An automated agricultural machinean intelligent crop production decision-making system are developed for greenhouse asparagus growth monitoring. Automationenergy-saving machinery technologiesInternet of Things/information communication techniques are adopted to achieve the goal of "saving laboreffort"improve "management technologies". The developed machinesystem can greatly improve the qualityquantity of domestic asparagus production,achieve the dual goals of stabilizing the outputenhancing quality of harvested asparagus.

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.

The Air/Ground cooperation for optimal rice harvesting model is established to provide a visual harvesting decision service on a cloud platform. Drones and mobile devices are employing to estimate grain moisture and forecast the variation of harvest moisture content (HMC) in the coming days by huge amounts of imagery data, deep learning algorithms, and weather forecasts. This model can benefit in several aspects, such as setting an accurate and comprehensive optimal harvest schedule, reducing the cost of agricultural apparatus and barn ovens, ensuring the rice quality, and maximizing farmers' benefits. The potential value of the model practice could be more than a billion in Taiwan.

In order to solve the undiscovered problem that caused by the fall ofelders, we develop a deep learning based systemnamed it 「SkyEye」,which includes our own sensor 「AI Falling Image Sensor」, a cloud sever that storespushes information about fall eventsa mobile app that communicates with users.

This system uses the innovative AIoT application to target annoying scalp maintenance problemsdevelops an intelligent scalp detectionmanagement system. The core of the technology is to use deep learning-based object detection models. Tens of thousands of microscopic image data sets that are labeledtrained for scalp symptoms. As a result, the scalp image recognition module is develop, such as dandruff, hair loss, oil, and inflammation, etc. Hence this system can provide scalp detection, and maintenance effectiveness tracking functions lead scalp maintenance services to a new level of intelligent management.

(1) Image recognition3D positioning of the pest: Modularize the system for testing on the orchard. (2) High efficiency laser pest control scanning system: It was developed to be suitable for windlessbreezy environments. (3) Enhance the economic tracked vehicle for hillside field: Self-developed power designsteering control based on dynamic model,(4) Field UGV control platform: Based on the preciseaccurate positioning in field, when robot operates on the reference path which planned by map, it can do path trackingdynamically obstacle avoiding in real-time.

”AI2 Robot City” is a game-based learning kit for primary and secondary school students, which combines AI image-recognition teaching tool of MIT App Inventor and the computational thinking board game named as "Robot City." Through this learning kit, users will learn to make smart cars, create image-recognition models, write and perform mobile application. Users will learn to write a program to recognize the personal cards in the board game, and furthermore to control the smart cars with blue-tooth and compete in the computational thinking board game.

1. CNN model architecture optimization techniques: Propose an Agilev3-L architecture, with a performance index of mAP@50:95FPS:FP16 up to 171.79 2. GOP-mode acceleration scheme for real time inference: employ GOP-modeTRACKING algorithms to enhance the processing rate. 3. Propose a rapid data labeling system HiTag.

This project integrated with industrial foresight technologies, including UAV, artificial intelligenceimage recognition, to collect real-time images, apply algorithm in evaluation, link the technology of IOT (Internet of Things)environment sensing,use unmanned vehicle to conduct controlling work.

This technique uses multimodal nonlinear optical microscopy with the image contrast of second harmonic generation (SHG)coherent anti-Stokes Raman scattering (CARS). By analyzing the nonlinear signals on images, it can be used to characterizequantify the structural variation in the collagen scaffolds processed with different crosslinking methods. The correlation between signalsmechanical properties can then be derived. Additionally, comparative analysis of these two signals can be used to monitor the structural (triple-helix) change of collagen molecules during the crosslinking processes.