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Non‐destructive classification of apple bruising time based on visible and near‐infrared hyperspectral imaging
Author(s) -
Pan Xuyang,
Sun Laijun,
Li Yingsong,
Che Wenkai,
Ji Yamin,
Li Jinlong,
Li Jie,
Xie Xu,
Xu Yuantong
Publication year - 2018
Publication title -
journal of the science of food and agriculture
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.782
H-Index - 142
eISSN - 1097-0010
pISSN - 0022-5142
DOI - 10.1002/jsfa.9360
Subject(s) - hyperspectral imaging , vnir , artificial intelligence , pattern recognition (psychology) , random forest , linear discriminant analysis , computer science , decision tree , mathematics , remote sensing , geography
BACKGROUND Bruising time of apple is one of the most important factors for internal quality assessment. The present study aimed to establish a non‐destructive method for the classification of apple bruising time using visible and near‐infrared (VNIR) hyperspectral imaging. In this study, VNIR hyperspectral images were obtained and analyzed at seven bruising periods. Moreover, regions of interest (ROIs) were chosen to construct the bruised region classification model, and spectra of bruised regions were collected and resampled based on four different methods. Subsequently, machine learning algorithms were employed and used for dealing with the time classification model of apples. In order to reduce data redundancy and improve the accuracy of the classification model, a tree‐based assembling learning model was used to select feature wavelengths, and linear discriminant analysis (LDA) was used to improve the discernibility of data. RESULTS The results revealed that the random forest (RF) model can precisely locate bruised regions, while the gradient boosting decision tree (GBDT) model can validly classify apple bruising times with 70.59% accuracy. Data of 128 wavebands were compressed to 13 wavebands, providing a high accuracy of 92.86%. CONCLUSION The results prove that the hyperspectral technique can be used for predicting apple bruising time, which will help to assess the internal quality and safety of apples. © 2018 Society of Chemical Industry