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Arun Saco, P. Shanmuga Sundari, Karthikeyan J and Anand Paul
In recent years, machine learning algorithms have been applied in many real-time applications. Crises in the energy sector are the primary challenges experienced today among all countries across the globe, regardless of their economic status. There is a ...
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Lifei Wei, Yangxi Zhang, Ziran Yuan, Zhengxiang Wang, Feng Yin and Liqin Cao
Soil total arsenic (TAs) contamination caused by human activities?such as mining, smelting, and agriculture?is a problem of global concern. Visible/near-infrared (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIB...
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Pablo Salvador, Diego Gómez, Julia Sanz and José Luis Casanova
Crop growth modeling and yield forecasting are essential to improve food security policies worldwide. To estimate potato (Solanum tubersum L.) yield over Mexico at a municipal level, we used meteorological data provided by the ERA5 (ECMWF Re-Analysis) da...
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Lifei Wei, Ziran Yuan, Yanfei Zhong, Lanfang Yang, Xin Hu and Yangxi Zhang
Hyperspectral remote sensing can be used to effectively identify contaminated elements in soil. However, in the field of monitoring soil heavy metal pollution, hyperspectral remote sensing has the characteristics of high dimensionality and high redundanc...
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Miguel Garcia-Hidalgo,Ángela Blázquez-Casado,Beatriz Águeda,Francisco Rodriguez
Pág. eSC03
Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde ...
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