In this paper we present a novel method for automated detection of Mycosphaerella melonis infected cucumber fruits. The two-step method consists of machine learning approach using: shape based features extracted from cucumber color images and light transmission spectra based features. The automated detection rate was compared to the manual detection rate of the human workers. Our automated method reached the 95% detection accuracy, which is comparable to the manual detection accuracy of 96%.
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