Early detection of plant diseases is vital to ensuring agricultural productivity and safeguarding global food security. If left unmanaged, plant diseases can severely reduce crop yield and quality, posing risks to both farmers’ livelihoods and the broader societal food supply. Automated disease detection through machine learning, particularly using convolutional neural networks (CNN), enables farmers to take timely remedial actions. This chapter focuses on the use of CNN-based models for detecting plant leaf diseases in their early stages, providing actionable insights into disease management. CNNs are well-suited for this task because they can accurately process and analyze image data, identifying disease symptoms that might be difficult to detect with the naked eye. By focusing on early-stage detection, CNN-based models enable farmers to take timely corrective actions, reducing the spread of diseases and preventing significant crop damage. By experimentally evaluating different deep learning approaches, this chapter highlights how advanced machine learning techniques can significantly improve the accuracy of plant disease diagnosis. The results underscore the potential of these models not only to enhance agricultural practices but also to mitigate the societal impact of crop loss, contributing to sustainable food production and security.