Everybody’s life is impacted by agriculture, and crop-related disease outbreaks are a constant cause of stress for farmers all over the world. Unfavorably, the incidence of agricultural crop diseases is on the rise, resulting in considerable loss in revenue and economy. Rice is a major crop cultivated in India, but it faces numerous diseases throughout its growth stages. Rice crop leaf disease is a widespread problem, which has a negative impact on the efficiency, output, and productivity of the rice plants. Timely recognition along with precise determination and identification of bugs and illness in the rice crop, are essential for addressing this problem. The latest advances in the field of deep learning indicate that Convolutional Neural Network (CNN) can be very beneficial in these kinds of scenarios. In this paper, we used a custom CNN and ResNet-50 DL models to classify and diagnose diverse kinds of rice crop leaf diseases. An image dataset from Kaggle, which contains 5932 images of four types of rice crop leaf diseases, has been utilized to judge the outcomes. The suggested hybrid architecture, which is a combination of custom CNN and ResNet-50 architecture, consists of convolution, pooling, and dense layers. Following any required pre-processing and augmentation, the training and testing of images is carried out by the proposed architecture, and an accuracy of 95.52% is achieved on the testing data, which is better than various machine learning methods.