Crop disease detection plays a crucial role in ensuring food security and improving agricultural practices. In this research, we investigate the performance of deep learning (DL) models, hybrid models combining DL and machine learning (ML), and transfer learning models for crop disease detection across multiple crops. To conduct this study, we employed various existing DL methods, DL + ML hybrid methods, and transfer learning methods. A comprehensive dataset was collected from Dantiwada Agriculture University, encompassing diverse crop diseases across multiple crops. Upon evaluating these models, our findings reveal that the transfer learning model exhibited superior performance compared to other algorithms. Specifically, on the potato crop, the transfer learning model achieved remarkable accuracy, precision, recall, and F1-Score, all reaching 99%. These results demonstrate the potential of transfer learning for crop disease detection, highlighting its ability to leverage knowledge from pre-trained models to enhance detection accuracy and overall performance. The implications of these findings extend to the agricultural sector, offering promising avenues for improving crop management and disease prevention strategies.