Harvesting labor is the single largest cost in production of apples and other tree fruits. Harvest automation is thus urgently needed to alleviate labor shortage and rising labor cost for U.S. apple and other specialty crop growers. Moreover, pre-sorting or removal of low-quality or defective apples at the time of harvesting in orchard can help growers and packers achieve significant cost savings in postharvest storage and packing and improve postharvest pest and inventory management. This USDA-NIFA funded project is therefore intended to develop and transfer an automated and integrated mobile system (AIMS) for commercial apple harvesting, in-field pre-sorting, and quality recording, so as to help apple growers address the labor cost and shortage concerns and enhance their profitability and long-term sustainability. Efficient multi-arm robotic harvesting modules and an AI-based fruit sorter, along with automated fruit and bin handling functions, will be designed, constructed and integrated with a new autonomous mobile platform. Field tests and demonstrations will be conducted of the new robotic harvester, in-field sorter, and AIMS in commercial orchards in Michigan, Pennsylvania and Washington. Various forms of outreach activities will be organized for growers, researchers, extension personnel, and K-12 and college students to broadly disseminate, and accelerate adoption of, the developed technologies. Economic/labor analyses will be conducted on the impact of the developed technologies and different technology adoption models and strategies on the apple industry and future labor force.