Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches
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Published:2023-09-07
Issue:9
Volume:13
Page:1777
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ISSN:2077-0472
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Container-title:Agriculture
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language:en
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Short-container-title:Agriculture
Author:
Huang Yixin1, Srivastava Rishi1, Ngo Chloe1, Gao Jerry2ORCID, Wu Jane3, Chiao Sen4ORCID
Affiliation:
1. Applied Data Science Department, San Jose State University, San Jose, CA 95192, USA 2. 3iCloud, San Jose State University, San Jose, CA 95192, USA 3. 3iCloud.Co and BRI Captial Inc., San Francisco, CA 94104, USA 4. NOAA Center for Atmospheric Sciences and Meteorology, Howard University, Washington, DC 20059, USA
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.
Funder
U.S. Department of Commerce, National Oceanic and Atmospheric Administration, Educational Partnership Program Professor NOAA Center for Atmospheric Sciences and Meteorology, Howard University
Subject
Plant Science,Agronomy and Crop Science,Food Science
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