Crop diseases can greatly affect crop yields, and early detection is thus important to reduce losses. Recently, different automatic plant disease recognition systems have been developed. This paper introduces four deep learning models—VGG16, EfficientNetB0, ResNet-50, and GoogleNet to recognize four rice diseases based on plant leaf images. The models were trained and tested using different public datasets, including the Rice Leaf Disease Image dataset in Mendeley. The Dense Layer model of VGG16 has an accuracy rate of 99.747%, while that of VGG16 2D-CNN is 99.916%. The Dense Layer model also recorded a validation loss of 1.3880 with a test accuracy of 27.004%, while the 2D-CNN EfficientNetB0 model has a validation loss of 1.3830 with the same test accuracy. ResNet-50 with Dense Layer decreased the validation loss to 0.8121 and had a test accuracy of 70.211%, whereas ResNet-50 2D-CNN achieved a validation loss of 0.7238 with a test accuracy of 69.789%. Lastly, the GoogleNet model with Dense Layer provided 99.916% accuracy, and the GoogleNet 2D-CNN model had 99.831% accuracy. The outcomes show that such sophisticated deep learning models diagnose rice diseases accurately, providing intelligent agriculture solutions for disease detection at the early stage. Farmers can be assisted by deploying AI-based systems to ensure optimal crop yields and quality. Overall, this research proves that plant disease diagnosis can be done highly effectively using AI-based approaches and presents a green solution to an important agricultural issue.