<![CDATA[Tomato leaves (Solanum lycopersicum) are susceptible to various diseases that significantly impact both yield and quality in agricultural production. To enhance the effectiveness of precision agriculture, deep learning-based image classification techniques have emerged as reliable tools for automatically detecting disease symptoms. In this study, two deep learning models are investigated for this purpose: a custom-built Convolutional Neural Network (CNN) and the ResNet18 architecture, which leverages transfer learning. The experimental workflow encompasses preprocessing of input images, data augmentation strategies, architectural development of the custom CNN, and the fine-tuning phase of the ResNet18 model. The evaluation was conducted on a validation dataset comprising 1,000 images evenly distributed across ten disease categories. Results show that the ResNet18 model attained a validation accuracy of 74%, whereas the custom CNN model achieved 55%. While the latter demonstrated lower predictive performance, it offers advantages in computational simplicity and execution speed. These findings suggest that transfer learning with ResNet18 is more suitable for complex, multi-class classification problems on limited datasets, whereas the lightweight CNN model may be better positioned for deployment on low-resource, edge-based agricultural systems.]]>
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