Classification of crops and weeds extracted by active shape models

Active shape models (ASMs) for the extraction and classification of crops using real field images were investigated. Three sets of images of crop rows with sugar beet plants around the first true leaf stage were used. The data sets contained 276, 322 and 534 samples, equally distributed over crops and weeds. The weed populations varied between the data sets resulting in from 19% to 53% of occluded crops. Three ASMs were constructed using different training images and different description levels. The models managed to correctly extract up to 83% of the crop pixels and remove up to 83% of the occluding weed pixels. Classification features were calculated from the shapes of extracted crops and weeds and presented to a k-NN classifier. The classification results for the ASM-extracted plants were compared to classification results for manually extracted plants. It was judged that 81–87% of all plants extracted by ASM were classified correctly. This corresponded with 85–92% for manually extracted plants.