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 completedreveal lodging areadamage level in-time.

This technique uses Tainan 11 (TN11), which occupies about 70 of the rice planting area in Taiwan. Building the early watering warning system of crops by combining the physiological indicators of rice with environmental dataartificial intelligence technology. The system can analyzepredict the impending water shortage for rice in advanceissue drought warning immediately. Therefore, decision makers can give the timelyappropriate amount for rice irrigationimprove the efficiency of using waterreducing the cost of farmersfarming losses.