The unprecedented growth of the world’s population has made securing food alongside sustaining wheat production rates increasingly important. The reliance purely on the traditional approaches of farmers for monitoring wheat crops has now become obsolete and misleading. As a solution to this problem, integrating deep learning methods with crop monitoring has become vital and efficient. This study updates and fine-tunes a pre-trained model of MobileNetV2, which improves the automation of the wheat rust detection system using image classification. The system can classify leaves into healthy and unhealthy categories contributing to crop damage and yield decline. In doing so, the proposed model can reduce losses and yield improvement can be realized. The system also fully automated the classification of leaves, achieving an astonishing accuracy of 98%. The innovation is planned to be further customized for other environmental settings.