Separation of clods and stones from harvested potatoes using laser backscattering imaging technique

Traditionally, the separation of harvested potatoes from foreign materials, such as clods and stones, is performed manually with low efficiency and economic drawbacks. In this study, we proposed an automatic potato separation strategy that incorporates feature selection using laser backscattering imaging technology. Four sample mixtures of potatoes, clods, and stones from China were used for feature selection. Specifically, five optimal features were selected using combination of five filter methods and Borda count in the four groups. The classification model accounted for potato origin and variety was tested at different impurity rates. This approach presented high and promising separation results with an overall accuracy rate greater than 96%. The results demonstrated that the selected features were exceptionally powerful and effective for separating potatoes from non-potatoes of different origins and varieties under different impurity rates.