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.
The automation of food processes increases the quality, productivity, and economy of the companies. This study aimed at the development of a Computer Vision System (CVS) to classify lemons in real-time according to their diameter. The CVS consisted of a software, coded in Java programming language, which covers the acquisition, pre-processing, segmentation, description, recognition, and interpretation of the images. The classification criteria were in accord with the CODEX STAN 213 standard for lime-lemon. The CVS reached a 0% error in the classification of the diameter of the lemons and required 0.33 seconds as processing time to detect and classify each lemon. The CVS showed high performance in the automatic classification of lemons according to their diameter. This CVS could avoid the disadvantages of manual classification.
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