An IoT Transfer Learning-Based Service for the Health Status Monitoring of Grapevines

Abstract

Grapevine is a valuable and profitable crop that is susceptible to various diseases, making effective disease detection crucial for crop monitoring. This work explores the use of deep learning-based plant disease detection as an alternative to traditional methods, employing an Internet of Things approach. An edge device, a Raspberry Pi 4 equipped with an RGB camera, is utilized to detect diseases in grapevine plants. Two lightweight deep learning models, MobileNet V2 and EfficientNet B0, were trained using a transfer learning technique on commercially available online dataset, then deployed and validated on field-site in an organic winery. The models’ performance was further enhanced using semantic segmentation with the Mobile-UNet algorithm. Results were reported through a web service using FastAPI. Both models achieved high training accuracies exceeding 95%, with MobileNet V2 slightly outperforming EfficientNet B0. During validation, MobileNet V2 achieved an accuracy of 94%, compared to 92% for EfficientNet B0. In terms of IoT deployment, MobileNet V2 exhibits faster inference time (330 ms) compared to EfficientNet B0 (390 ms), making it the preferred model for online deployment.