Maize Leaf Disease Prediction Using Deep Learning

This research addresses the critical challenge of maize crop diseases by leveraging Convolutional Neural Networks (CNNs) for automated disease detection, overcoming the inefficiencies of traditional manual methods. The proposed CNN-based framework effectively classifies diseases such as Gray Leaf Spot, Common Rust, and Northern Leaf Blight by incorporating advanced preprocessing techniques like data augmentation and normalization to enhance accuracy and robustness. By enabling early disease detection, the system supports timely interventions, reduces crop losses, and promotes sustainable farming practices. Future directions include expanding datasets, integrating IoT for real-time monitoring, and exploring advanced DL architectures to further optimize performance, contributing to global food security and modernized agriculture.