Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches

Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao (), Jane Wu and Sen Chiao ()
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Yixin Huang: Applied Data Science Department, San Jose State University, San Jose, CA 95192, USA
Rishi Srivastava: Applied Data Science Department, San Jose State University, San Jose, CA 95192, USA
Chloe Ngo: Applied Data Science Department, San Jose State University, San Jose, CA 95192, USA
Jerry Gao: 3iCloud, San Jose State University, San Jose, CA 95192, USA
Jane Wu: 3iCloud.Co and BRI Captial Inc., San Francisco, CA 94104, USA
Sen Chiao: NOAA Center for Atmospheric Sciences and Meteorology, Howard University, Washington, DC 20059, USA

Agriculture, 2023, vol. 13, issue 9, 1-22

Abstract: Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of the crop product, it is necessary to monitor and evaluate farmland soil quality by analyzing the physical and chemical properties of soil since the soil is the base to provide nutrition to the crop. As a result, soil analysis contributes greatly to maintaining the sustainability of soil in producing crops regularly. Recently, some agriculture researchers have started using machine learning approaches to conduct soil analysis, targeting the different soil analysis needs separately. The optimal method is to consider all those features (climate, soil chemicals, nutrition, and geolocations) based on the growing crops and production cycle for soil analysis. The contribution of this project is to combine soil analysis, including crop identification, irrigation recommendations, and fertilizer analysis, with data-driven machine learning models and to create an interactive user-friendly system (Soil Analysis System) by using real-time satellite data and remote sensor data. The system provides a more sustainable and efficient way to help farmers harvest with better usages of land, water, and fertilizer. According to our analysis results, this combined approach is promising and efficient for smart farming.

Keywords: soil analysis; soil quality evaluation; crop identification; irrigation cycle; fertilizer recommendation; machine learning models; satellite and remote sensor data (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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