An Accuracy Improvement Method Based on Multi-Source Information Fusion and Deep Learning for TSSC and Water Content Nondestructive Detection in "Luogang" Orange

文献类型： 外文期刊

第一作者： Xu, Sai

作者： Xu, Sai;Xu, Sai;Lu, Huazhong;Lu, Huazhong;Zhang, Qianqian;Ference, Christopher

作者机构：

关键词： quality detection; accuracy improvement; information fusion; deep learning; orange

期刊名称：ELECTRONICS （ 影响因子：2.397； 五年影响因子：2.408 ）

ISSN：

年卷期： 2021 年 10 卷 1 期

页码：

收录情况： SCI

摘要： The objective of this study was to find an efficient method for measuring the total soluble solid content (TSSC) and water content of "Luogang" orange. Quick, accurate, and nondestructive detection tools (VIS/NIR spectroscopy, NIR spectroscopy, machine vision, and electronic nose), four data processing methods (Savitzky-Golay (SG), genetic algorithm (GA), multi-source information fusion (MIF), convolutional neural network (CNN) as the deep learning method, and a partial least squares regression (PLSR) modeling method) were compared and investigated. The results showed that the optimal TSSC detection method was based on VIS/NIR and machine vision data fusion and processing and modeling by SG + GA + CNN + PLSR. The R-2 and RMSE of the TSSC detection results were 0.8580 and 0.4276, respectively. The optimal water content detection result was based on VIS/NIR data and processing and modeling by SG + GA + CNN + PLSR. The R-2 and RMSE of the water content detection results were 0.7013 and 0.0063, respectively. This optimized method largely improved the internal quality detection accuracy of "Luogang" orange when compared to the data from a single detection tool with traditional data processing method, and provides a reference for the accuracy improvement of internal quality detection of other fruits.

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