Application of Vector Agents to Weed detection from UAV imagery

In the remote sensing field, weed detection algorithms usually use the segmentation process to classify weeds in an image. In this context, the results are subject to user-defined parameters (e.g. scale) and predefined assumptions (e.g. uniform distribution of crop), limiting the usefulness of results. This paper presents a new approach based on Vector Agents (VAs) to extract weeds, more specifically boneseed, from Unmanned Aerial Vehicle (UAV) imagery. VAs are objects that can construct their own geometry and interact spatially with other VAs in the context of Geographic Automata Systems (GAS). The dynamic structure of VAs allows them to directly address real-world objects in an image, such as weeds. In this case, the method can automatically draw the boundary of the real world objects without setting any user-defined parameters, e.g. scale or compactness. We test the proposed model against the ones conventionally used in weed detection, e.g. mean shift and multiresolution. The preliminary results show 8% and 30% improvement in the correctness value of the VA model compared to the mean shift and multiresolution methods, respectively.