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.

Integrating deep learning, 3D information analysis, hyperspectral analysis, computer vision analysiscombined the multi-source images to develop fruit quality monitoring techniques, including: planting area monitoring, position monitoring, yield monitoring, harvesting time prediction, fruit maturity measurement, fruit quality testing, to achieve the goal of enhancing industrial value.

“MeDA OXR: Real-time Hazard Recognition System,” which can automatically screeninterpret the medical images in real-time, is designed for emergency rooms to assist physicians in diagnosis, reducing medical risks,improving overall efficiency. Combining portable X-RayAI algorithms, the system performs real-timeaccurate preliminary screeningdiagnoses of diseases, such as pneumothorax, pneumonia,tuberculosis. It can also locate the nasogastric tube, endotracheal tube,central venous catheter, while misplacement of that is sent to the physicians when detected.

PANCREASaver contains a “PC automatic segmentation model” (image segmentation)a “PC analysis AI model” (image classification) that can read the DICOM format of postcontrast CT images directly for the automatic analysis process. After conducting prep-processing with image processing algorithms, C2FNAS is employed to illustrate the tumor position prior to the diagnosis conducted by CNN. The results can be provided to the physician for diagnostic reference so as to reduce early omissionsincrease the detection rate of pancreatic cancer.