Genetic Programming for Prediction of Fat Content in Meat Images Lucia Ballerini 1 and Leonardo Bocchi 2 1 Dept. of Food Science, Swedish University of Agricultural Sciences P.O. Box 7051, 75007, Uppsala, Sweden lucia@cb.uu.se 2 Dept. of Electronics and Telecommunications, University of Florence Via S.Marta 3, 50139 Firenze, Italy leo@asp.det.unifi.it Abstract. In this paper we investigate the feasibility of genetic pro- gramming for combining features extracted from meat images to predict fat content. Images of beef meat were acquired and analyzed. Feature sets including fat percentage, fat fleck size distribution and homogeneity measurements were extracted from these images. Using genetic program- ming we built individual programs which give a prediction of fat content based on these features. Results shows that the proposed method is ef- fective in achieving a very good approximation of the fat percentage measured by chemical analysis. 1 Introduction Intramuscular fat content in meat influences some important meat quality pa- rameters. For example, the quantitative intramuscular fat content has been shown to influence the palatability characteristics of meat. In addition, the vi- sual appearance of the fat does influence the consumers overall acceptability of meat and therefore the choice when selecting meat before buying. There are several methods to analyze quantitative intramuscular fat content and its visual appearance in meat. However, few of them are satisfactory enough in terms of fat quantification in a cross section of a consumer size meat slice, without using large amounts of organic solvents, being too time consuming, or invasive. Computer video systems are being used increasingly in the food industry for quality assurance purposes. These systems offer the potential to automate manual grading practices thus standardizing techniques and eliminating tedious human inspection tasks. Review papers that describe the progress of computer vision in the agricultural and food industry attest the growing interest in these methods. [1] Image processing and image analysis are recognized as being the core of computer vision. [2] As concern the meat industry, visual inspection is used extensively for the quality assessment of meat products applied to processes from the initial grading through to consumer purchases. [3] Applications to beef, lamb and pig meat can be found in literature [4]. Gerrard et al. [5] have developed an image processing