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Image analysis and data mining techniques for classification of morphological and color features for seeds of the wild castor oil plant (Ricinus communis L.)

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Abstract

In this study, a castor seed (Ricinus communis L.) classification process was developed using a precise image analysis technique, and several data mining algorithms. Castor seed oil has an excellent demand in the pharmaceutical sector, and it has recently aroused the interest of the biodiesel production companies. However, there are few studies describing the physical characteristics of Ricinus communis; thus, any advance in this field contributes to the design of technology that may increase the production of this oil, up to industrial levels. In fact, this work aims to contribute not only to understand the physical features of castor seed varieties, but also to unveil key information to develop better castor seed oil extraction machines. Additionally, a novel methodology to study accessions of castor seed gathered from several geographical locations is proposed. Particularly, an automatically accurate image analysis technique was implemented in order to extract color and morphological features from seeds. The data set of seeds was built considering fifty samples per accession. After that, several classification experiments were done using well known data mining algorithms in order to cluster all samples. Experimental results showed that it is possible to cluster studied seeds into ten similar classes with high accuracy (larger than 95 %). Moreover, image analysis and data mining techniques were efficient tools for the classification of seeds, and the color and morphological data gathered are really useful for the design of oil extraction equipment. In fact, the effectiveness in the correct classification instances was 100 %, with a computation time of 0.01 seconds.

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Acknowledgments

Authors thank to the Mexican Council of Science and Technology (CONACYT, Mexico) for many years of support, and the Laboratory of Applied Technological Systems of the Telematic Engineering Department, Polytechnic University of Queretaro. Paticularly, J. D. Mosquera-Artamonov and J.F. Vasco-Leal thank CONACYT for their respective PhD. granted scholarships.

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Authors and Affiliations

  1. Universidad Politecnica de Queretaro, El Rosario, Mexico

    Cesar Isaza, Karina Anaya & Jonny Zavala de Paz

  2. Universidad Autonoma de Queretaro, Santiago de Querétaro, Mexico

    Jose F. Vasco-Leal

  3. Colegio de Postgraduados SLP, Salinas de Hidalgo, Mexico

    Ismael Hernandez-Rios

  4. Facultad de Ingenieria Mecanica y Electrica, Universidad Autonoma de Nuevo Leon, San Nicolás de los Garza, Mexico

    Jose D. Mosquera-Artamonov

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  1. Cesar Isaza
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  2. Karina Anaya
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  4. Jose F. Vasco-Leal
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  5. Ismael Hernandez-Rios
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  6. Jose D. Mosquera-Artamonov
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Correspondence to Cesar Isaza.

Additional information

This research was partially supported with a grant from Secretaria de Educacion Publica under grant Nuevos PTC F-PROMEP-38/Rev-03–SEP-23-005, and the Mexican Council of Science and Technology (CONACYT, Mexico)

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Isaza, C., Anaya, K., de Paz, J.Z. et al. Image analysis and data mining techniques for classification of morphological and color features for seeds of the wild castor oil plant (Ricinus communis L.) . Multimed Tools Appl 77, 2593–2610 (2018). https://doi.org/10.1007/s11042-017-4438-y

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