MobileH-Transformer: Enabling real-time leaf disease detection using hybrid deep learning approach for smart agriculture

Abstract

Agriculture has produced the vast majority of food for the world’s population throughout human history and plays a significant role in the economies of many countries, particularly on the continents of Asia and Africa. However, the quality and quantity of crop yields are influenced by various natural factors, including leaf diseases. While recent studies leveraged advanced deep learning models to accurately detect early disease symptoms, a significant gap remains in adapting these models for resource-constrained devices with limited computational capabilities, such as drones and smartphones. In this paper, we introduce MobileH-Transformer, a novel hybrid model combining convolutional neural networks (CNN) and Transformer architectures for accurate leaf disease detection with minimal computation demands. The proposed model integrates the CNN component with a novel dual convolutional block offering the ability to extract diverse features and reduce the input size for the Transformer component. In addition, it leverages CNN’s local feature extraction and Transformer’s global dependency learning, resulting in better accuracy with less computation resource consumption. The evaluation results on public datasets show that our model achieves competitive F1-score values of 97.20% on the corn leaf disease and 96.80% on the subset of the PlantVillage datasets, surpassing recent studies with only 0.4 Giga Floating Point Operations (GFLOPs) and ensures real-time processing on mobile devices at 30.5 frames per second (FPS).