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

• Autor(a) principal: Salazar-Campos,Orlando
• Data de Publicação: 2019
• Outros Autores: Salazar-Campos,Johonathan, Menacho,Danny, Morales,Diego, Aredo,Victor
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Brazilian Journal of Food Technology
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1981-67232019000100413
• topic: Artificial vision; Asparagus officinalis; Automatization; Economical evaluation; Productivity; Quality

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.