Development of a spatial method for weed detection and localization

This paper presents an algorithm specifically developed for filtering low frequency signals. The application is related to weed detection into aerial images where crop lines are detected as repetitive structures. Theoretical bases of this work are presented first. Then, two methods are compared to select low frequency signals and their limitations are described. A decomposition based on wavelet packet is used to combine advantages of both methods. This algorithm allows a high selectivity of low frequency signals with an interesting computation time. At last, a complete algorithm for weed/crop classification is explained and a few results are shown.