model combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) for maize disease classification. The dataset is designed to support our research, enabling improved automated disease detection in maize crops.The dataset includes the following:1. Data for Maize Leaf Classification RAR data: A 15719 labeled images for maize leaf classification. These images are critical for training CNNs and ViTs to identify various maize diseases based on leaf appearances.2. Generated Raw Data for Maize Leaf Disease pdf data: A detailed PDF report generated from Python containing raw data on maize leaf disease used for model training and validation.3. Maize Dataset Manifest excel data: A structured 15719 rows of Excel sheet that provides the dataset's contents, including metadata for each image file and important information about dataset organization.4. Python Source Code of Maize Disease Classification zip data: A ZIP file containing the source code necessary to classify the maize disease process. The code implements the stacking ensemble model and includes scripts for training, validation, and evaluation, along with dependencies for running the model.Therefore, this row data assists researchers in developing deep learning models for scalable maize disease detection.