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Cultivar identification of sweet cherries based on texture parameters determined using image analysis
Author(s) -
Ropelewska Ewa,
Popińska Wioletta,
Sabanci Kadir,
Aslan Muhammet Fatih
Publication year - 2021
Publication title -
journal of food process engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.507
H-Index - 45
eISSN - 1745-4530
pISSN - 0145-8876
DOI - 10.1111/jfpe.13724
Subject(s) - rgb color model , lightness , artificial intelligence , mathematics , color space , discriminative model , color histogram , rgb color space , pattern recognition (psychology) , texture (cosmology) , histogram , computer vision , color image , computer science , image (mathematics) , image processing
The aim of this study was to develop discriminative models for distinguishing different cultivars of whole sweet cherries based on the texture parameters determined using image analysis. The whole fruit images of “Büttner's Red,” “Kordia,” and “Lapins” were acquired using a digital camera. The discriminative models were built for textures selected from individual color channels R , G , B , L , a , b , X , Y , and Z and color spaces CIE RGB ( R —red, B —blue, G —green), CIE Lab ( L* —lightness from black to white, a* —green and red, b* —blue and yellow), CIE XYZ ( Y —lightness, X and Z —components of color information). The models were developed for texture sets without division and subsets of textures with division into those calculated based on the histogram, co‐occurrence matrix, run‐length matrix, autoregressive model, gradient map. The total accuracy reached 100% for the models built based on sets of textures without division selected from color channels R and X and color spaces RGB, Lab, and XYZ. In the case of divided sets of textures, the correctness of 100% was obtained for textures selected from histogram and co‐occurrence matrix for color space Lab. For color channels, the highest accuracy was equal to 97% for the model built based on the selected textures calculated based on the histogram for color channel L . The significance of this study is great for practical applications. The correct identification of sweet cherry cultivar using the discriminative models can be important for the selection of fruits with the desired properties for consumption and processing. Practical applications The research involved the use of a procedure that allows the classification of sweet cherries in a non‐destructive, objective, and inexpensive manner. The obtained results were very satisfactory and allowed for the complete discrimination of sweet cherry cultivars based on texture parameters of the whole fruit. The developed models may be used in practice for cultivar identification of sweet cherries and detection of falsification or confirmation of authenticity. It will avoid the mixing of different cultivars with different properties intended for use and processing.