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Recognition of clustered fruits and vegetables is a most challenging subject in researches on the vision system of harvesting robot. A recognition algorithm for clustered tomatoes based on mathematical morphology was tested. This algorithm mainly included four steps. First, tomato image segmentation was realized based on a normalized color difference. Second, clustered region could be recognized according to the length of the longest edge of the minimum enclosing rectangle of the tomato region. Third, clustered regions in binary image were processed by an iterative erosion course to separate every tomato in this clustered region. Finally, every seed region in the clustered region acquired by the iterative erosion was restored using a circulatory dilation operation. As a result, every tomato in the clustered region was recognized. 99 clustered regions which were classified into two types based on the clustered degree, adhering tomatoes and overlapping tomatoes, were tested using this algorithm. Test results show that the average correct recognition rate for adhering tomatoes at the shooting distance of 500 mm was 87.5%, but that for two kinds of clustered tomatoes at the shooting distance from 300 to 700 mm was only 58.4%.
Xiang et al. (Sun,) studied this question.