Dachen Wang , Yilei Hu , Jiaqi Xiong , Yibin Ying , Ce Yang , Di Cui
{"title":"Laser Doppler vibrometer enables in-situ monitoring of peach firmness","authors":"Dachen Wang , Yilei Hu , Jiaqi Xiong , Yibin Ying , Ce Yang , Di Cui","doi":"10.1016/j.biosystemseng.2024.09.013","DOIUrl":null,"url":null,"abstract":"<div><div>Fruit firmness is a measure of the edible quality and maturity of peaches. In-situ monitoring of peach firmness can aid in fruit quality control and determining the optimal harvest time according to market demand. In this study, a non-contact acoustic vibration-based method was proposed for in-situ monitoring of fruit firmness of on-tree peaches. A new design of a compressed air excitation unit was constructed to impact the peach on the tree and a laser Doppler vibrometer was adopted to measure the acoustic vibration response (AVR) of the peach. To isolate the vibration information characterising fruit firmness, the AVR was firstly pre-processed by the wavelet threshold denoising method and then analysed by the autoregressive method to acquire the power spectral density (PSD) of the peach. For effectively extracting vibration features from the PSD to predict peach firmness, a novel one-dimensional convolutional neural network (CNN<sub>m</sub>) with multiscale perceptual fields was constructed. The performance of CNN<sub>m</sub> was compared with those of partial least squares regression, support vector regression models, and a single-branch 1D-CNN model with the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (<em>R</em><sup><em>2</em></sup>), and residual prediction deviation (<em>RPD</em>). The results indicated that the proposed method enabled in-situ monitoring of peach firmness and the established CNN<sub>m</sub> model performed better than other models in predicting peach firmness (<span><math><mrow><msubsup><mi>R</mi><mi>P</mi><mn>2</mn></msubsup></mrow></math></span> = 0.813, MAEP = 1.636 N mm<sup>−1</sup>, RMSEP = 2.501 N mm<sup>−1</sup>, and <span><math><mrow><msub><mrow><mi>R</mi><mi>P</mi><mi>D</mi></mrow><mi>P</mi></msub></mrow></math></span> = 2.334).</div></div>","PeriodicalId":9173,"journal":{"name":"Biosystems Engineering","volume":"247 ","pages":"Pages 191-201"},"PeriodicalIF":4.4000,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biosystems Engineering","FirstCategoryId":"97","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1537511024002150","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRICULTURAL ENGINEERING","Score":null,"Total":0}
引用次数: 0
Abstract
Fruit firmness is a measure of the edible quality and maturity of peaches. In-situ monitoring of peach firmness can aid in fruit quality control and determining the optimal harvest time according to market demand. In this study, a non-contact acoustic vibration-based method was proposed for in-situ monitoring of fruit firmness of on-tree peaches. A new design of a compressed air excitation unit was constructed to impact the peach on the tree and a laser Doppler vibrometer was adopted to measure the acoustic vibration response (AVR) of the peach. To isolate the vibration information characterising fruit firmness, the AVR was firstly pre-processed by the wavelet threshold denoising method and then analysed by the autoregressive method to acquire the power spectral density (PSD) of the peach. For effectively extracting vibration features from the PSD to predict peach firmness, a novel one-dimensional convolutional neural network (CNNm) with multiscale perceptual fields was constructed. The performance of CNNm was compared with those of partial least squares regression, support vector regression models, and a single-branch 1D-CNN model with the mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2), and residual prediction deviation (RPD). The results indicated that the proposed method enabled in-situ monitoring of peach firmness and the established CNNm model performed better than other models in predicting peach firmness ( = 0.813, MAEP = 1.636 N mm−1, RMSEP = 2.501 N mm−1, and = 2.334).
期刊介绍:
Biosystems Engineering publishes research in engineering and the physical sciences that represent advances in understanding or modelling of the performance of biological systems for sustainable developments in land use and the environment, agriculture and amenity, bioproduction processes and the food chain. The subject matter of the journal reflects the wide range and interdisciplinary nature of research in engineering for biological systems.