Watermelon Sugar Content Detection and Grading System Based on Acoustic Characteristics

被引：0

作者：

Zuo J. ^{[1
,2
]}

Peng Y. ^{[1
,2
]}

Li Y. ^{[1
,2
]}

Zou W. ^{[1
,2
]}

Zhao X. ^{[1
,2
]}

Sun C. ^{[1
,2
]}

机构：

[1] College of Engineering, China Agricultural University, Beijing

[2] National R&D Center for Agro-processing Equipment, Beijing

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2022年 / 53卷

关键词：

acoustic; classification; machine learning; non-destructive detection; watermelon;

D O I：

10.6041/j.issn.1000-1298.2022.S1.035

中图分类号：

学科分类号：

摘要：

Sugar content is one of the important indicators for watermelon grading, for the drawbacks of traditional watermelon detection methods, the feasibility of acoustic characteristics combined with machine learning for non-destructive detection and grading of watermelon was investigated. The acoustic detection system of watermelon was designed and the time domain signals of different batches of samples were collected. After the time domain signal was normalized, the frequency domain signal was obtained by fast Fourier transform and pre-processed by detrending. The principal components of the frequency domain signal were extracted by using principal component analysis, the cumulative contribution rate of the first three principal components was 95. 32%, the samples with different levels were differentiable using the first and second principal components. Watermelon all-variable grading models were developed by using four different machine learning algorithms, and the prediction set classification accuracies all reached over 66% . Feature variables were extracted by using stability competitive adapative reweighted sampling algorithm, which reduced the number of variables by about 84% . The performance of the classification models developed using the extracted feature variables were all improved, with the support vector machine model achieved the highest prediction set accuracy (95. 56% ), Fl score (96% ) and Kappa coefficient (93% ) . The results indicated that acoustic characterization combined with machine learning was feasible for non-destructive detection and grading of watermelons. The research result can provide a feasible technical solution for non-destructive detection and grading of watermelon, and provide a reference for non-destructive detection and grading of other similar fruits and vegetables. © 2022 Chinese Society of Agricultural Machinery. All rights reserved.

引用

页码：316 / 323

页数：7

共 26 条

[11] WEI Yanjun, RAO Xiuqin, QI Bing, Acoustic detecting system for sugar content of watermelon, Transactions of the CSAE, 28, 3, pp. 283-287, (2012)
[12] DING C Q, WANG D C, FENG Z, Et al., Extracting and modifying the vibration characteristic parameters of watermelon based on experimental modal measurement and finite element analysis for hollow heart defect detection, Transactions of the ASABE, 65, 1, pp. 151-167, (2022)
[13] CHAWGIEN K, KIATTISIN S., Machine learning techniques for classifying the sweetness of watermelon using acoustic signal and image processing, Computers and Electronics in Agriculture, 181, (2021)
[14] YANG Liwei, HUANG Jiayun, ZHANG Jiqin, Et al., Mass flow measurement system of granular fertilizer based on microwave Doppler method [J], Transactions of the Chinese Society for Agricultural Machinery, 51, pp. 210-217, (2020)
[15] YAO Yumei, YUAN Xiangru, HAN Lujia, Et al., Microstructures and properties of bovine bone collagen polypeptide composite films with different molecular weight distributions, Transactions of the Chinese Society for Agricultural Machinery, 51, 6, pp. 318-325, (2020)
[16] CHEN X, YUAN P P, DENG X Y., Watermelon ripeness detection by wavelet multiresolution decomposition of acoustic impulse response signals[J], Postharvest Biology and Technology, 142, pp. 135-141, (2018)
[17] SHEN W, JI N, YIN Y, Et al., Fusion of acoustic and deep features for pig cough sound recognition, Computers and Electronics in Agriculture, 197, (2022)
[18] ZENG W, HUANG X, ARISONA S M, Et al., Classifying watermelon ripeness by analysing acoustic signals using mobile devices[J], Personal and Ubiquitous Computing, 18, 7, pp. 1753-1762, (2014)
[19] Applied vibro acoustic
[20] CUI Yulu, YANG Wei, WANG Weichao, Et al., Design and experiment of portable soil organic matter detector based on spectroscopy principle[J], Transactions of the Chinese Society for Agricultural Machinery, 52, pp. 323-328, (2021)

← 1 2 3 →