Line cluster detection using a variant of the Hough transform for culture row localisation

The sowing rows were revealed by a background correction, the background being obtained thanks to a median rank filter. The method was found efficient in eliminating the shadows. For the crop rows, a neural network was used to localise the plants. While the petiole and the leaves were easily separated from the soil, the chicory root and the soil having about the same colour and the lighting condition varying widely, it was more difficult to obtain a good contrast between those parts, which leaves place for some improvements. The adapted Hough transform consisted in computing one transform for each line in the cluster with, for reference, the position and direction of the theoretical position of the row. The different transforms were then added. It was found effective for both the sowing rows and the chicory rows. Results remained good even in very noisy conditions, when the rows were incomplete or when artefacts would lead its classical counter part to show several alignments other than the expected ones. The culture rows were localised with a precision of a few centimetres which was compatible with the proposed applications.