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An Apple Fungal Infection Detection Model Based on BPNN Optimized by Sparrow Search Algorithm

文献类型： 外文期刊

作者： Zhao, Changtong 1 ; Ma, Jie 1 ; Jia, Wenshen 1 ; Wang, Huihua 3 ; Tian, Hui 1 ; Wang, Jihua 2 ; Zhou, Wei 4 ;

作者机构： 1.Beijing Informat Sci & Technol Univ, Mech Elect Engn Sch, Beijing 100192, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Inst Qual Stand & Testing Technol, Beijing 100097, Peoples R China

3.Beijing Vocat Coll Agr, Dept Food & Bioengn, Beijing 102206, Peoples R China

4.Hebei Food Inspect & Res Inst, Hebei Food Safety Key Lab, Shijiazhuang 050091, Hebei, Peoples R China

关键词： electronic nose; fungal infection; sparrow search algorithm; apples

期刊名称：BIOSENSORS-BASEL （ 影响因子：5.743； 五年影响因子：5.972 ）

ISSN：

年卷期： 2022 年 12 卷 9 期

页码：

收录情况： SCI

摘要： To rapidly detect whether apples are infected by fungi, a portable electronic nose was used in this study to collect the gas information from apples, and the collected information was processed by smoothing filtering, data dimensionality reduction, and outlier removal. Following this, we utilized K-nearest neighbors (KNN), random forest (RF), support vector machine (SVM), a convolutional neural network (CNN), a back-propagation neural network (BPNN), a particle swarm optimization-back-propagation neural network (PSO-BPNN), a gray wolf optimization-backward propagation neural network (GWO-BPNN), and a sparrow search algorithm-backward propagation neural network (SSA-BPNN) model to discriminate apple samples, and adopted the 10-fold cross-validation method to evaluate the performance of each model. The results show that SSA can effectively optimize the performance of the BPNN, such that the recognition accuracy of the optimized SSA-BPNN model reaches 98.40%. This study provides an important reference value for the application of an electronic nose in the non-destructive and rapid detection of fungal infection in apples.

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