Laser Doppler vibrometer enables in-situ monitoring of peach firmness

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 (CNN_m) with multiscale perceptual fields was constructed. The performance of CNN_m 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 CNN_m model performed better than other models in predicting peach firmness ($R_P^2$ = 0.813, MAEP = 1.636 N mm⁻¹, RMSEP = 2.501 N mm⁻¹, and ${RPD}_P$ = 2.334).