Innovations Real-time identification of crop losses using UAV imagery
■Technology Introduction
This technology integrates 1000+ times of UAV imaging experiences with labeled rice lodging images for training. A rice lodging recognition model using deep learning reaches 90% accuracy. The recognition model can be deployed in a microcomputer mounted on UAVs to implement edge computing. While taking aerial images, the inference can be completed and reveal lodging area and damage level in-time.
■Scientific Innovation
This technology employs image segmentation and edge computing to build agricultural disaster image database and implement the real-time inference on UAVs. This technology enables surveying personnel to instantly identify crop loss and damage distribution. This technology greatly simplifies the time-consuming and labor-intensive surveying and increases the efficiency of agriculture loss subsidy.
■This technology won the Futuretech Breakthrough Award in 2019 Future Tech Expo. 

Nation Chung Hsing University

Real-time identification of crop losses using UAV imagery