GeoAI for Agriculture

Chishan Zhang, Chunyuan Diao, Tianci Guo

Research output: Chapter in Book/Report/Conference proceedingChapter


The development of smart agricultural management practice is important for addressing the global food security challenges, and is also critical for insurance designing, decision making, and economic planning. By synthesizing artificial intelligence (AI) and geographic information system, Geospatial Artificial Intelligence (GeoAI) has been increasingly utilized in the field of agriculture. This chapter briefly reviews the development of GeoAI in agriculture. As yield estimation is one of the most important topics, the main focus of the chapter is introducing GeoAI-based conceptual framework of crop yield estimation. The framework comprises preparation of geospatial modeling inputs, GeoAI-based yield estimation models, as well as feature importance and uncertainty analysis. Using the U.S. Corn Belt as a case study, three GeoAI models for county-level crop yield estimation and uncertainty quantification are discussed. Results show that GeoAI models enhance the ability to understand crop yield response to various environmental conditions, thus helping optimize farm management strategies and agricultural decision making for sustainable agricultural development.

Original languageEnglish (US)
Title of host publicationHandbook of Geospatial Artificial Intelligence
PublisherCRC Press
Number of pages21
ISBN (Electronic)9781003814924
ISBN (Print)9781032311661
StatePublished - Jan 1 2023

ASJC Scopus subject areas

  • General Engineering
  • General Earth and Planetary Sciences
  • General Computer Science
  • General Environmental Science


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