In the most recent years, many deep learning models have been used to identify and classify diseases of plant leaves by inputting plant leaf images as input to the model. However, there is still a gap in research on how to improve the accuracy of the deep learning models of plant leaf diseases. This thesis is about investigating various techniques for improving the MobileNetV2 model's accuracy for plant disease detection in leaves and classification. These techniques involved adjusting the learning rate, adding additional layers, and various data-augmented operations. The results of this thesis have shown that these techniques can significantly improve the accuracy of the model, and the best results can be achieved by using random rotation and crop data augmentation. After adding random rotation and crop data augmentation to the model, it achieved an accuracy of 94%, a precision of 91%, a recall of 96%, and an F1-score of 95%. This shows that the proposed techniques can be used to improve the accuracy of plant leaf disease detection and classification models, which can help farmers identify and treat plant diseases.