Quality Assessment Level of Quality of Cocoa Beans Export Quality using Hybrid Adaptive Neuro - Fuzzy Inference System (ANFIS) and Genetic Algorithm

Santika, Gayatri Dwi; Wulandari, Diah Ayu Retnani; Dewi, Fitriyana

doi:10.1109/icecos.2018.8605189

Cited by 2 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results obtained in this research are significant, with a root mean square error (RMSE) of 4.3, which testifies to the suitability of the algorithms used as an expert system for bean quality selection. This approach shows promise for improving the quality and management of cocoa production in this region [4]. Karada g et al undertook a study to identify healthy peppers from those infected with Fusarium wilt (Capsicum annuum).…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, scientific research and technological innovation have converged to give rise to sophisticated systems capable of rapidly, accurately, and non-invasively analyzing colorimetric and spectral properties. This significant advancement has paved the way for creating artificial intelligence models that can assess the quality of cocoa beans in real time based on their unique visual and spectral characteristics [3,4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Ayikpa,

Gouton,

Mamadou

et al. 2024

J. Imaging

View full text Add to dashboard Cite

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%.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%