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In this study, the objects found in the environment are detected and classified in real time, the results obtained are presented. Hazelnut fruit is used in the experimental studies of the proposed method. The image belongs to hazelnut that is in a work environment is taken with the camera, it is processed by using image processing techniques. The size and area data of hazelnut on the image plane is calculated. By evaluating the obtained data, the hazelnut is divided into three classes as small (K1), medium (K2) and big (K3) in real time application. This process is performed using mean-based classification and K-means clustering methods. Detection and classification of cluster centers is provided by using the information database obtained from the data of hazelnut fruit. Hazelnut fruits found in the experimental environment are determined with 100% accuracy using image processing techniques. The classification of hazelnut fruits using the mean-based and K-means clustering methods has been compared. As a result of the comparison, it is observed that the two methods realized are similar ratio of 90% to 100%. [ABSTRACT FROM AUTHOR]