Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm.
Fiche publication
Date publication
janvier 2024
Journal
Journal of imaging
Auteurs
Membres identifiés du Cancéropôle Est :
Pr GOUTON Pierre
Tous les auteurs :
Ayikpa KJ, Gouton P, Mamadou D, Ballo AB
Lien Pubmed
Résumé
The quality of cocoa beans is crucial in influencing the taste, aroma, and texture of chocolate and consumer satisfaction. High-quality cocoa beans are valued on the international market, benefiting Ivorian producers. Our study uses advanced techniques to evaluate and classify cocoa beans by analyzing spectral measurements, integrating machine learning algorithms, and optimizing parameters through genetic algorithms. The results highlight the critical importance of parameter optimization for optimal performance. Logistic regression, support vector machines (SVM), and random forest algorithms demonstrate a consistent performance. XGBoost shows improvements in the second generation, followed by a slight decrease in the fifth. On the other hand, the performance of AdaBoost is not satisfactory in generations two and five. The results are presented on three levels: first, using all parameters reveals that logistic regression obtains the best performance with a precision of 83.78%. Then, the results of the parameters selected in the second generation still show the logistic regression with the best precision of 84.71%. Finally, the results of the parameters chosen in the second generation place random forest in the lead with a score of 74.12%.
Mots clés
genetic algorithm, machine learning, spectral analysis, spectral measurements
Référence
J Imaging. 2024 01 8;10(1):