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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Image 7 Information Relationship Model.jpg|200px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''[[Information]]''', in its most restricted technical sense, is a sequence of symbols that can be interpreted as a message, recorded as signs, or transmitted as signals. Conceptually, information is the message (utterance or expression) being conveyed. Therefore, in a general sense, information is "knowledge communicated or received concerning a particular fact or circumstance."
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


From the stance of information theory, information is taken as a sequence of symbols from an alphabet, say an input alphabet χ, and an output alphabet ϒ. Information processing consists of an input-output function that maps any input sequence from χ into an output sequence from ϒ. The mapping may be probabilistic or determinate. It may have memory or be memoryless.
[[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 />
 
''Recently featured'':
Information cannot be predicted and resolves uncertainty. The uncertainty of an event is measured by its probability of occurrence and is inversely proportional to that. The more uncertain an event, the more information is required to resolve uncertainty of that event. The amount of information is measured in bits. The concept that ''information is the message'' has different meanings in different contexts. Thus the concept of information becomes closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, understanding, stimulation, pattern, perception, representation, and entropy. ('''[[Information|Full article...]]''')<br />
{{flowlist |
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* [[Journal:A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model|A blockchain-driven IoT-based food quality traceability system for dairy products using a deep learning model]]
''Recently featured'': [[Clinical laboratory]], [[Hospital information system]], [[Imaging informatics]],
* [[Journal:Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study|Effect of good clinical laboratory practices (GCLP) quality training on knowledge, attitude, and practice among laboratory professionals: Quasi-experimental study]]
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* [[Journal:GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration|GitHub as an open electronic laboratory notebook for real-time sharing of knowledge and collaboration]]
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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...)
Recently featured:

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