Deep Tutti Frutti: Exploring CNN architectures for dry matter prediction in fruit from multi-fruit near-infrared spectra

Abstract

Convolutional Neural Networks (CNNs) have proven to be a valuable Deep Learning (DL) algorithm to model near-infrared spectral data in Chemometrics. However, optimizing CNN architectures and their associated hyperparameters for specific tasks is challenging. In this study, we explore the development of 1D-CNN architectures for the task of fruit dry matter (DM) estimation, testing various designs and optimization strategies to achieve a generic DL model that is robust against data fluctuations. The models are built using a multi-fruit data set (n = 2397) that includes NIR spectra of apples, kiwis, mangoes, and pears. The obtained CNN models are compared with PLS (taken as baseline), and to Locally Weighted PLS (LW-PLS) models. In general, the optimized CNN architectures obtained lower RMSEs (best RMSE = 0.605 %) than PLS (RMSE = 0.892 %) and LW-PLS (RMSE = 0.687 %) on a holdout test set. For this specific task, CNNs start outperforming PLS when the number of training samples is around 500. Furthermore, it is also shown how a global CNN model, trained on multi-fruit data, performs against PLS models of individual fruits in the sub-tasks of individual fruit DM prediction and generalization to an external mango data set. Overall, with proper architecture optimization, CNNs show strong performance and generalization for NIR-based dry matter estimation across diverse fruits.