Crop/weed discrimination in simulated images

Abstract

In the context of site-specific weed management by vision systems, an efficient image processing for a crop/weed discrimination is required in order to quantify the Weed Infestation Rate (WIR) in an image. This paper presents a modeling of crop field in presence of different Weed Infestation Rates and a set of simulated agronomic images is used to test and validate the effectiveness of a crop/weed discrimination algorithm. For instance, an algorithm has been implemented to firstly detect the crop rows in the field by the use of a Hough Transform and secondly to detect plant areas by a region based-segmentation on binary images. This image processing has been tested on virtual cereal fields of a large field of view with perspective effects. The vegetation in the virtual field is modeled by a sowing pattern for crop plants and the weed spatial distribution is modeled by either a Poisson process or a Neyman-Scott cluster process. For each simulated image, a comparison between the initial and the detected weed infestation rate allows us to assess the accuracy of the algorithm. This comparison demonstrates an accuracy of better than 80% is possible, despite that intrarow weeds can not be detected from this spatial method.