F1 2016 pc key no survey
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It will avoid the mixing of different cultivars with different properties intended for use and processing. The developed models may be used in practice for cultivar identification of sweet cherries and detection of falsification or confirmation of authenticity. 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 research involved the use of a procedure that allows the classification of sweet cherries in a non-destructive, objective, and inexpensive manner. 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. The significance of this study is great for practical applications. 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. 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. 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. 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 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 whole fruit images of “Büttner's Red,” “Kordia,” and “Lapins” were acquired using a digital camera. 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. This paper presents the important elements of a computer vision system and emphasizes the important aspects of the image processing technique coupled with a review of the most recent developments throughout the food industry.
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Computer vision has been successfully adopted for the quality analysis of fruits and vegetables, eggs. This inspection approach based on image analysis and processing has found a variety of different applications in the food industry. Computer vision provides one alternative for a computerized, non-destructive and cost-effective technique to accomplish these requirements. With increased expectations for food products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in food products continues to grow. The sorting process depends on capturing the image of the fruit or product and analyzing this image to discard defected products. In this research a vision-based sorting system is developed to increase the quality of food products.
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Sorting tons of fruits and vegetables manually is a slow, costly, and an inaccurate process. The large population and the increased requirements of food products make it difficult to arrive the desired quality. The quality of food products is very important for the human health.