This research explores the application of computer technology and image analysis for the detection of plant diseases. In contrast to traditional studies that often concentrate on individual plant diseases and overlook image preparation, this study introduces a versatile model capable of handling a diverse range of plants. Our research introduces a versatile computer program capable of accommodating six distinct plant types: tomato, potato, chilli, lemon, pepper bell, and rice. We incorporate sophisticated image processing methods to maximize the performance of our computer program while considering a variety of plant scenarios. Our method uses a state-of-the-art computer program called a “convolutional neural network,” which is trained on images collected from internet databases. Our ability to interpret images is greatly enhanced by this novel computer model. Moreover, by preserving consistent image sizes, we improve our efficiency in identifying diseased areas and detecting irregularities in plants. As of right now, we have our plant images from the internet. Some of the plants we have are tomato, potato, chilli, lemon, pepper, bell, and rice. Our findings suggest that our computer program could be highly beneficial in identifying diseased plants. We intend to add even more plant species with both healthy and diseased leaves in the future.