Advancing agricultural research using machine learning algorithms

Abstract Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, a...

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Autores principales:	Spyridon Mourtzinis, Paul D. Esker, James E. Specht, Shawn P. Conley
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b24c1ea5518c4a0baa41b5a535eaecac
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spelling	oai:doaj.org-article:b24c1ea5518c4a0baa41b5a535eaecac2021-12-02T14:58:47ZAdvancing agricultural research using machine learning algorithms10.1038/s41598-021-97380-72045-2322https://doaj.org/article/b24c1ea5518c4a0baa41b5a535eaecac2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97380-7https://doaj.org/toc/2045-2322Abstract Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.Spyridon MourtzinisPaul D. EskerJames E. SpechtShawn P. ConleyNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Spyridon Mourtzinis Paul D. Esker James E. Specht Shawn P. Conley Advancing agricultural research using machine learning algorithms
description	Abstract Rising global population and climate change realities dictate that agricultural productivity must be accelerated. Results from current traditional research approaches are difficult to extrapolate to all possible fields because they are dependent on specific soil types, weather conditions, and background management combinations that are not applicable nor translatable to all farms. A method that accurately evaluates the effectiveness of infinite cropping system interactions (involving multiple management practices) to increase maize and soybean yield across the US does not exist. Here, we utilize extensive databases and artificial intelligence algorithms and show that complex interactions, which cannot be evaluated in replicated trials, are associated with large crop yield variability and thus, potential for substantial yield increases. Our approach can accelerate agricultural research, identify sustainable practices, and help overcome future food demands.
format	article
author	Spyridon Mourtzinis Paul D. Esker James E. Specht Shawn P. Conley
author_facet	Spyridon Mourtzinis Paul D. Esker James E. Specht Shawn P. Conley
author_sort	Spyridon Mourtzinis
title	Advancing agricultural research using machine learning algorithms
title_short	Advancing agricultural research using machine learning algorithms
title_full	Advancing agricultural research using machine learning algorithms
title_fullStr	Advancing agricultural research using machine learning algorithms
title_full_unstemmed	Advancing agricultural research using machine learning algorithms
title_sort	advancing agricultural research using machine learning algorithms
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b24c1ea5518c4a0baa41b5a535eaecac
work_keys_str_mv	AT spyridonmourtzinis advancingagriculturalresearchusingmachinelearningalgorithms AT pauldesker advancingagriculturalresearchusingmachinelearningalgorithms AT jamesespecht advancingagriculturalresearchusingmachinelearningalgorithms AT shawnpconley advancingagriculturalresearchusingmachinelearningalgorithms
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Advancing agricultural research using machine learning algorithms

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