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Early Identification of Rotten Potatoes Using an Electronic Nose Based on Feature Discretization and Ensemble Convolutional Neural Network

文献类型： 外文期刊

作者： Lin, Haonan 1 ; Wei, Zhenbo 1 ; Chen, Changqing 2 ; Huang, Yun 2 ; Zhu, Jianxi 2 ;

作者机构： 1.Zhejiang Univ, Dept Biosyst Engn, 866 Yuhangtang Rd, Hangzhou 310058, Peoples R China

2.Zhejiang Acad Agr Machinery, 1158 Zhihe Rd, Jinhua 321051, Peoples R China

关键词： rotten potato; electronic nose; ensemble neural network; discrete analysis; pattern recognition methods

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2024 年 24 卷 10 期

页码：

收录情况： SCI

摘要： The early identification of rotten potatoes is one of the most important challenges in a storage facility because of the inconspicuous symptoms of rot, the high density of storage, and environmental factors (such as temperature, humidity, and ambient gases). An electronic nose system based on an ensemble convolutional neural network (ECNN, a powerful feature extraction method) was developed to detect potatoes with different degrees of rot. Three types of potatoes were detected: normal samples, slightly rotten samples, and totally rotten samples. A feature discretization method was proposed to optimize the impact of ambient gases on electronic nose signals by eliminating redundant information from the features. The ECNN based on original features presented good results for the prediction of rotten potatoes in both laboratory and storage environments, and the accuracy of the prediction results was 94.70% and 90.76%, respectively. Moreover, the application of the feature discretization method significantly improved the prediction results, and the accuracy of prediction results improved by 1.59% and 3.73%, respectively. Above all, the electronic nose system performed well in the identification of three types of potatoes by using the ECNN, and the proposed feature discretization method was helpful in reducing the interference of ambient gases.

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