Gray-level Co-Occurrence Matrix application to Images Processing of crushed Olives fruits.
Published: 31 December 2019
INTELIGENCIA ARTIFICIAL , Volume 22, pp 135-142; doi:10.4114/intartif.vol22iss64pp135-142
Abstract: This paper shows the results obtained from images processing digitized, taken with a 'smartphone', of 56 samples of crushed olives, using the methodology of the gray-level co-occurrence matrix (GLCM). The values of the appropriate direction (θ) and distance (D) that two pixel with gray tone are neighbourhood, are defined to extract the information of the parameters: Contrast, Correlation, Energy and Homogeneity. The values of these parameters are correlated with several characteristic components of the olives mass: oil content (RGH) and water content (HUM), whose values are in the usual ranges during their processing to obtain virgin olive oil in mills and they contribute to generate different mechanical textures in the mass according to their relationship HUM / RGH. The results indicate the existence of significant correlations of the parameters Contrast, Energy and Homogeneity with the RGH and the HUM, which have allowed to obtain, by means of a multiple linear regression (MLR), mathematical equations that allow to predict both components with a high degree of correlation coefficient, r = 0.861 and r = 0.872 for RGH and HUM respectively. These results suggest the feasibility of textural analysis using GLCM to extract features of interest from digital images of the olives mass, quickly and non-destructively, as an aid in the decision making to optimize the production process of virgin olive oil.
Keywords: occurrence / Decision making / GLCM / Olives / Matrix / digital / optimize / rGH / Hum / crushed
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