Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm
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 analyzi...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-01-01
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| Series: | Journal of Imaging |
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| Online Access: | https://www.mdpi.com/2313-433X/10/1/19 |
| _version_ | 1797343366361382912 |
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| author | Kacoutchy Jean Ayikpa Pierre Gouton Diarra Mamadou Abou Bakary Ballo |
| author_facet | Kacoutchy Jean Ayikpa Pierre Gouton Diarra Mamadou Abou Bakary Ballo |
| author_sort | Kacoutchy Jean Ayikpa |
| collection | DOAJ |
| description | 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%. |
| first_indexed | 2024-03-08T10:46:35Z |
| format | Article |
| id | doaj.art-c0441581767b4163b5b764418339ec79 |
| institution | Directory Open Access Journal |
| issn | 2313-433X |
| language | English |
| last_indexed | 2024-03-08T10:46:35Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Imaging |
| spelling | doaj.art-c0441581767b4163b5b764418339ec792024-01-26T17:11:03ZengMDPI AGJournal of Imaging2313-433X2024-01-011011910.3390/jimaging10010019Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic AlgorithmKacoutchy Jean Ayikpa0Pierre Gouton1Diarra Mamadou2Abou Bakary Ballo3Laboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, FranceLaboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, FranceLaboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, FranceLaboratoire Imagerie et Vision Artificielle (ImViA), Université de Bourgogne, 21000 Dijon, FranceThe 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%.https://www.mdpi.com/2313-433X/10/1/19spectral analysisgenetic algorithmmachine learningspectral measurements |
| spellingShingle | Kacoutchy Jean Ayikpa Pierre Gouton Diarra Mamadou Abou Bakary Ballo Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm Journal of Imaging spectral analysis genetic algorithm machine learning spectral measurements |
| title | Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm |
| title_full | Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm |
| title_fullStr | Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm |
| title_full_unstemmed | Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm |
| title_short | Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm |
| title_sort | classification of cocoa beans by analyzing spectral measurements using machine learning and genetic algorithm |
| topic | spectral analysis genetic algorithm machine learning spectral measurements |
| url | https://www.mdpi.com/2313-433X/10/1/19 |
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