An Ensemble of Convolutional Neural Networks to Detect Foliar Diseases in Apple Plants
This work addresses the need for automated disease monitoring in apple plantations to aid farmers, though it is incremental as it combines existing CNN architectures.
The paper tackles the problem of early detection of foliar diseases in apple plants by proposing an ensemble of Xception, InceptionResNet, and MobileNet architectures, achieving outstanding results in multi-class and multi-label classification on the Plant Pathology 2021 dataset.
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.