Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange

• Published in: Applied sciences Vol. 12; no. 16; p. 8233
• Main Authors: Al-Sammarraie, Mustafa Ahmed Jalal, Gierz, Łukasz, Przybył, Krzysztof, Koszela, Krzysztof, Szychta, Marek, Brzykcy, Jakub, Baranowska, Hanna Maria
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2022
• Subjects: Accuracy; Algorithms; Artificial intelligence; artificial intelligence technology; Classification; Color vision; Comparative studies; Data analysis; Data processing; Exports; Food products; Food quality; Fruits; Oranges; RGB; Smartphones; Sugar; sugar content; Sweetness; Vegetables; Egypt; Iraq
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app12168233

The manual classification of oranges according to their ripeness or flavor takes a long time; furthermore, the classification of ripeness or sweetness by the intensity of the fruit’s color is not uniform between fruit varieties. Sweetness and color are important factors in evaluating the fruits, the fruit’s color may affect the perception of its sweetness. This article aims to study the possibility of predicting the sweetness of orange fruits based on artificial intelligence technology by studying the relationship between the RGB values of orange fruits and the sweetness of those fruits by using the Orange data mining tool. The experiment has applied machine learning algorithms to an orange fruit image dataset and performed a comparative study of the algorithms in order to determine which algorithm has the highest prediction accuracy. The results showed that the value of the red color has a greater effect than the green and blue colors in predicting the sweetness of orange fruits, as there is a direct relationship between the value of the red color and the level of sweetness. In addition, the logistic regression model algorithm gave the highest degree of accuracy in predicting sweetness.