Wavelet Scattering Convolution Network-Based Detection Algorithm on Nondestructive Microcrack Electrical Signals of Eggs

Chenbo Shi, Yanhong Cheng, Chun Zhang, Jin Yuan, Yuxin Wang, Xin Jiang and Changsheng Zhu ()
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Chenbo Shi: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Yanhong Cheng: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Chun Zhang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Jin Yuan: College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, China
Yuxin Wang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Xin Jiang: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China
Changsheng Zhu: College of Intelligent Equipment, Shandong University of Science and Technology, Tai’an 271019, China

Agriculture, 2023, vol. 13, issue 3, 1-19

Abstract: The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 μ m at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection.

Keywords: nondestructive detection; poultry eggs; wavelet scattering convolutional network; microcurrent signal analysis; egg’s electrical characteristic model (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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