Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products

Abstract

Food processing industry is moving forward to a full automation of all processes, especially in technological line segments which represent critical control points of food safety. One of these points is color sorting by using machine vision, where inappropriate products are removed. Most important product appearance attributes are color and texture. During food processing, the product is captured by optical devices, mostly color cameras and lasers. The aim of this paper is to investigate new eligibility criteria for digital image segmentation by using only image from the camera. The goal is to describe the texture of the product, based on chosen mathematical measures, and to allow for recognition and then classification according to the predefined range of values in an appropriate class. Images of frozen raspberry were used. Image analysis of color parameters in RGB color space and statistical tests to examine normality of data were carried out. Thereafter, one-way Anova and correlation analysis was performed. Statistically significant difference was found for the values of two indicators: entropy and new criteria were derived from standard deviation, as well as mean values of pixels for every channel, and marked as L. After determining the range of these criteria, a new algorithm was developed for image segmentation written in Matlab. One of the results of applying this algorithm is that more than 80% of good products were recognized.

Imagistic Technique and Fractal Analysis - Investigations Mechanisms of the Morphological and Temporal Variability of the Wheat Cultures

“…The usual identification of plants and of vegetal associations is a problem that has exceeded the boundaries of botany, being also in the sphere of other more comprehensive disciplines: ecology, plant physiology, genetics, geography, etc. Also some related areas such as bio-economy, precision farming, bio-resources and others, are interested in the possibility of effective real-time assessment of the state of natural or cultivated vegetation, the level of crop safety, agricultural or biomass production as well as quality or efficiency aspects [1][2][3][4][5][6][7][8][9][10].…”