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

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.

Design of a computational application for the evaluation of the quality of export lemon

“…A total of 385 lemons (Citrus aurantifolia) from Olmos-Lambayeque (Peru) were used in this study. The number of samples was defined considering a random sampling for infinite populations (Salazar-Campos et al, 2019). Prior to classification by the CVS, the samples were washed with water, dried and stored in polyethylene bags.…”

“…Classification is an important operation in the modern food industry, hence, when done wrong, can lead to considerable economic losses. The classification is susceptible to errors when it is performed manually since human vision makes it a subjective, inconsistent, and slow operation (Zhang et al, 2014;Salazar-Campos et al, 2019). In this context, contact-free technologies are used successfully in food industries to control processes, measure quality and classify products, which generates improvements in performance and economic income in small as well as large companies (Magwaza et al, 2012;Siche et al, 2016;Aredo et al, 2019).…”