earticle

영어

Precision agriculture increasingly relies on advanced technologies to enhance sustainability and productivity. Among these, deep learning and machine learning play a critical role in developing automated systems capable of accurately identifying plant diseases. This study presents a comparative analysis of various deep learning models for plant disease classification. Specifically, we employ transfer learning using pre-trained architectures such as VGG16, ResNet-50, DenseNet-121, and EfficientNet-B0, alongside a custom convolutional neural network (CNN) trained from scratch. The models are evaluated using a dataset containing images of both healthy and diseased plants. Experimental results indicate that transfer learning models outperform the custom CNN, with DenseNet-121 and EfficientNet-B0 offering the optimal balance between computational efficiency and classification accuracy. These findings underscore the potential of deep learning techniques to support precision agriculture by enabling faster, more accurate, and scalable disease detection—reducing the reliance on manual inspection and facilitating timely interventions.