Classification of Corn Leaf Diseases Using Convolutional Neural Network
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Abstract: The study employs a deep learning model using Convolutional Neural Network (CNN), specifically the ResNet34 model, for detecting common corn leaf diseases in the municipality of Polomolok and General Santos City, Philippines. Corn, a vital staple crop and key economic commodity, is highly susceptible to diseases that threaten productivity and food security, making accurate and efficient detection crucial for effective management and yield optimization. The dataset used in the study comprises 27,146 images categorized into five classifications: Brown Spot, Corn Rust, Leaf Blight, Maize Streak Virus, and Healthy leaves. To improve model generalization and address class imbalance, data augmentation techniques of Basic Image Manipulation such as rotation, scaling, flipping, shearing, and color transformation were applied. The proposed model achieved an overall classification accuracy of 98.67%, with consistently high precision, recall, and F1-scores across all categories, as further validated through confusion matrix analysis that confirmed its strong performance in distinguishing between disease classes. To extend practical utility, an initial version of a mobile application called LeafScan was developed, which allows users to take or upload corn leaf images for disease prediction. These findings demonstrate the effectiveness of deep learning in agriculture, offering a reliable and scalable tool for disease classification and proactive crop management. The study provides a foundation for implementing AI-driven agricultural solutions in the Philippines. Future work could focus on developing a fully functional and user tested mobile expert system application for real-time disease detection and on expanding the model to include additional corn diseases for broader applicability in precision farming.
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