首页|Recent Studies from University of Montpellier Add New Data to Machine Learning ( Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellit e Time Series Data)
Recent Studies from University of Montpellier Add New Data to Machine Learning ( Early Season Forecasting of Corn Yield at Field Level from Multi-Source Satellit e Time Series Data)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News - New study results on artificial intell igence have been published. According to news originating from Montpellier, Fran ce, by NewsRx editors, the research stated, “Crop yield forecasting during an on going season is crucial to ensure food security and commodity markets.” Financial supporters for this research include French National Association of Re search And Technology. Our news reporters obtained a quote from the research from University of Montpel lier: “For this reason, here, a scalable approach to forecast corn yields at the field-level using machine learning and satellite imagery from Sentinel-2 and La ndsat missions is proposed. The model, evaluated on 1319 corn fields in the U.S. Corn Belt from 2017 to 2022, integrates biophysical parameters from Sentinel-2, Land Surface Temperature (LST) from Landsat, and agroclimatic data from ERA5 re analysis dataset. Resampling the time series over thermal time significantly enh ances predictive performance. The addition of LST to our model further improves in-season yield forecasting, through its capacity to detect early drought, which is not immediately visible to optical sensors such as the Sentinel-2.”
University of MontpellierMontpellierFranceEuropeCyborgsEmerging TechnologiesMachine Learning
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