Publication Date
9-1-2023
Document Type
Article
Publication Title
Agriculture (Switzerland)
Volume
13
Issue
9
DOI
10.3390/agriculture13091777
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.
Funding Number
NA22SEC4810015
Funding Sponsor
U.S. Department of Commerce
Keywords
crop identification, fertilizer recommendation, irrigation cycle, machine learning models, satellite and remote sensor data, soil analysis, soil quality evaluation
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Department
Computer Engineering
Recommended Citation
Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu, and Sen Chiao. "Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches" Agriculture (Switzerland) (2023). https://doi.org/10.3390/agriculture13091777
