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In recent years, the application of deep learning techniques for plant disease classification has become increasingly important for smart agriculture. Early classification and treatment of plant diseases are crucial for maintaining the quality and quantity of crops, and deep learning algorithms have the potential to provide accurate and efficient solutions to this problem. In this study, we used two datasets collected from Mendeley for coffee leaf disease classification. The first dataset contained two classes of images, while the second dataset contained the remaining three types of images. The two datasets were combined to create a more robust dataset for training the two-transfer learning and CNN model. We update the pre-trained model MobileNet and RestNet50 by adding a flattened and dense layer to classify the five types of coffee diseases. The model was trained using 100 epochs, and the performance was …