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AI-Augmented Agricultural Geography and Remote Sensing

Authors: Ziheng Sun*, George Mason University, Liping Di, George Mason University, Hui Fang, George Mason University
Topics: Agricultural Geography, Remote Sensing, Cyberinfrastructure
Keywords: Artificial intelligence, agricultural geography, remote sensing
Session Type: Paper
Day: 4/8/2020
Start / End Time: 1:45 PM / 3:00 PM
Room: Virtual Track 3
Presentation File: No File Uploaded


There are many non-observable or non-recordable variables in the agriculture-climate-human cycle, which poses big challenges for scientists to estimate impacts and predict the future trends in crop production, food supply, market pricing, etc. To ensure food security and protect environment, the modern agriculture is looking for solutions to advance farming technology. Artificial intelligence gives us hope to skip those variables and sophisticated simulation equations by building direct relationship models between the variables which we do have observations and the information which could be used to advise farming activities, predict food market, or making related policies. It is widely believed that once these AI models are built and trained using historical data, agricultural stakeholders would be able to get more direct, quick, high-quality information to make more accurate assertion about the history, present and future development of agricultural geography. This presentation will introduce the state-of-art of AI techniques in agricultural geography. Most use cases leverage remote sensing data and algorithms to enable in-time monitoring and guide farming activities to improve crop productivity.

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