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Innovations
Real-time identification of crop losses using UAV imagery
■Technology IntroductionThis 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. ■Institutes Nation Chung Hsing University mdyang@dragon.nchu.edu.tw |
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