Abstract
The study was aimed at the evaluation of the usefulness of textures of the outer surface from the images of apple skin and flesh for discrimination of different cultivars. The texture parameters were calculated from color channels: R, G, B, L, a, b, U, V, H, S, I, X, Y, Z. In the case of cultivar discrimination performed for the apple skin, the highest accuracies were obtained for textures from channels R, a and X. In the case of channels R and a, the apples were classified with the total accuracy of up to 93%. For channel X, the highest total accuracy was 90%. For discrimination based on the textures selected from images of a longitudinal section of apples, the total accuracy reached 100% for channels G, b and U. In the case of the cross-section images, the total accuracies were also satisfactory and reached 93% for channel G, 97% for channels b and U. The obtained results proved that the use of image processing based on textures can allow the discrimination of apple cultivars with a high probability of up to 100% in the case of textures selected from images of a longitudinal section. The results can be applied in practice for cultivar discrimination and detection of the falsification of apple cultivars. The obtained results revealed that texture features can allow for cultivar identification of apples with a very high probability in an inexpensive, objective, and fast way.Graphic abstract
Highlights
Apple (Malus domestica Borkh.) is a widely cultivated crop in temperate regions of the world, significant in terms of the economy [1]
In the case of cultivar discrimination performed using textures from the images of the apple skin, the highest accuracies were obtained for channels R, a and X
The research confirmed the usefulness of image processing based on the selected texture features of the skin, longitudinal section and cross-section of apples for discrimination of different apple cultivars
Summary
Apple (Malus domestica Borkh.) is a widely cultivated crop in temperate regions of the world, significant in terms of the economy [1]. Color, and size, which can be determined using image analysis, as well as the taste and chemical composition can affect the quality of apples. Differentiation of fruit properties including texture, color, and shape may depend on the degree of ripeness [6]. It is an important issue for carrying out cultivar discrimination using these characteristics. Application of machine vision based on color image processing for fruit sorting can provide high accuracy and can allow the detection of even slight changes and it is easier, more objective, less time-consuming, and arduous than human vision [13]. Texture parameters can even specify the changes that are difficult to relate to changes, which are perceived in the visual manner [14]
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