Evaluation the state of maturity of pineapple in its variety perolera using computer vision techniques
DOI:
https://doi.org/10.15332/iteckne.v9i1.2744Keywords:
Computer vision system, Segmentation, Feature extraction, Statistical classificationAbstract
Computer vision systems allow identifying the physical characteristics and defects of a product in a non-invasive and reliable form.Due to these advantages, computer vision systems have gained acceptance in the food industry, since this industry requires a high demand for objectivity, consistency and efficiency in controlling the quality of the product, conditions that this systems can meet.This paper proposes a method for automatically evaluating the maturation state of the pineapple (AnanasComosus) perolera variety in postharvest usingcomputer vision system.The proposed evaluation procedure is implemented through an algorithm of color digital image processing based on the stages of preprocessing, segmentation, feature extraction and statistical classification.Algorithm used, images in the HSV color space, automatic segmentation using Otsu’s method, the first moment of the distributions of H and S planes as features, and the algorithm MBSAS (Modified Basic Sequential Algorithmic Scheme) for classification.319 images were used, of which 110 images were used in the process of training and 209 images were used in the evaluation process. The results of the evaluation procedure proposed in this paper were compared with the value judgment of three experts, showing that the proposed algorithm has an efficiency of close to 96.36% assessment.
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