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The Economic and Technical Feasibility of AI Substitution of Harvesting Jobs in the United States During COVID-19 5 on augmentation technologies and for training programs would make socioeconomic sense. Conclusion and Recommendations This study has demonstrated that the implementation of AI/automation to replace harvesters is economically feasible both during the COVID-19 pandemic and on into the future. Although it has been demonstrated that mechanical bottlenecks may get in the way of such implementation, at least in the short term, relegating technical feasibility to a subset of harvesting jobs determined by the crop, it also can be assumed that over the longer term, robotic automationwill only expand in extent and in terms of worker impact. In light of these conclusions, multiple policy recommendations have been proposed. These recommendations should be seriously considered so as to prevent serious short-term repercussions for harvest workers during the COVID-19 pandemic and to smooth the adoption of such technologies for such workers in the long term. References 1. Barcia de Mattos, Fernanda, et al. Robotics and Reshoring: Employment Implications For Developing Countries. International Labour Organization, 2020. 2. Bennet, Andrea. “Essential Business NAICS Code Directory.” EverString , 28 April 2020. 3. Birrell, Simon, et al. “A Field-tested Robotic Harvesting System for Iceberg Lettuce.” Journal of Field Robotics , Vol. 37, no. 2, 2019, pp. 225–245. 4. Bochtis, Dionysis, et al. “Agricultural Workforce Crisis in Light of the COVID-19 Pandemic.” Sustainability , Vol. 12, 8212, 5 October 2020. 5. Bureau of Labor Statistics. Employment by Detailed Occupation. 1 September 2020. 6. Calvin, Linda, & Martin, Philip. The US Produce Industry and Labor: Facing the Future in a Global Economy. EER106. US Department of Agriculture, Economic Research Service. November 2010. 7. del Rio-Chanona, R. Maria, et al. “Supply and Demand Shocks in the COVID-19 Pandemic: An Industry and Occupation Perspective.” Oxford Review of Economic Policy , 2020, pp. 94–137. 8. Di Vaio, Assunta, et al. “Artificial Intelligence in the Agri-food System: Rethinking Sustainable Business Models in the COVID-19 Scenario.” Sustainability , Vol. 12, 485114, June 2020. 9. Frey, Carl Benedikt, & Osborne, Michael A. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change , Vol. 114, January 2017, pp. 254–280. 10. Lewis, Nell. “Why Robots Will Soon Be Picking Soft Fruits and Salad.” CNN , 4 September 2019. 11. McGee, Joseph. Interview conducted by Cameron MacMahon, Svetlana Gulyaeva, and Zemichael Gebeyehu. 27 January 2021. 12. O*NET. “45-2092.00 - Farmworkers and Laborers, Crop, Nursery, and Greenhouse.” 2020. 13. Tang, Yunchao, et al. “Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.” Frontiers in Plant Science , Vol. 11, 19May 2020. 14. Technavio. Crop Harvesting Robots Market by Product and Geography—Forecast and Analysis, 2020-2024. July 2020. We wish to thank the ILO and mentors from Lehigh University and Universidad San Francisco de Quito for their assistance on this Martindale Center and Lehigh University / United Nations Partnership Policy Brief project. Martindale Center for the Study of Private Enterprise Lehigh University College of Business Rauch Business Center, 621 Taylor Street Bethlehem, PA 18015 Tel: (610) 758-4771 / Fax: (610) 758-6549 Executive Director: Todd A. Watkins, Ph.D. Tel (610) 758-4954 / taw4@lehigh.edu Fellowship Advising and United Nations Programs 32 Sayre Drive, Bethlehem, PA 18015 (610) 758-4977 / invpia@lehigh.edu Director: Bill Hunter, Ph.D. Tel (610) 758-4505 / wdh3@lehigh.edu