Data-intensive crop knowledge discovery in the era of cybergis and machine intelligence

Cai, Yaping

Permalink

https://hdl.handle.net/2142/106228 Copy

Description

Title

Data-intensive crop knowledge discovery in the era of cybergis and machine intelligence

Author(s)

Cai, Yaping

Issue Date

2019-12-03

Director of Research (if dissertation) or Advisor (if thesis)

Wang, Shaowen

Doctoral Committee Chair(s)

• Wang, Shaowen
• Guan, Kaiyu

Committee Member(s)

• Peng, Jian
• Rhoads, Bruce

Department of Study

Geography & Geographic InfoSci

Discipline

Geography

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

Ph.D.

Degree Level

Dissertation

Keyword(s)

Remote Sensing, Agriculture, Machine Intelligence, Crop Type Classification, Wheat Yield Prediction, N Stress Detection, Scaling up

Abstract

With an increasing global population and continuing climate change, food security has become a grand scientific and societal challenge. To tackle this challenge, it is critically important to obtain timely crop information such as yield potential and growing conditions as crop information is often time sensitive for societal applications. With rapidly advancing remote sensing technologies such as satellite- and UAV-based approaches of fine-resolution, and continuous observation in visible bands, NIR, thermal, microwave for large geographic areas, timely crop knowledge discovery based on massive remote sensing data provides a promising means to tackle the food security challenge. Furthermore, to integrate remote sensing data of crops with related environmental data (e.g., temperature, precipitation, and radiation) can help understand crop changes in various environmental conditions. How to harness such rich data sources to achieve timely crop knowledge discovery based on advanced computing and geospatial approaches such as deep learning and cyberGIS for multiple agricultural applications is the primary focus of this dissertation research. Specifically, several interrelated studies have been conducted to achieve high-performance and in-season crop type classification at both the county and state scales in Illinois, USA; integrate climate and satellite data for wheat yield prediction in Australia; and detect in-season crop nitrogen stress using UAV- and CubeSat-based multispectral sensing at a field level. These studies are enabled by cutting-edge machine learning methods (e.g. deep neural networks) and advanced cyberGIS capabilities (e.g. ROGER supercomputer). Collectively, findings from the studies promise to transform data-intensive crop knowledge discovery.

Graduation Semester

2019-12

Type of Resource

text

Permalink

http://hdl.handle.net/2142/106228

Copyright and License Information

© 2019 Yaping Cai

Owning Collections