Utilization of Explainable Machine Learning Algorithms for Determination of Important Features in ‘Suncrest’ Peach Maturity Prediction

Abstract

Peaches (Prunus persica (L.) Batsch) are a popular fruit in Europe and Croatia. Maturity at harvest has a crucial influence on peach fruit quality, storage life, and consequently consumer acceptance. The main goal of this study is to develop a machine learning model that will detect the most important features for predicting peach maturity by first training models and then using the importance ratings of these models to detect nonlinear (and linear) relationships. Thus, the most important peach features at a given stage of its ripening could be revealed. To date, this method has not been used for this purpose, and at the same time, it has the potential to be applied to other similar peach varieties. A total of 33 fruit features are measured on the harvested peaches, and three imbalanced datasets are created using firmness thresholds of 1.84, 3.57, and 4.59 kg·cm−2. These datasets are balanced using the SMOTE and ROSE techniques, and the Random Forest machine learning model is trained on them. Permutation Feature Importance (PFI), Variable Importance (VI), and LIME interpretability methods are used to detect variables that most influence predictions in the given machine learning models. PFI shows that the h° and a* ground color parameters, COL ground color index, SSC/TA, and TA inner quality parameters are among the top ten most contributing variables in all three models. Meanwhile, VI shows that this is the case for the a* ground color parameter, COL and CCL ground color indexes, and the SSC/TA inner quality parameter. The fruit flesh ratio is highly positioned (among the top three according to PFI) in two models, but it is not even among the top ten in the third.

Assessment of Various Machine Learning Models for Peach Maturity Prediction Using Non-Destructive Sensor Data

“…To date, many studies have been conducted to predict fruit maturity using various machine learning models, and machine learning implementation in agriculture has been extensively researched. A random forest (RF) algorithm in combination with explainable machine learning methods was used by Ljubobratović et al [ 11 ] to develop a machine learning model that identifies the most important features for predicting the maturity of peaches to detect nonlinear (and linear) relationships between them. In their study, Scalisi et al [ 12 ] used partial least square (PLS) regression and linear discriminant analysis (LDA) algorithms for peach maturity prediction in different configurations of the spectrometer (fluorescence, near infrared spectroscopy (NIR), and RGB color model).…”