Classification of Corn Leaf Disease Images Using Convolutional Neural Network Algorithm
DOI:
https://doi.org/10.57152/predatecs.v3i2.2105Keywords:
Convolutional Neural Network, Corn Leaf Disease, Deep Learning, Plant Disease DetectionAbstract
Corn leaf diseases can reduce crop yields and cause financial losses, thus requiring accurate and objective classification methods. This study aims to classify four corn leaf conditions, namely Blight, Common Rust, Gray Leaf Spot, and healthy leaves, using a Convolutional Neural Network (CNN) approach based on image processing. A systematic comparative evaluation was conducted on three CNN architectures, namely MobileNetV2, ResNet50V2, and DenseNet201, by examining the effect of architecture-optimizer pairs using Adam and RMSprop to determine the optimal model configuration. The results showed that the proposed approach was effective in classifying corn leaf diseases, with the highest accuracy of 93% achieved by the combination of DenseNet201 and the Adam optimizer. This study contributes by providing a structured comparative analysis of the performance of CNN architectures and optimizers as a reference for the development of more accurate and efficient early detection systems for plant diseases.
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