The Influence of Colour Features on Seed Identification Using Machine Vision

Abstract

Little studies have been done on morphology of medicinal plants seeds. This paper presents an automatic system for medicinal plant seed identification and evaluates the influence of colour features on seed identification. Six colour features (means of red, green and blue colours of the seed surface, as well as means of hue, intensity and saturation) were extracted by algorithm and applied as network input. Different combinations of colour features (one, two three, four, five and six colour features) were used to find out the most accurate combination for seed identification. Results showed that the six colour feature was the most accurate combination for seed identification (99.184% and 87.719% for training and test of neural network respectively). One colour feature had the lowest average accuracy values for seed identification (3.120% and 2.771%). In general, increasing the number of colour features increased the total average of accuracy values.