Large-scale investigation of weed seed identification by machine vision

We explore the feasibility of implementing fast and reliable computer-based systems for the automatic identification of weed seeds from color and black and white images. Seeds size, shape, color and texture characteristics are obtained by standard image-processing techniques, and their discriminating power as classification features is assessed. These investigations are performed on a database much larger than those used in previous studies, containing 10,310 images of 236 different weed species. We consider the implementation of a simple Bayesian approach (naïve Bayes classifier) and (single and bagged) artificial neural network systems for seed identification. Our results indicate that the naïve Bayes classifier based on an adequately selected set of classification features has an excellent performance, competitive with that of the comparatively more sophisticated neural network approach. In addition, we discuss the possibility of using only morphological and textural characteristics as classification features, which would reduce the operational complexity and hardware cost of a commercial system since they can be obtained from black and white images. We find that, under particular operational conditions, this would result in a relatively small loss in performance when compared to the implementation based on color images.