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Application of the condensed nearest-neighbor rule to the recognition of seed species by artificial vision

The identification of impurities in seed lots is required before their commercialization. Artificial vision can therefore be used for this goal. The aim of the present study was to identify rumex and wild oat in lots of lucerne seeds by color image analysis. A set of 58 morphometrical and textural parameters were assessed from the images of seeds. Among them, 3 relevant parameters were selected by stepwise discriminant analysis. The main purpose of this paper was to investigate the application of condensed nearest neighbor (CNN) rule for the recognition of seeds. The results were compared with those obtained by the well- known k-nearest neighbor rule, which requires a lot of computer time. CNN was found to substantially reduce the size of the training set. It was surprising that with only 9 observations selected by the CNN from the sample collection, it was possible to correctly classify all the 1194 observations of the training set. It also appeared that the seize of the consistent subset increased in relation to the number of required k-neighbors. CNN presented good classification performances despite the heterogeneity of wild oat seeds. Moreover, the algorithm converged fairly quickly and the number of iterations did not exceed 4.