Apple trees are perhaps one of the most popular plants to grow in large plantations and in-home gardens. At the same time, Apple plants are among the plants that are the most prone to diseases. Disease identification at an early stage and its prevention before spreading into other parts of the plant is a challenge even for the expert’s eye. Therefore, an adequate system is required to detect plant disease in the initial stage. This paper displays the prowess of Convolutional Neural Networks to automatically detect and address the issue. Images of Apple leaves, covering various diseases as well as healthy samples, from the Plant Village dataset are used to validate results. Image filtering, image compression, and image generation techniques are used to gain a large train-set of images and tune the system perfectly. The trained model achieves high accuracy scores in all the classes with a net accuracy of 98.54% on the entire dataset, sampled and generated from 2561-labelled images.