首页|University of Szeged Researchers Have Provided New Data on Machine Learning (Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting)

University of Szeged Researchers Have Provided New Data on Machine Learning (Effectiveness of machine learning and deep learning models at county-level soybean yield forecasting)

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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – New study results on artificial intelligence have been published. According to newsreporting from Szeged, Hungary, by NewsRx journalists, research stated, “Crop yield forecasting is criticalin modern agriculture to ensure food security, economic stability, and effective resource management.”Our news reporters obtained a quote from the research from University of Szeged: “The main goal of thisstudy was to combine historical multisource satellite and environmental datasets with a deep learning (DL)model for soybean yield forecasting in the United States’ Corn Belt. The following Moderate ResolutionImaging Spectroradiometer (MODIS) products were aggregated at the county level. The crop data layer(CDL) in Google Earth Engine (GEE) was used to mask the data so that only soybean pixels were selected.Several machine learning (ML) models were trained by using 5 years of data from 2012 to 2016: randomforest (RF), least absolute shrinkable and selection operator (LASSO) regression, extreme gradient boosting(XGBoost), and decision tree regression (DTR) as well as DL-based one-dimensional convolutional neuralnetwork (1D-CNN). The best model was determined by comparing their performances at forecasting thesoybean yield in 2017-2021 at the county scale. The RF model outperformed all other ML models with thelowest RMSE of 0.342 t/ha, followed by XGBoost (0.373 t/ha), DTR (0.437 t/ha), and LASSO (0.452t/ha) regression.”

University of SzegedSzegedHungaryEuropeCybersecurityCyborgsEmerging TechnologiesMachine Learning

2024

Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

ISSN:
年,卷(期):2024.(Jan.29)
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