Intelligent dielectric method for evaluating some qualitative characteristics of date fruit

Abstract

This study aimed to develop an intelligent capacitive system to measure the moisture content of date fruit and to recognize fruit characteristics, such as variety, size, and ripeness. A cost-effective and fast non-contact measurement solution using the capacitive method was employed to create a platform with a variable oscillator to measure the dielectric properties of date fruit after harvest. Different date varieties, namely Zahedi, Ghasb, Mazafati and Medjool, representing dry, semi-dry and wet date fruit, respectively, were selected to model and calibrate the proposed system. Samples of date fruit of each variety were selected at three different ripening stages (Khalal, Rutab and Tamr), ranging from high to low moisture content. Additionally, five distinct moisture contents were determined using the oven method. The moisture content of the date fruit samples ranged from 8.6 % to 86.9 % owing to the selection of four varieties, three ripening stages and five stepwise thermal treatments. After acquiring electronic information, 80 % of the dataset was allocated for training purposes, while the remaining 20 % was reserved for evaluating the final regression model. The results showed that of all the trained machine learning models, Support Vector Regression (SVR) had the highest potential for predicting moisture content at the specified frequencies. The SVR model was fine-tuned by fitting 1824 combinations of hyperparameters over 6 folds. The tuned model's prediction for 20 % of the assigned test data resulted in a coefficient of determination of 88 % compared to the actual moisture content, with a Root Mean Square Error (RMSE) of 9.4 %. Furthermore, the dielectric-based system classified the ripening stages using a Multilayer Perceptron (MLP) model, achieving F1 scores of 87 %, 60 % and 68 % for the Khalal, Rutab and Tamr stages, respectively. The MLP regression model also predicted the geometric mean of the date fruit with a coefficient of determination of 0.82 and an RMSE of 3.05 mm.