Weed seeds identification by machine vision

Abstract

The implementation of new methods for reliable and fast identification and classification of seeds is of major technical and economical importance in the agricultural industry. As in ocular inspection, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work, we assess the discriminating power of these characteristics for the unique identification of seeds of 57 weed species. Using the performance of a naı̈ve Bayes classifier as selection criterion, we identified a nearly optimal set of 12 (six morphological+four color+two textural) seed characteristics to be used as classification parameters. We found that, as expected, size and shape characteristics have larger discriminating power than color and textural ones. However, all these features are required to reach an identification performance acceptable for practical applications. In spite of its simplicity, the naı̈ve Bayes classifier reveals itself surprisingly good for the identification of seed species. This might be due to the careful selection of the feature set, leading to nearly independent parameters as assumed by this method. We also found that, using the same feature set, a more sophisticated classifier based on an artificial neural network committee performs only slightly better than this simple Bayesian approach.