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

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

Agricultural Systems, 2024, vol. 218, issue C

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.

Keywords: Crop yield; Remote sensing; Machine learning; Randomized search; Agricultural prediction (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:218:y:2024:i:c:s0308521x24001057

DOI: 10.1016/j.agsy.2024.103955

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