Effect of different combinations of size and shape parameters in the percentage error of classification of structural elements in vegetal tissue of the pumpkin Cucurbita pepo L. using probabilistic neural networks

Keywords: Combination, size and shape parameters, probabilistic neural network

Abstract

The optimal combination of size and shape parameters for classifying structural elements with the lowest percentage error is determined. For this purpose, logical sequences and a series of micrographs of tissues of the pumpkin Cucurbita pepo L. were used to identify and manually classify structural elements into three different classes: cells, intercellular spaces and unrecognizable elements. From each element, eight parameters of size and shape (area, equivalent diameter, major axis length, minor axis length, perimeter, roundness, elongation and compaction) were determined, and a logical sequence was developed to determine the combination of parameters that generated the lowest error in the classification of the microstructural elements by comparison with manual classification. It was found by this process that the minimum error rate was 12.7%, using the parameters of major axis, minor axis, perimeter and roundness.

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Author Biographies

Jimy Frank Oblitas-Cruz, Universidad Privada del Norte

Agroindustrial Engineer, postgraduate in food technology and operations manegement, Director of the industrial Engineering career , Department of Engineering. 

 

 

 

Wilson Manuel Castro-Silupu, Universidad Nacional Toribio Rodríguez de Mendoza de Amazonas

Agroindustrial Engineer , Postgraduated in food Science and Engineering, Department of Engineering and Agricultural Sciences

 

Luis Mayor López, Universidad Politécnica de Valencia

PhD in food technology, Institute of Agrochemistry and Food Techonology 

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Published
2016-03-18
How to Cite
Oblitas-Cruz J. F., Castro-Silupu W. M., & Mayor López L. (2016). Effect of different combinations of size and shape parameters in the percentage error of classification of structural elements in vegetal tissue of the pumpkin Cucurbita pepo L. using probabilistic neural networks. Revista Facultad De Ingeniería Universidad De Antioquia, (78), 30-37. https://doi.org/10.17533/udea.redin.n78a04