Machine grading and blemish detection in apples

Five classifiers including the K-means, Fuzzy c-means, K-nearest neighbour, Multi-Layer Perceptron Neural Network and Probabilistic Neural Network classifiers are compared for application to colour grade classification and detection of bruising of Granny Smith apples. A number of suitable discriminate features are determined heuristically for the categorisation of four classes including: high grade fruit, high grade fruit with bruising or blemishes, off-grade h i t , and off-grade fruit with bruising or blemishes. Robust features based on intensity statistics are extracted from enhanced monochrome images produced by special transformation from original RGB images. The best of the five classifiers using the optimal feature set, is shown to outperform human graders viewing the same images. KeyWords: K-means, Fuzzy c-means, K-nearest neighbour, Multi-Layer Perceptron, Probabilistic Neural Network.