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Fresh grapes are characterized by a short shelf life and are often subjected to quality losses during post-harvest storage. The quality assessment of grapes using image analysis may be a useful approach using non-destructive methods. This study aimed to compare the effect of different storage methods on the grape image texture parameters of the fruit outer structure. Grape bunches were stored for 4 weeks using 3 storage methods (– 18 °C, + 4 °C, and room temperature) and then were subjected subsequently to image acquisition using a flatbed scanner and image processing. The models for the classification of fresh and stored grapes were built based on selected image textures using traditional machine learning algorithms. The fresh grapes and stored fruit samples (for 4 weeks) in the freezer, in the refrigerator and in the room were classified with an overall accuracy reaching 96% for a model based on selected texture parameters from images in color channels R, G, B, L, a, and b built using Random Forest algorithm. Among the individual color channels, the carried-out classification for the R color channel produced the highest overall accuracies of up to 92.5% for Random Forest. As a result, this study proposed an innovative approach combining image analysis and traditional machine learning to assess changes in the outer structure of grape berries caused by different storage conditions. [ABSTRACT FROM AUTHOR]

This study aimed to determine the discriminatory power of textural features to differentiate the sorghum grains subjected to normal, mild deficit, and severe deficit irrigation. The studies were carried out with the use of image processing, discrimination analysis, analysis of variance and cluster analysis using the selected texture parameters calculate for images from individual color channels L, a, b, R, G, B, U, V, S, X, Y and Z. The results indicated that different levels of irrigation can discriminate the sorghum grain with an accuracy of up to about 100%. Most of the genotypes for each level of irrigation were different in the terms of values of textural features and formed separate homogeneous groups. Drought is one of the limiting factors contributing to a decrease in sorghum grain productivity and nutritional quality, especially when it is cultivated in a marginal area. Therefore, low-quality grains produced under water stress should be recognized before they enter into the food and feed chain. The application of image analysis based on textures of sorghum grain images proved to be useful for the discrimination of sorghum grains subjected to drought stress. The applied procedure provided the fast, objective results that may be applied in practice for screening distinguishing the sorghum grains with different irrigation levels. [ABSTRACT FROM AUTHOR]