This research investigates the application of Residual Neural Networks (ResNet) for enhanced plant disease classification, aiming to improve accuracy and efficiency in disease identification within agricultural settings. Leveraging a diverse dataset encompassing various plant diseases, the study employs ResNet, a deep learning architecture known for its residual learning mechanism. The ResNet-based model achieves a commendable accuracy of 95.6%, showcasing superior performance in classifying different plant diseases. Precision and recall metrics average at 93.2% and 96.5%, respectively, underscoring the model’s ability to accurately identify true positives while minimizing false negatives. With an overall F1 score of 94.8%, this approach demonstrates its efficacy in accurate disease classification, signifying ResNet’s potential for practical deployment in agricultural disease management for improved crop yield and sustainable farming practices.