Maize Disease Detection: Multi-Format Image Analysis Using Deep Learning for Precise Diagnosis

Abstract

This research introduces a comprehensive deep learning strategy designed for identifying maize diseases, utilizing RGB, grayscale, and segmented images to enhance classification precision and dependability. By utilizing Convolutional Neural Networks (CNNs), the model was trained on a dataset featuring prominent maize diseases, such as Cercospora leaf spot, Common rust, Northern leaf blight, along with healthy maize foliage. The study implements a multi-format ensemble approach that takes advantage of a majority voting system to merge predictions from all image formats, resulting in a remarkable classification accuracy of 94.3%. This approach surpasses models based on single formats and offers a scalable, instantaneous solution for the early detection of maize diseases. The integration of image processing, feature extraction, and deep learning guarantees strong performance across various disease types, making it a valuable resource for agricultural practices and early intervention. The results emphasize the potential to improve crop management strategies, especially in areas where prompt disease detection is vital for preserving crop yield and quality.