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 Villegas-Pérez Foods23 12-22.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 quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for ''Trichinella spp.'']]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[Trichinosis|Trichinellosis]] is a parasitic foodborne zoonotic disease transmitted by ingestion of raw or undercooked meat containing the first larval stage (L1) of the nematode. To ensure the [[Quality (business)|quality]] and safety of food intended for human consumption, meat inspection for detection of ''Trichinella'' spp. larvae is a mandatory procedure per European Union (E.U.) regulations. The implementation of [[quality assurance]] (QA) practices in [[Laboratory|laboratories]] that are responsible for ''Trichinella'' spp. detection is essential given that the detection of this parasite is still a pivotal threat to public health, and it is included in List A of Annex I, Directive 2003/99/EC, which determines the agents to be monitored on a mandatory basis ... ('''[[Journal:A quality assurance discrimination tool for the evaluation of satellite laboratory practice excellence in the context of European regulatory meat inspection for Trichinella spp.|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...)
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