Supporting data for "A novel k-FLBPCM method for detecting morphologically similar crops and weeds based on the combination of contour masks and Local Binary Pattern operators"

Weeds are a major cause of low agricultural productivity. Some weeds have morphological features similar to crops making them difficult to discriminate. This paper proposes a novel method using a combination of filtered-features extracted by combined Local Binary Pattern operators and features extracted by plant-leaf contour masks to improve the discrimination rate between broadleaf plants. Opening and closing morphological operators were applied to filter noise in plant images. The images at four stages of growth were collected using a testbed system. Mask-based Local Binary Pattern features were combined with filtered-features and a coefficient k. The classification of crops and weeds was achieved using support-vector-machine with radial basis function kernel. By investigating optimal parameters, this method reached a classification accuracy of 98.63% with four classes in the bccr-segset dataset published online in comparison with an accuracy of 91.85% attained by a previously reported method.