A Deep Learning Approach for Disease Detection in Maize Crops
DOI:
https://doi.org/10.25299/itjrd.2025.22875Keywords:
Data Classification, Deep Learning, Convolutional Neural Network, TensorFlow, Random ForestAbstract
Maize is a vital global food crop, yet its production faces significant threats from leaf diseases that compromise yield and quality. In Nigeria, such diseases lead to an estimated 20–30% loss in annual maize production, equating to billions in economic damage. This research presents the development and deployment of a machine learning-based maize leaf disease classification model using deep learning techniques, specifically Convolutional Neural Networks (CNNs) with the MobileNetV2 architecture. A dataset of 12,000 maize leaf images encompassing four classes, namely Common Rust, Gray Leaf Spot, Leaf Blight, and Healthy was used. Leveraging TensorFlow, the model was trained and fine-tuned using the Nadam optimizer, which facilitated faster convergence and high classification accuracy, achieving a peak validation accuracy of 99.48%, with an F1 score of 0.9411. Compared to traditional algorithms such as Random Forest, MobileNetV2 demonstrated superior performance across all evaluation metrics. The trained model was deployed in a user-friendly web application, allowing for real-time disease diagnosis via image uploads. This approach not only enhances early detection but also contributes to precision agriculture and sustainable crop management.
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