Implementation of an Automated Solanum Tuberosum Grading System using Image Processing
DOI:
https://doi.org/10.46842/ipn.cien.v29n1a01Keywords:
automatic classifier, image processing, machine vision, LabVIEWTM, automationAbstract
Automated vision systems are used as an effective solution to improve accuracy in objects inspection. However, the transition from manual sorting methods to automated systems has been slow due to factors such as the size and shape of the objects to be inspected. In the food industry, tubers represent a challenge for vision systems due to their high dimensional and morphological variability.
This paper presents the design and implementation of an automated system for sorting Solanum tuberosum (potato) based on machine vision and image processing technologies. The system employs a low-cost camera together with an algorithm developed in LabVIEWTM, which allows determining the size of tubers in a production line. Sorting is carried out by means of a servo-driven sorting mechanism, achieving greater accuracy and efficiency compared to traditional manual methods.
A controlled lighting system was implemented to optimize the quality of the captured images, which allowed for a significant reduction in sorting errors. The results showed a 260% increase in production capacity and a 75% reduction in the error rate, validating the effectiveness of the proposed solution to improve both productivity and process quality in the food industry. In addition, the system offers a high level of flexibility and safety in operation.
By improving potato grading efficiency and reducing the need for manual intervention, a return on investment is anticipated, as well as a positive impact on process responsiveness, which could lead to an increase in product demand.
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