Potato plant diseases threaten food security in the world generally due to the heavy economic impact and reduced yield in agriculture. The traditional diagnosis of potato plant diseases has been through manual identification and is, therefore, very labor-intensive, costly, and vulnerable to man’s error. It, therefore, presents a hybrid deep learning architecture that integrates CNN with DenseNet, aiming to exploit the latter in improving the accuracy for potato plant diseases. The model is to be trained on wholesome public datasets with advanced preprocessing techniques such as data augmentation, normalization, and noise reduction under various diversified environmental conditions. Experimental results show that the hybrid model yields classification accuracy of 99.72% compared to stand-alone CNN (98.2%), and DenseNet (99.14%). The so-developed model shows effectiveness in scenarios that prove a challenge, like various lighting and orientations of leaves and occlusions. It developed a web application where farmers upload their images and received onsite reliable diagnoses and treatment recommendations immediately. Its crop loss and pesticide uses are minimized by promoting sustainability in agriculture with efficient and scalable solutions for modern agricultural disease management.