Automatic identification of weed seeds by color image processing

Abstract

The analysis and classification of seeds are essential activities contributing to the final added value in the crop production. Besides varietal identification and cereal grain grading, it is also of interest in the agricultural industry the early identification of weeds from the analysis of strange seeds, with the purpose of chemically controlling their growth. The implementation of new methods for reliable and fast identification and classification of seeds is thus of major technical and economical importance. Like the manual identification work, the automatic classification of seeds should be based on knowledge of seed size, shape, color and texture. In this work we present a study of the discriminating power of morphological, color and textural characteristics of weed seeds, which can be measured from video images. This study was conducted on a large basis, considering images of weed seeds found in Argentina’s commercial seed production industry and listed by the Secretary of Agriculture as prohibited and primary- and secondary-tolerated weeds. We first describe the experimental setting and hardware used to capture the seed images. Then, we define the morphological, color and textural parameters measured from these images, and discuss the selection of the most relevant ones for identification purposes. Finally, we present results for the identification of test images obtained using a Naive Bayes classifier and a committee of Artificial Neural Networks.