Abstract: Efficient detection technologies are necessary to address the danger of plant diseases to global agriculture and food security. The conventional method of disease identification, which depends on farmers visually inspecting the crops, is frequently ineffective and prone to bias. This work explores the feasibility of employing image processing and machine learning techniques to automatically detect plant diseases, specifically focusing on infections affecting leaves. In deep learning, a subfield of machine learning, artificial neural networks are used to extract features from data in a way that is reminiscent of the human brain.