Agriculture plays a vital role in ensuring the economic stability of a country. This has led to a significant amount of money spent on research and innovation to improve crop yield and sustainability. A significant challenge for farmers worldwide is effectively diagnosing and managing diseases that threaten plant leaf health. Plant diseases significantly reduce crop yields if left unchecked at an appropriate stage. Plants show symptoms of diseases on their leaves in many ways - color, shape, size, and texture. By understanding these symptoms visually, farmers identify the diseases. In an effort to automate the diagnosis, CNN models are used. Capturing all of these variationsin a single CNN model is challenging due to the complex features of the leaves. To alleviate this issue, this research proposes using transfer learning to quickly train and diagnose plant diseases. Additionally, two ensembled models are also proposed. The performance of the models is analyzed using the Rice Leaf Disease dataset. The performance of the proposed ensembled models and the pre-trained models are evaluated using the metrics - accuracy, precision, recall, and F1-Score. The experimental results show that the ensembled model featuring EfficientNetV2, DenseNet- 201, and VGG-19 model performs the best.