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ARS Home » Southeast Area » Stoneville, Mississippi » Crop Production Systems Research » Research » Publications at this Location » Publication #346117
Research Project: Application Technologies to Improve the Effectiveness of Chemical and Biological Crop Protection Materials

Location: Crop Production Systems Research

Title: Advancing to the next generation precision agriculture

Author
item Huang, Yanbo
item BROWN, MOLLY - University Of Maryland

Submitted to: Global Agri-Food Systems to 2050– Threats and Opportunities
Publication Type: Book / Chapter
Publication Acceptance Date: 3/20/2018
Publication Date: 11/20/2018
Citation: Huang, Y., Brown, M. 2018. Advancing to the next generation precision agriculture. In Serraj, R., Pingali, P., editors. Agriculture and Food Systems to 2050 – Global Trends, Challenges and Opportunities. Singapore, Phillipines: World Scientific. p. 285-314. https://doi.org/10.1142/11212.
DOI: https://doi.org/10.1142/11212

Interpretive Summary:

Technical Abstract: Agriculture has been revolutionized with precision agriculture in the 1980s and biotechnological innovations in the 2010s. Precision agriculture has been modernized with GPS, GIS and remote sensing technologies from strategic monitoring in the 1980s to tactical monitoring and control in the 2010s with Internet and big data. We are in the age of big data with optimized algorithms and supercomputing power. In the past ten years, scientists in the USDA-ARS Crop Production Systems Research Unit at Stoneville, Mississippi and University of Maryland at College Park have achieved in advancing the technologies of aerial application, remote sensing and agricultural statistics on satellites, manned aircraft, unmanned aerial vehicles, ground on-the-go systems and handheld sensors and personal digital devices to solve problems in crop protection and production. In the next ten years technologies will be more advanced and applied for sustainable development of agriculture in different areas in the nation. With massive datasets from the Internet, agricultural information systems with deep-learning capabilities will be developed to control crop phenology to follow the desired trajectory to move precision agriculture to the next generation.