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:Tab2 Valdes-Donoso CaliforniaAg2019 73-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Costs of mandatory cannabis testing in California|Costs of mandatory cannabis testing in California]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Every batch of [[wikipedia:Cannabis|cannabis]] sold legally in California must be tested for more than 100 contaminants. These contaminants include 66 pesticides, for 21 of which the state's tolerance is zero. For many other substances, tolerance levels are much lower than those allowed for food products in California. This article reviews the state's testing [[Regulatory compliance|regulations]] in context—including maximum allowable tolerance levels—and uses primary data collected from California's major cannabis testing [[Laboratory|laboratories]] and several cannabis testing equipment manufacturers, as well as a variety of expert opinions, to estimate the cost per pound of testing under the state's framework. We also estimate the cost of collecting [[Sample (material)|samples]], which depends on the distance between cannabis distributors and laboratories. We find that, if a batch fails mandatory tests, the value of cannabis that must be destroyed accounts for a large share of total testing costs, more than the cost of the tests that laboratories perform. Findings from this article will help readers understand the effects of California's testing regime on the price of legal cannabis in the state, and understand how testing may add value to products that have passed a series of tests that aim to validate their safety. ('''[[Journal:Costs of mandatory cannabis testing in California|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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