A deep learning approach to improving spectral analysis of fruit quality under interseason variation

Model updating for developed calibrations is critical for robust spectral analysis in fruit quality control. Existing methods have limitations that usually need sufficient samples for model recalibration and are mainly designed for conventional linear models. This study proposes a model fine-tuning approach to update nonlinear deep learning models using limited sample sizes for fruit detection under interseason variation. This approach provides RMSE of 0.407, 1.035, and 0.642, for predicting soluble solid content (%) or dry matter content (%), in the Cuiguan pear, Rocha pear, and Mango dataset. The proposed approach reduces at least 9.2%, 17.5%, and 11.6% of test RMSE in three datasets compared with conventional model updating methods, including the global model, recalibration, and slope/bias correction. The model fine-tuning approach shows improved reliability under different updating sample sizes, ranging from 5% to 20% proportions of the new season's samples. The utilization of cumulative data in multiple previous seasons enables further improved performance. This study potentially facilitates implementing high-performance deep learning approaches in on-site applications of fruit quality control.