Deep Learning and Hyperspectral Images Based Tomato Soluble Solids Content and Firmness Estimation

文献类型： 外文期刊

第一作者： Xiang, Yun

作者： Xiang, Yun;Chen, Qijun;Su, Zhongjing;Zhang, Lu;Chen, Zuohui;Xuan, Qi;Zhou, Guozhi;Yao, Zhuping;Cheng, Yuan

作者机构：

关键词： hyperspectral imaging; deep learning; cherry tomato; soluble solids content; firmness; one-dimensional convolutional neural networks

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 13 卷

页码：

收录情况： SCI

摘要： Cherry tomato (Solanum lycopersicum) is popular with consumers over the world due to its special flavor. Soluble solids content (SSC) and firmness are two key metrics for evaluating the product qualities. In this work, we develop non-destructive testing techniques for SSC and fruit firmness based on hyperspectral images and the corresponding deep learning regression model. Hyperspectral reflectance images of over 200 tomato fruits are derived with the spectrum ranging from 400 to 1,000 nm. The acquired hyperspectral images are corrected and the spectral information are extracted. A novel one-dimensional (1D) convolutional ResNet (Con1dResNet) based regression model is proposed and compared with the state of art techniques. Experimental results show that, with a relatively large number of samples our technique is 26.4% better than state of art technique for SSC and 33.7% for firmness. The results of this study indicate the application potential of hyperspectral imaging technique in the SSC and firmness detection, which provides a new option for non-destructive testing of cherry tomato fruit quality in the future.

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