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Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm.

Kacoutchy Jean Ayikpa¹, Pierre Gouton¹, Diarra Mamadou¹

¹Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, France.

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|January 22, 2024

Summary

Optimizing machine learning parameters enhances cocoa bean quality classification. Genetic algorithms and spectral analysis improve accuracy, benefiting producers and the chocolate industry.

Keywords:

genetic algorithm machine learning spectral analysis spectral measurements

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Area of Science:

Agricultural Science
Data Science
Food Science

Background:

Cocoa bean quality significantly impacts chocolate's sensory attributes and consumer appeal.
High-quality cocoa beans are economically valuable, particularly for producers in regions like Ivory Coast.

Purpose of the Study:

To evaluate and classify cocoa bean quality using advanced spectral analysis and machine learning.
To optimize classification model performance through parameter tuning using genetic algorithms.

Main Methods:

Spectral measurements of cocoa beans were analyzed.
Machine learning algorithms including logistic regression, support vector machines (SVM), random forest, XGBoost, and AdaBoost were employed.
Genetic algorithms were utilized for parameter optimization.

Main Results:

Parameter optimization is critical for achieving optimal classification performance.
Logistic regression demonstrated the highest precision (83.78%) when using all parameters.
After parameter selection in the second generation, logistic regression achieved 84.71% precision, while random forest led with 74.12% in the final selection.

Conclusions:

The study underscores the effectiveness of integrating spectral analysis and machine learning for cocoa bean quality assessment.
Optimized machine learning models, particularly logistic regression and random forest, show promise for objective cocoa quality classification.