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:Fig2 DiNardo Toxins2020 12-4.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The diffusion of the legalization of [[wikipedia:Cannabis|cannabis]] for recreational, medicinal, and nutraceutical uses requires the development of adequate analytical methods to assure the safety and security of such products. In particular, [[wikipedia:Aflatoxin|aflatoxins]] are considered to pose a major risk for the health of cannabis consumers. Among analytical methods that allow for adequate monitoring of food safety, [[immunoassay]]s play a major role thanks to their cost-effectiveness, high-throughput capacity, simplicity, and limited requirement for equipment and skilled operators. Therefore, a rapid and sensitive [[enzyme immunoassay]] has been adapted to measure the most hazardous [[wikipedia:Aflatoxin B1|aflatoxin B<sub>1</sub>]] in cannabis products. The assay was acceptably accurate (recovery rate: 78–136%), reproducible (intra- and inter-assay means coefficients of variation 11.8% and 13.8%, respectively), and sensitive (limit of detection and range of quantification: 0.35 ng mL<sup>−1</sup> and 0.4–2 ng mL<sup>−1</sup> ... ('''[[Journal:Enzyme immunoassay for measuring aflatoxin B1 in legal cannabis|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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