Predicting Fruit’s Sweetness Using Artificial Intelligence—Case Study: Orange
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Sensory Analysis and Statistical Methods · Food and Agricultural Sciences · Biochemical Analysis and Sensing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Agricultural Machinery and Equipment, College of Agricultural Engineering Sciences, University of Baghdad, Baghdad 10071, Iraq
Institute of Machine Design, Faculty of Mechanical Engineering, Poznań University of Technology, Piotrowo 3, 60-965 Poznan, Poland
Department of Dairy and Process Engineering, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, Wojska Polskiego 31, 60-624 Poznan, Poland
Department of Biosystems Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-625 Poznan, Poland
Lukasiewicz Research Network—Poznań Institute of Technology, Starołecka 31, 60-963 Poznan, Poland
Department of Physics and Biophysics, Faculty of Food Science and Nutrition, Poznań University of Life Sciences, 38/42 Wojska Polskiego Street, 60-637 Poznan, Poland
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