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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:CarrierCloud.png|180px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''[[Carrier cloud]]''' is class of [[cloud computing]] service that merges the high-performance capabilities and reliability of a communications service provider's network with the lower costs and flexibility provided by traditional public cloud services. The carrier cloud attempts to remove the data bottleneck and security issues that often occur in and to the virtualized data center due to lack of control of data flow over the public Internet.
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


Carrier cloud service is similar to public cloud service in that infrastructures are converged into a single, optimized computing package, and services are shared across a group or organization. Carrier cloud service, however, utilizes the existing and upgraded network structures of the communication service provider (CSP) to provide end-to-end services over their own network. Since the CSP more readily controls the data flow through its content delivery networks and/or dedicated virtual private networks, it can better manage issues with bandwidth, latency, and jitter. Additional "last mile" carrier-grade services already provided by CSPs in cities also "offset the latencies associated with cross-country or inter-continental backhaul." ('''[[Carrier cloud|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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