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

This dataset provides the necessary resources to further explore 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 research for disease recognition, enabling improved automated disease detection in maize crops. The dataset includes Maize Dataset Manifest excell data, which is A structured rows of Excel sheet that provides the dataset's contents, including metadata for each image file and important information about dataset organization. It contains also Generated Raw Data for Maize Leaf Disease in PDF format, which is a detailed report generated from Python containing raw data on maize leaf disease used for model training and validation. Therefore, this row data assists researchers in developing deep learning models for scalable maize disease detection.本数据集提供了必要的研究资源，以供进一步探索经统计验证的堆叠集成模型——该模型结合了卷积神经网络（Convolutional Neural Networks，CNNs）与视觉Transformer（Vision Transformers，ViTs），用于玉米病害分类。本数据集旨在为病害识别相关研究提供支撑，助力实现玉米作物自动化病害检测技术的优化升级。 本数据集包含玉米数据集清单Excel数据：该结构化Excel表格记录了数据集的全部内容，涵盖每张图像文件的元数据以及数据集组织的相关重要信息。此外，数据集还包含PDF格式的玉米叶片病害原始生成数据，该文件为通过Python生成的详细报告，涵盖了用于模型训练与验证的玉米叶片病害原始数据。 因此，该原始数据可帮助研究人员开发可规模化应用的玉米病害检测深度学习模型。