In-situ and non-destructive grape quality discrimination via field spectroradiometer and machine learning models
2024 International Conference on Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS)(2024)
Abstract
The grape composition impacts the subsequent wine quality. Timely non-destructive assessing grape quality demands rapid and highly accurate estimation, which is often quantified using spectroscopic analyses under the lab condition. However, non-destructive assessing grape quality in vineyard remains challenging. This work evaluated using field spectroscopy to assess total soluble solids (TSS) in grape non-destructively. We used in-field spectral database of Pinot Noir to develop classification models to discriminate grape TSS. Classification models of TSS had accuracy ranged from 0.73 to 0.9 for a test dataset. This study showed field spectroscopic analysis could discriminate grape TSS rapidly and non-destructively.
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Key words
grape quality,field spectroscopy,machine learning,non-destructive
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