A novel random forest-based approach for the non-destructive and explainable estimation of ammonia and chlorophyll in fresh-cut rocket leaves

he perceived visual quality of fruits and vegetables plays a central role in the choices made by retail customers. Machine learning (ML) approaches based on image analysis have been recently proposed to overcome the poor efficiency and subjectivity of human visual evaluation as well as the expensiveness and destructiveness of physical and chemical methods that measure internal indicators. In this paper, we propose a ML method based on Random Forests for estimating the chlorophyll and ammonia contents (considered, in the literature, reliable indicators of product freshness) from images of fresh-cut rocket leaves. Our approach copes with specific issues raised by (i) the non-uniform distributions of ammonia and chlorophyll values and (ii) the need to provide insights into the features that produce a particular model outcome, aiming to enhance its trustworthiness. Our experiments, performed on real images of fresh-cut rocket leaves, proved that the proposed approach significantly outperforms 7 competitor methods, obtaining an improvement of the RSE results of 6.6% for the prediction of the ammonia and of 10.4% for the prediction of the chlorophyll over its best competitor. Moreover, a specific analysis of the explainability of the predictions showed that the learned models are based on reasonable features, empowering their acceptance in real-world applications.