Foliar diseases are a threat to the apple orchard industry, and misdiagnosis or delay in treatment can cause great economic loss. In this project, we aim to build a machine learning model capable or identifying a range of pathogens in apple trees based on pictures of their leaves. We explore and discuss different approaches, from simple CNN models to more complex architectures, using different loss functions and ways to handle the multi-label problem.