Producing a high quality of crops is a major contribution of agriculture to the economy of any country. Plant disease identification is one of the most important aspects of maintaining a nation with a developed agricultural economy. The effectiveness of Convolutional Neural Networks—specifically, the ResNet-50 architecture—in the context of paddy crop disease detection is examined in this study. The study utilizes a comprehensive dataset comprising images of diseased and healthy paddy plants for training and testing the ResNet-50 model. Through rigorous experimentation, the CNN demonstrates remarkable accuracy, precision, and recall in identifying various paddy crop diseases. By demonstrating the ResNet-50 CNN model’s greater performance over conventional techniques, the study adds to the body of current material. The detailed analysis underscores the capability of deep learning techniques in revolutionizing detection of disease in agricultural settings, providing a more reliable and efficient solution. While acknowledging the successes, the study also highlights certain challenges and limitations encountered during the research process. This research’s future reach goes beyond the scholarly sphere. The study envisions the creation of an application in recognition of the usefulness of the CNN-based disease detection method. This application aims to empower farmers and agricultural stakeholders by providing a user-friendly tool for swift and accurate identification of crop diseases in the field.