CornHealthNet: Automated Corn Leaf Disease Classification Using ResNet50
Keywords:
CornHealthNet, Corn Leaf Disease Classification, ResNet50, Deep Learning, Transfer Learning, Precision Agriculture, Plant Disease Detection, Image-Based ClassificationAbstract
Early and accurate identification of the diseases of corn is important to ensuring the health of the crop, maximizing yield potential, and supporting sustainability in agriculture. This paper proposes CornHealthNet, a powerful automated classification of corn leaf diseases based on a fine-tuned architecture, ResNet50, which is built on the deep learning network. The model is trained and tested using an open corn leaf disease dataset, downloaded from the Kaggle platform, which consists of images of healthy leaves, and common rust, grey leaf spot and northern leaf blight. Transfer learning is used for leveraging the pre-trained ImageNet weights and let perform feature extraction efficiently and quickly converging. Data augmentation methods and regularization techniques are also applied to improve generalization and prevent overfitting. Comprehensive experimental evaluations using standard metrics such as precision, recall, F1-score, confusion matrix and ROC--AUC shows the effectiveness of the proposed approach. CornHealthNet has an impressive 99% classification accuracy which is better than several baseline deep learning models. The results demonstrate the great discriminative ability and reliability of the proposed framework, and thus it is a good solution for real-time, automated diagnosis of corn leaf diseases in precision agriculture systems.
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