Differentiation of peach cultivars by image analysis based on the skin, flesh, stone and seed textures

The peaches belonging to different cultivars can be characterized by differentiation in properties. The aim of this study was to evaluate the usefulness of individual parts of fruit (skin, flesh, stone and seed) for cultivar discrimination of peaches based on textures determined using image analysis. Discriminant analysis was performed using the classifiers of Bayes net, logistic, SMO, multi-class classifier and random forest based on a set of combined textures selected from all color channels R, G, B, L, a, b, X, Y, Z and for textures selected separately for RGB, Lab and XYZ color spaces. In the case of sets of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z), the accuracy of 100% was observed for flesh, stones and seeds for selected classifiers. The sets of textures selected from RGB color space produced the correctness equal to 100% in the case of flesh and seeds of peaches. In the case of Lab and XYZ color spaces, slightly lower accuracies than for RGB color space were obtained and the accuracy reaching 100% was noted only for the discrimination of seeds of peaches. The research proved the usefulness of selected texture parameters of fruit flesh, stones and seeds for successful discrimination of peach cultivars with an accuracy of 100%. The distinguishing between cultivars may be important for breeders, consumers and the peach industry for ensuring adequate processing conditions and equipment parameters. The cultivar identification of fruit by human may be characterized by large errors. The molecular or chemical methods may require special equipment or be time-consuming. The image analysis may ensure objective, rapid and relatively inexpensive procedure and high accuracy for peach cultivar discrimination.

The application of image processing for cultivar discrimination of apples based on texture features of the skin, longitudinal section and cross-section

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

Rapid Nondestructive Postharvest Potato Freshness and Cultivar Discrimination Assessment

Background: Quality and safety of potato is both cultivar and postharvest management dependent. The precise assessment of freshness and cultivar are complex tasks requiring time-consuming, expensive, and destructive techniques. Method: Potatoes from three commercial cultivars were stored for 5 months at 5 °C. Color and chlorophyll fluorescence were recorded, Red-Green-Blue (R-G-B), Red-Green-Near infrared (R-G-NIR) and Red-Blue-Near infrared (R-B-NIR) digital images, as well as hyperspectral images were acquired both on the external periderm of the tuber and in the inner flesh part. Partial least square regression (PLSR) and discriminant analysis, combined with feature selection techniques were implemented, in order to assess the potato freshness and to classify them into the respective genotypes. Results: The PLSR analysis of visible/near infrared (Vis/NIR) spectra reflectance most reliably predicted potato freshness, with a cross-validated regression coefficient equal to 0.981 and 0.947, as determined by external or internal measurements, respectively. Variance inflation factor, variable importance scores, and genetic algorithms identified specific wavelength regions that mostly affected the accuracy of the model in terms of strongest regression and lowest collinearity and root mean cross validation error. Conclusions: Vis/NIR spectra reflectance data from the skin of the potato tubers may be reliably used in the assessment of postharvest storage life, as well as in the cultivar discrimination process.

Effect of boiling on classification performance of potatoes determined by computer vision

The aim of this study was to evaluate the effect of potato boiling on the correctness of cultivar discrimination. The research was performed in an objective, inexpensive and fast manner using the image analysis technique. The textures of the outer surface of slice images of raw and boiled potatoes were calculated. The discriminative models based on a set of textures selected from all color channels (R, G, B, L, a, b, X, Y, Z, U, V, S), textures selected for color spaces and textures selected for individual color channels were developed. In the case of discriminant analysis of raw potatoes of cultivars ‘Colomba’, ‘Irga’ and ‘Riviera’, the accuracies reached 94.33% for the model built based on a set of textures selected from all color channels, 94% for Lab and XYZ color spaces, 92% for color channel b and 92.33% for a set of combined textures selected from channels B, b, and Z. The processed potatoes were characterized by the accuracy of up to 98.67% for the model including the textures selected from all color channels, 98% for RGB color space, 95.33% for color channel b, 96.67% for the model combining the textures selected from channels B, b, and Z. In the case of raw and processed potatoes, the cultivar ‘Irga’ differed in 100% from other potato cultivars. The results revealed an increase in cultivar discrimination accuracy after the processing of potatoes. The textural features of the outer surface of slice images have proved useful for cultivar discrimination of raw and processed potatoes.

The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.