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:Fig1 Green PubHlthRsPract2018 28-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Childhood obesity prevalence is an issue of international public health concern, and governments have a significant role to play in its reduction. The Healthy Children Initiative (HCI) has been delivered in New South Wales (NSW), Australia, since 2011 to support implementation of childhood obesity prevention programs at scale. Consequently, a system to support local implementation and data collection, analysis, and reporting at local and state levels was necessary. The Population Health Information Management System (PHIMS) was developed to meet this need.
[[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 />
 
A collaborative and iterative process was applied to the design and development of the system. The process comprised identifying technical requirements, building system infrastructure, delivering training, deploying the system, and implementing quality measures. ('''[[Journal:Codesign of the Population Health Information Management System to measure reach and practice change of childhood obesity programs|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...)
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