  Apple diseases, if not diagnosed early, can lead to massive resource loss and
pose a serious threat to humans and animals who consume the infected apples.
Hence, it is critical to diagnose these diseases early in order to manage plant
health and minimize the risks associated with them. However, the conventional
approach of monitoring plant diseases entails manual scouting and analyzing the
features, texture, color, and shape of the plant leaves, resulting in delayed
diagnosis and misjudgments. Our work proposes an ensembled system of Xception,
InceptionResNet, and MobileNet architectures to detect 5 different types of
apple plant diseases. The model has been trained on the publicly available
Plant Pathology 2021 dataset and can classify multiple diseases in a given
plant leaf. The system has achieved outstanding results in multi-class and
multi-label classification and can be used in a real-time setting to monitor
large apple plantations to aid the farmers manage their yields effectively.
