Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data.

ABSTRACT: In the current work, we attempt to leverage the fewer wavelengths and samples to develop a classification model for classifying hard and soft blueberries using near infrared (NIR) data. To do this, random frog selection and active learning approaches are used in the spectral space and the sample queue, respectively. To reduce the spectral number, a random frog spectral selection approach was applied to collect wavelengths informative with hardness. Prediction model based on 22 selected spectra gave slightly better results than that based on the full spectra. In terms of the selection operation in the sample space, the query by committee was validated to be suitable for blueberry hardness classification with the accuracy, precision and recall of 78%, 74% and 98% when taking only 25 sample q

SUBMITTER: Hu M 

PROVIDER: S-EPMC5923227 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature