首页|Data from University of Florida Advance Knowledge in Machine Learning (Simulating Soil Hydrologic Dynamics Using Crop Growth and Machine Learning Models)
Data from University of Florida Advance Knowledge in Machine Learning (Simulating Soil Hydrologic Dynamics Using Crop Growth and Machine Learning Models)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Investigators publish new report on Ma chine Learning. According to news originatingfrom Homestead, Florida, by NewsRx correspondents, research stated, “Accurate measurement of cropevapotranspirati on (ETc) and soil moisture content (SMC) is critical for different purposes, inc ludingdeveloping irrigation scheduling practices that improve water use efficie ncy and crop yield. The objectivesof this study were to 1) simulate daily ETc a nd SMC of green beans and sweet corn under full irrigationand three deficit irr igation rates using the Decision Support System for Agrotechnology Transfer (DSSAT) CROPGRO-Green bean and CERES-Sweet corn models and 2) evaluate the performan ce of threemachine learning models for simulating ETc of green beans and sweet corn.”
HomesteadFloridaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningUniversi ty of Florida
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