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.

We developedoptimized a FRET-based genetically-encoded biosensor Met-lead with high sensitivity 10nM (0.2μg/dL, 2ppb) to fit the related regulations: the safety value of blood lead level for children 3μg/dLWHO-permitted level for tap water 10ppb. We further built a portable smart-phone device to measure Pb on-site. The practical applications of portable Met-lead can fulfill the needs from blood/urine Pb detection to drinking water/environmental irrigation water Pb monitoring, it will also help to build up a warning system to prevent further contamination from heavy metal Pb.