首页|Sun Yat-sen University Researchers Highlight Recent Research in Machine Learning (Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Mul tisource Remote Sensing Data and Ensemble Machine Learning)
Sun Yat-sen University Researchers Highlight Recent Research in Machine Learning (Downscaling Administrative-Level Crop Yield Statistics to 1 km Grids Using Mul tisource Remote Sensing Data and Ensemble Machine Learning)
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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 reporting from Zhuhai, People’s Re public of China, by NewsRx journalists, research stated, “The United States (U.S .) is a global leader in the production and exportation of soybeans and corn. Ac curate monitoring and estimation of soybean and corn yields in the U.S. is essen tial for improving global food security.” Funders for this research include Unveiling The List of Hanging; Basic And Appli ed Basic Research Foundation of Guangdong Province. The news journalists obtained a quote from the research from Sun Yat-sen Univers ity: “However, there is currently a lack of publicly available spatial distribut ion datasets with high temporal and spatial resolution for U.S. corn and soybean yields, which hampers related research and policy-making. Therefore, in this st udy, we proposed a statistical downscaling framework to produce spatially explic it crop yield estimates by utilizing multisource environmental covariates and en semble machine learning methods. We produced distribution maps of soybean and co rn yields in the U.S. from 2006 to 2021 at a 1-km resolution through the optimal Cubist model, resulting in the USASoy&CornYield1km dataset. The re sults demonstrated stable accuracy, with R2 values for corn ranging from 0.70 to 0.89 (average of 0.80) and for soybeans ra nging from 0.74 to 0.90 (average of 0.81) during the period 2006-2021. Compariso n with the spatial production allocation model (SPAM) dataset further confirmed the reliability of this dataset, with correlations of 0.84 for soybeans and 0.78 for corn when compared to SPAM2010. Spatial uncertainty analysis showed that th e yield estimation uncertainty was 14.04% for soybeans and 20.49% for corn, indicating a generally low level of uncertainty.”
Sun Yat-sen UniversityZhuhaiPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningRemo te Sensing
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