Determination of chestnuts grading based on machine vision

In order to realize grading of eligible and defected chestnut by using machine vision, a classification method of chestnut was developed based on BP-ANN and image feature of chestnut. In this experiment, Luotian chestnuts were used as experimental targets. Principal component analysis (PCA) was implemented on these feature variables from eight eigen values including color parameters and veins characteristics parameters etc., and principal components (PCs) vectors were extracted as the inputs of pattern recognition. Grading models were built by BP neural network. The test result showed that when the number of principal component factor was three and the number of nodes of hidden layer was twelve, the discriminating rate was as high as 100% in training set, and 91.67% in prediction set. The overall results shows that it is feasible to discriminate chestnut quality with machine vision.