CAST papers are the foundation of CAST’s science communication work. Developed by expert-led task forces, these peer-reviewed publications translate agricultural science into trusted insights that inform policy, guide public understanding, and fuel CAST programs, dialogues, and educational initiatives.

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Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Task Force Chairs
University of Nebraska
Authors
Montana State University
Washington State University
Iowa State University
University of Nebraska-Lincoln
Purdue University
University of Illinois
Ohio State University
Janzen Agricultural Law, LLC
Washington State University
DeLaval
Mississippi State University
University of Kentucky
University of Missouri
Ohio State University
Mississippi State University
Abstract
Rapid advances in automation, robotics, and artificial intelligence are reshaping agricultural production. This paper examines the emerging roles of ground and aerial robotic systems—unmanned ground vehicles (UGVs), unmanned aerial systems (UASs), and robotic manipulators—in crop and livestock systems. Robots now offer novel opportunities to improve labor efficiency, enhance precision in input management, reduce environmental impacts, and increase productivity across row crops, specialty crops, and animal agriculture. UGVs enable continuous, highly precise field operations while reducing soil compaction and offering scalable, swarm-based solutions. UASs provide high‑resolution sensing capabilities for crop monitoring, disease detection, and targeted input application. Robotic manipulators are advancing tasks such as automated milking, fruit harvesting, weeding, and livestock monitoring. Despite their potential, widespread adoption is challenged by technical barriers (e.g., autonomy, machine vision, sensor integration), economic feasibility, limited rural broadband connectivity, and evolving regulatory, liability, and data privacy concerns. The paper highlights key enabling technologies—including AI, machine vision, big data infrastructure, and interoperability standards—and emphasizes the need for workforce training that integrates agricultural, engineering, and data science skills. Ultimately, the deployment of agricultural robots promises to transform farming into a more efficient, data-driven, and sustainable system, provided that economic, technical, and policy challenges are addressed.
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Reviewers
Texas A&M AgriLife Research
Cotton Incorporated
Iowa State University
CAST Liaisons
Syngenta Crop Protection
Innovation Center for U.S. Dairy
Translators