Improvement of the classification of green asparagus using a Computer Vision System

Orlando Salazar-Campos, Johonathan Salazar-Campos, Danny Menacho, Diego Morales, Victor Aredo

Producción científica: Contribución a una revistaArtículorevisión exhaustiva

1 Cita (Scopus)

Resumen

The aim of this work was to improve the classification of green asparagus in an agro-export company by way of a Computer Vision System (CVS). Thus, an image analysis application was developed in the MATLAB® environment to classify green asparagus according to the absence of white spots and the width of the product. The CVS performance was compared with a manual classification using the error in the classification as the quality indicator; the yield from the raw material (%) and line productivity (kg/h) as the production indicators; and the net present value (USD) and internal rate of return (%) as the economic indicators. The CVS classified the green asparagus with 2% error; improved the yield from the raw material from 43% to 45%, and line productivity from 5 to 10 kg/h; and increased the net present value by 102,609.00 USD, yielding an Internal Rate of Return of 156.3%, much higher than the Opportunity Cost of the Capital (8.6%). Hence the classification of green asparagus by a CVS is an efficient and profitable alternative to manual classification.

Idioma originalInglés
Número de artículoe2018140
PublicaciónBrazilian Journal of Food Technology
Volumen22
DOI
EstadoPublicada - 2019
Publicado de forma externa

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