Real-time Crop Classification Using Edge Computing and Deep Learning
Ming‐Der Yang, Hsin-Hung Tseng, Yu Chun Hsu, Wei Chen Tseng
Abstract
In recent years, edge computing and deep learning have been successfully performed processing and classification tasks in a variety of fields including agriculture. Therefore, this research aims to use unmanned aerial vehicle (UAV) for agriculture applications with integrating edge computing and deep learning techniques. This research experiment was carried out in the NCHU Experimental Farm. The DJI Matrice 100 drone with ASUS Tinker Board S embedded system, which connects a Logitech C925e webcam to capture images are used in this study. The ASUS Tinker Board S runs a folder monitoring program and sends images over the 4G LTE network to the backend server whenever new images are captured and stored. The backend server runs a pre-trained image semantic segmentation model and provides image inference service. The image inference results with the associated segmented image will be sent to the mobile device of the drone controller, and a dynamic flight control action can be triggered. The image semantic segmentation model adopts SegNet network architecture. For a comparison purpose, another network architecture, FCN-AlexNet, was also trained and validated. The preliminary results show SegNet outperformed FCN-AlexNet in image semantic segmentation tasks in terms of the evaluation between training and validation. The average inference speed of the semantic image segmentation model is 0.7s with segmentation identification accuracy is 89%. The promising results shed light on many agriculture applications, such as crop growth condition assessment, fertilizer management, and yield prediction. Additionally, this research provides possible solutions for the labor shortage issue of agriculture which is a common challenge in an aging community like Taiwan and many countries worldwide.