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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Denuders.jpg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
A '''[[denuder]]''' is a cylindrical or annular conduit or tube internally coated with a reagent that selectively reacts with a stable flow of gas drawn through the conduit. The gas molecules diffuse to the walls while the [[analyte]] contained in the gas is transmitted outwards via laminar flow, collected, and analyzed. Effectiveness of the system depends primarily "on a complete discrimination between the gas species and particulate matter."
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Additional non-linear denuder geometries have also been tried with mixed results. Coiled configurations increased collection efficiency but lost larger particulate. A parallel multi-tube diffusion denuder has also been tried and found to increase collection efficiency. Other geometries include honeycombed, annular, and parallel plate. The development of the annular denuder in particular allowed researchers to overcome the inefficiencies of cylindrical denuders, allowing operation at larger flow rates (up to 30 times that of cylindrical denuders), shorter sampling periods, and less particle loss. ('''[[Denuder|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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