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Bluster or Lustre: Can AI Improve Crops and Plant Health?

In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as...

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Autores principales: Gardiner, Laura-Jayne, Krishna, Ritesh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707749/
https://www.ncbi.nlm.nih.gov/pubmed/34961177
http://dx.doi.org/10.3390/plants10122707
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author Gardiner, Laura-Jayne
Krishna, Ritesh
author_facet Gardiner, Laura-Jayne
Krishna, Ritesh
author_sort Gardiner, Laura-Jayne
collection PubMed
description In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question—Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences.
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spelling pubmed-87077492021-12-25 Bluster or Lustre: Can AI Improve Crops and Plant Health? Gardiner, Laura-Jayne Krishna, Ritesh Plants (Basel) Review In a changing climate where future food security is a growing concern, researchers are exploring new methods and technologies in the effort to meet ambitious crop yield targets. The application of Artificial Intelligence (AI) including Machine Learning (ML) methods in this area has been proposed as a potential mechanism to support this. This review explores current research in the area to convey the state-of-the-art as to how AI/ML have been used to advance research, gain insights, and generally enable progress in this area. We address the question—Can AI improve crops and plant health? We further discriminate the bluster from the lustre by identifying the key challenges that AI has been shown to address, balanced with the potential issues with its usage, and the key requisites for its success. Overall, we hope to raise awareness and, as a result, promote usage, of AI related approaches where they can have appropriate impact to improve practices in agricultural and plant sciences. MDPI 2021-12-09 /pmc/articles/PMC8707749/ /pubmed/34961177 http://dx.doi.org/10.3390/plants10122707 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Gardiner, Laura-Jayne
Krishna, Ritesh
Bluster or Lustre: Can AI Improve Crops and Plant Health?
title Bluster or Lustre: Can AI Improve Crops and Plant Health?
title_full Bluster or Lustre: Can AI Improve Crops and Plant Health?
title_fullStr Bluster or Lustre: Can AI Improve Crops and Plant Health?
title_full_unstemmed Bluster or Lustre: Can AI Improve Crops and Plant Health?
title_short Bluster or Lustre: Can AI Improve Crops and Plant Health?
title_sort bluster or lustre: can ai improve crops and plant health?
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8707749/
https://www.ncbi.nlm.nih.gov/pubmed/34961177
http://dx.doi.org/10.3390/plants10122707
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