Towards Huanglongbing In-field detection system with AI edge computing

To address the low efficiency of manual inspection methods used for Citrus Huanglongbing prevention and control, a system design of citrus huanglongbing in-field detection with AI edge computing device is proposed and evaluated. The system consist of Image Capture Robotic Devices, AI Edge Computing Service, Cloud Service, and Remote Control Client. A citrus Huanglongbing detection neural network model was trained with 84.1%mAP, which can be deployed on an AI edge computing device, such as Jetson Nano to detect HLB with lower delay than using a cloud-based AI approach. Therefore, robotic devices such as UAVs, surveillance cameras can be used to efficiently inspect citrus orchard, process images of citrus leaves collected from cameras in real-time. Experimental result shows that this system has great potential to apply on Citrus Huanglongbing field detection scenario to enhance the inspection efficiency of citrus orchards.