Keywords: lentils, grading, inspection, machine vision, color, image analysis, image classification, flatbed scanner
Publication Number GRL# 809. Shahin, M.A. and Symons, S.J. 2001.
A machine vision system for grading lentils
A machine vision system for color grading of lentils was developed using a flatbed scanner as the image-gathering device. Grain samples belonging to different grades of large green lentils were scanned and analyzed over a two-crop season period. Image color, color distribution, and textural features were found to begood indicators of lentil grade. Linear discriminant analysis, k-nearest neighbors, and neural network based classifiers performed equally well in predicting sample grade. An online classification system was developed with a neural classifier that achieved an overall accuracy (agreement with the grain inspectors) of more than 90%.
Publication Number GRL# 809. Shahin, M.A. and Symons, S.J. 2001.
Publication Number GRL# 809. Shahin, M.A. and Symons, S.J. 2001.
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