Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

De-la-Torre, Miguel; Zatarain, Omar; Avila-George, Himer; Muñoz, Mirna; Oblitas, Jimy; Lozada, Russel; Mejía, Jezreel; Castro, Wilson

doi:10.3390/pr7120928

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Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

^1,*

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and

^3,5

Departamento de Ciencias Computacionales e Ingenierías, Universidad de Guadalajara, Ameca 46600, Jalisco, Mexico

Centro de Investigación en Matemáticas, Zacatecas 98160, Mexico

Facultad de Ingeniería, Universidad Privada del Norte, Cajamarca 06001, Peru

⁴

Escuela Profesional de Ingeniería Electrónica, Facultad de Producción y Servicios, Universidad Nacional de San Agustín, Arequipa 04000, Peru

⁵

Facultad de Ingeniería de Industrias Alimentarias, Universidad Nacional de Frontera, Sullana 20100, Peru

Author to whom correspondence should be addressed.

^†

This paper is an extended version of our paper published in Proceedings of the 8th International Conference on Software Process Improvement, León, Guanajuato, México, 23–25 October 2019.

Processes 2019, 7(12), 928; https://doi.org/10.3390/pr7120928

Received: 1 November 2019 / Revised: 28 November 2019 / Accepted: 3 December 2019 / Published: 5 December 2019

(This article belongs to the Special Issue Green Technologies: Bridging Conventional Practices and Industry 4.0)

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Abstract

This paper explores five multivariate techniques for information fusion on sorting the visual ripeness of Cape gooseberry fruits (principal component analysis, linear discriminant analysis, independent component analysis, eigenvector centrality feature selection, and multi-cluster feature selection.) These techniques are applied to the concatenated channels corresponding to red, green, and blue (RGB), hue, saturation, value (HSV), and lightness, red/green value, and blue/yellow value (L*a*b) color spaces (9 features in total). Machine learning techniques have been reported for sorting the Cape gooseberry fruits’ ripeness. Classifiers such as neural networks, support vector machines, and nearest neighbors discriminate on fruit samples using different color spaces. Despite the color spaces being equivalent up to a transformation, a few classifiers enable better performances due to differences in the pixel distribution of samples. Experimental results show that selection and combination of color channels allow classifiers to reach similar levels of accuracy; however, combination methods still require higher computational complexity. The highest level of accuracy was obtained using the seven-dimensional principal component analysis feature space. View Full-Text

Keywords: Cape gooseberry; color space selection; color space combination; food engineering

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MDPI and ACS Style

De-la-Torre, M.; Zatarain, O.; Avila-George, H.; Muñoz, M.; Oblitas, J.; Lozada, R.; Mejía, J.; Castro, W. Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits. Processes 2019, 7, 928. https://doi.org/10.3390/pr7120928

AMA Style

De-la-Torre M, Zatarain O, Avila-George H, Muñoz M, Oblitas J, Lozada R, Mejía J, Castro W. Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits. Processes. 2019; 7(12):928. https://doi.org/10.3390/pr7120928

Chicago/Turabian Style

De-la-Torre, Miguel, Omar Zatarain, Himer Avila-George, Mirna Muñoz, Jimy Oblitas, Russel Lozada, Jezreel Mejía, and Wilson Castro. 2019. "Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits" Processes 7, no. 12: 928. https://doi.org/10.3390/pr7120928

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Multivariate Analysis and Machine Learning for Ripeness Classification of Cape Gooseberry Fruits

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