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Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe

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  • Asadollah, Seyed Babak Haji Seyed
  • Jodar-Abellan, Antonio
  • Pardo, Miguel Ángel

Abstract

Accurate estimating of crop yield is crucial for developing effective global food security strategies which can lead to reduce of hunger and more sustainable development. However, predicting crop yields is a complex task as it requires frequent monitoring of many weather and socio-economic factors over an extended period. Satellite remote sensing products have become a reliable source for climate-based variables. They are easier to obtain and provide detailed spatial and temporal coverage.

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  • Asadollah, Seyed Babak Haji Seyed & Jodar-Abellan, Antonio & Pardo, Miguel Ángel, 2024. "Optimizing machine learning for agricultural productivity: A novel approach with RScv and remote sensing data over Europe," Agricultural Systems, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:agisys:v:218:y:2024:i:c:s0308521x24001057
    DOI: 10.1016/j.agsy.2024.103955
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    1. Jodar-Abellan, Antonio & Albaladejo-García, José Antonio & Aznar-Crespo, Pablo & de los Santos, Manuel Ballesta & Seyed Asadollah, Seyed Babak Haji & Derdour, Abdessamed & Martínez-Nicolás, Juan José , 2024. "Exploring expert perceptions towards emerging pollutants and their impacts in reused wastewater and agriculture," Agricultural Water Management, Elsevier, vol. 304(C).

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