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      Improvement of the classification of green asparagus using a Computer Vision System Translated title: Aprimoramento da classificação de aspargos verdes usando um Sistema de Visão Computacional

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          Abstract

          Abstract 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.

          Translated abstract

          Resumo O objetivo deste trabalho foi o aprimoramento da classificação de aspargos verdes em uma empresa agroexportadora, mediante um Sistema de Visão Computacional (SVC). Para isso, um aplicativo de análise de imagens foi desenvolvido no MATLAB®, para a classificação de aspargos verdes, segundo a ausência de machas brancas e a largura do produto. O desempenho do SVC foi comparado com a classificação manual usando o erro na classificação como indicador de qualidade. O rendimento da matéria-prima (%), a produtividade da linha (kg/h), o valor presente líquido (USD) e a taxa interna de retorno (%) foram utilizados como indicadores econômicos. O SVC classificou o aspargo verde com 2% de erro, incrementou o rendimento da matéria-prima de 43% para 45% e a produtividade da linha de 5 para 10 kg/h, bem como aumentou o valor presente líquido em 102.609,00 USD, rendendo uma taxa interna de retorno de 156,3%, muito superior ao custo de oportunidade do capital (8,6%). Assim, a classificação de aspargos verdes mediante um CVS é uma alternativa eficiente e rentável quando comparada à classificação manual.

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          Most cited references13

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          Evaluation of Food Quality and Safety with Hyperspectral Imaging (HSI)

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            Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry

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              Rapid and nondestructive techniques for internal and external quality evaluation of watermelons: A review

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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                bjft
                Brazilian Journal of Food Technology
                Braz. J. Food Technol.
                Instituto de Tecnologia de Alimentos - ITAL (Campinas, SP, Brazil )
                1981-6723
                April 2019
                : 22
                : 0
                : e2018140
                Affiliations
                [02] Trujillo orgnameUniversidad Nacional de Trujillo orgdiv1Escuela de Ingeniería Agroindustrial orgdiv2Facultad de Ciencias Agropecuarias Peru
                [01] Trujillo orgnameUniversidad Privada del Norte orgdiv1Facultad de Ingeniería, Ingeniería de Sistemas Computacionales Peru
                [03] Pirassununga orgnameUniversidade de São Paulo orgdiv1Programa de Pós-graduação em Engenharia de Alimentos Brazil
                Article
                S1981-67232019000100413
                10.1590/1981-6723.14018
                b8825179-ecd9-4a4f-bd9a-2ea2a45075f7

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 23 June 2018
                : 13 November 2018
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 14, Pages: 0
                Product

                SciELO Brazil

                Categories
                Original Article

                Quality,Artificial vision,Asparagus officinalis,Automatization,Economical evaluation,Productivity,Visão artificial,Automatização,Avaliação econômica,Produtividade,Qualidade

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