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

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

Cai, Yaping

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

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/106228/CAI-DISSERTATION-2019.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

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.