Lin Haonan, Wei Zhenbo, Chen Changqing, Huang Yun, Zhu Jianxi
Department of Biosystems Engineering, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China.
Zhejiang Academic of Agricultural Machinery, 1158 Zhihe Road, Jinhua 321051, China.
Sensors (Basel). 2024 May 14;24(10):3105. doi: 10.3390/s24103105.
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.
由于腐烂症状不明显、储存密度高以及环境因素（如温度、湿度和环境气体），早期识别腐烂土豆是储存设施中最重要的挑战之一。开发了一种基于集成卷积神经网络（ECNN，一种强大的特征提取方法）的电子鼻系统来检测不同腐烂程度的土豆。检测了三种类型的土豆：正常样本、轻度腐烂样本和完全腐烂样本。提出了一种特征离散化方法，通过消除特征中的冗余信息来优化环境气体对电子鼻信号的影响。基于原始特征的ECNN在实验室和储存环境中对腐烂土豆的预测都取得了良好的结果，预测结果的准确率分别为94.70%和90.76%。此外，特征离散化方法的应用显著提高了预测结果，预测结果的准确率分别提高了1.59%和3.73%。最重要的是，电子鼻系统使用ECNN在三种类型土豆的识别中表现良好，并且所提出的特征离散化方法有助于减少环境气体的干扰。
