A data-driven fusion of deep learning and transfer learning for orange disease classification
In agriculture, early detection of crop diseases is imperative for sustainability and maximizing yields. Rooted in Agriculture 4.0, our innovative approach combines pre-trained Convolutional Neural Networks (CNNs) models with data-driven solutions to address global challenges related to water scarcity. By integrating the combined $L_{1}/L_{2}$ regularization technique to our model layers, we enhance their flexibility, reducing the risk of the overfitting effect of the model. In the orange dataset used in our experiments, we have 1790 orange images, including a class