Crop row detection by global energy minimization

This paper presents a new efficient method for crop row detection which uses a dynamic programming technique to combine image evidence and prior knowledge about the geometric structure which is searched for in the image. The proposed approach consists of three steps, i.e., (i) vegetation detection, (ii) detection of regular patterns, and (iii) determining an optimal crop model. The method is capable of accurately detecting both straight and curved crop rows. The proposed approach is experimentally evaluated on a set of 281 real-world camera images of crops of maize, celery, potato, onion, sunflower and soybean. The proposed approach is compared to two Hough transform based methods and one method based on linear regression. The methods are compared using a novel approach for evaluation of crop row detection methods. The experiments performed demonstrate that the proposed method outperforms the other three considered methods in straight crop row detection and that it is capable of detecting curved crop rows accurately.

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