Exploiting affine invariant regions and leaf edge shapes for weed detection

Abstract

In this article, local features extracted from field images are evaluated for weed detection. Several scale and affine invariant detectors from computer vision literature along with high performance descriptors were applied. Field dataset contained a total of 474 plant images of sugar beet and creeping thistle, divided into six groups based on illumination, age, and camera to plant distance. To establish a performance baseline, leaf image retrieval potential of the selected features was first assessed on a publicly available leaf database containing flatbed scanned images of 15 tree species. Then a comparison with the field data retrieval highlighted the trade-off due to the field challenges. Adopting a comprehensive approach, edge shape detectors and homogeneous surface detecting affine invariant regions were fused. In order to integrate vegetation indices as local features, a new local vegetation color descriptor was introduced which used various combinations of color indices and offered a very high precision. Retrieval in the field data was evaluated group-wise. Although, the impact of the sunlight was found to be very low on shape features, but relatively higher precisions were obtained for younger plants under a shade (overall more than 80%). The weed detection accuracy was assessed using the Bag-of-Visual-Word scheme with KNN and SVM classifiers. The assessment showed that with an SVM classifier, a fusion of surface color and edge shapes boosted the overall classification accuracy to as high as 99.07% with a very low false negative rate (2%).