Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig3 Krill Metabolites2020 10-7.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass|A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[wikipedia:Cannabis|Cannabis]] and its secondary metabolite content have recently seen a surge in research interest. Cannabis [[wikipedia:Terpene|terpenes]] and [[wikipedia:Terpenoid|terpenoids]] in particular are increasingly the focus of research efforts due to the possibility of their contribution to the overall therapeutic effect of [[wikipedia:Medical cannabis|medicinal cannabis]]. Current methodology to quantify terpenes in cannabis biomass mostly relies on large quantities of biomass, long extraction protocols, and long [[gas chromatography]] (GC) gradient times, often exceeding 60 minutes. They are therefore not easily applicable in the high-throughput environment of a cannabis breeding program. ('''[[Journal:A high-throughput method for the comprehensive analysis of terpenes and terpenoids in medicinal cannabis biomass|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
Recently featured:

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