2022
DOI: 10.3390/agriculture12101652
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The Use of Fluorescence Spectroscopic Data and Machine-Learning Algorithms to Discriminate Red Onion Cultivar and Breeding Line

Abstract: The objective of this study was to evaluate differences between the red onion cultivar and breeding line using models based on selected fluorescence spectroscopic data built using machine-learning algorithms from different groups of Trees, Functions, Bayes, Meta, Rules, and Lazy. The combination of fluorescence spectroscopy and machine learning is an original approach to the non-destructive and objective discrimination of red onion samples. The selected fluorescence spectroscopic data were used to build models… Show more

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Cited by 2 publications
(2 citation statements)
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“…ML uses a combination of algorithms that parse data and then applies what it learns to make an informed decision [ 30 ]. Because machine learning uses data to feed an algorithm that can understand the relationship between input and output, it requires little human intervention after deployment [ 31 ]. Machine learning models can provide their own predictions based on the received amount of data input [ 32 ].…”
Section: Methodsmentioning
confidence: 99%
“…ML uses a combination of algorithms that parse data and then applies what it learns to make an informed decision [ 30 ]. Because machine learning uses data to feed an algorithm that can understand the relationship between input and output, it requires little human intervention after deployment [ 31 ]. Machine learning models can provide their own predictions based on the received amount of data input [ 32 ].…”
Section: Methodsmentioning
confidence: 99%
“…Artificial intelligence-based methods are popular today to automatically perform these pixel-based analyses. In this context, different machine learning and deep learning-based studies [24][25][26][27][28] are frequently used to distinguish different agricultural products. Artificial intelligence contributes to the rapid digital transformation and growing power of the agriculture industry.…”
Section: Introductionmentioning
confidence: 99%