Most of the world’s food comes from plants, and in India, agriculture is a significant source of income for farmers. Farmers rely on healthy crops for their livelihood, but plant diseases can be challenging to diagnose and cost them a lot of effort. Traditionally, plant pathologists identify these diseases by looking at the plants, but this method is slow and can lead to mistakes. To improve this process, artificial intelligence (AI) and computer vision can be used to detect plant diseases early. This can help reduce the harmful effects of the diseases and overcome the limitations of human monitoring. In this article, we implement classifying diseases in peach plants using a deep learning model called EfficientNet-B7, which is a type of Convolutional Neural Network. We used 10,628 images of peach leaves to develop the model to distinguish between healthy leaves and those with bacterial spots. The model performed very well in this binary classification task, achieving 99.48% accuracy, precision 99.00%, recall 99.00% and support 99.00%. The results show that EfficientNet-B7 outperforms other models in identifying peach leaf diseases, making it a valuable tool for farmers to protect their crops.