Simultaneous prediction of peach firmness and weight using vibration spectra combined with one-dimensional convolutional neural network

Abstract

Firmness and weight are critical quality attributes of peaches. Non-destructive detection of peach firmness and weight is helpful to meet the market demand for high-quality peaches. In this study, the possibility of simultaneous prediction of peach firmness and weight based on fruit vibration spectra combined with a modified one-dimensional convolutional neural network (CNNm) was investigated. The vibration spectra of 216 peaches were measured by a laser Doppler vibrometer (LDV). The CNNm adopted an inception module with three parallel filters of different sizes so that it can abstract depth features at multiple scales from the full vibration spectra and has the potential to learn more useful information to achieve high prediction performance. The performance of CNNm for predicting peach firmness and weight was compared with that of a typical CNN (CNNt) and two classical chemometric methods based on both full vibration spectra and selected effective frequencies including partial least square (PLS), support vector regression (SVR), successive projections-PLS (SPA-PLS) and SPA-SVR. The results demonstrated that the CNN-based models (CNNm and CNNt) outperformed the classical chemometric models (PLS, SVR, SPA-PLS and SPA-SVR). And CNNm achieved the best performance with RP2 = 0.844, RMSEP = 0.429 N/mm and RPDP = 2.554 for peach firmness prediction and RP2 = 0.794, RMSEP = 29.954 g and RPDP = 2.223 for peach weight prediction. The preliminary results indicated that both peach firmness and weight could be accessed by the vibration spectra of fruits combined with CNNm. The proposed method provided a potential means for simultaneous online prediction of peach firmness and weight.