首页|Report Summarizes Machine Learning Study Findings from Xinjiang University (Weig hted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi -Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China)
Report Summarizes Machine Learning Study Findings from Xinjiang University (Weig hted Variable Optimization-Based Method for Estimating Soil Salinity Using Multi -Source Remote Sensing Data: A Case Study in the Weiku Oasis, Xinjiang, China)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in artificial intelligence. According to news reporting originating from Urumqi, Pe ople's Republic of China, by NewsRx correspondents, research stated, "Soil salin ization is a significant global threat to sustainable agricultural development, with soil salinity serving as a crucial indicator for evaluating soil salinizati on. Remote sensing technology enables large-scale inversion of soil salinity, fa cilitating the monitoring and assessment of soil salinization levels, thus suppo rting the prevention and management of soil salinization." Funders for this research include Esearch Project on Spatial And Temporal Evolut ion of Soil Salinization in The Aksu River Basin; Technology Innovation Team (Ti anshan Innovation Team), Innovative Team For Efficient Utilization of Water Reso urces in Arid Regions; Key Project of Natural Science Foundation of Xinjiang Uyg ur Autonomous Region; National Natural Science Foundation of China.
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