Center-based clustering for line detection and application to crop rows detection

• A new cluster-based method for several lines detection is proposed.
• The method is based on searching for an approximate globally optimal partition.
• A new efficient algorithm for crop rows detection is proposed.

This paper proposes a new efficient method for line detection based on known incremental methods of searching for an approximate globally optimal partition of a set of data points AA and on the DIRECT algorithm for global optimization. The proposed method was modified for solving the problem of detecting crop rows in agricultural production. This modification can recognize crop rows with a high accuracy, and the corresponding CPU-time is very acceptable. The method has been tested and compared on synthetic data sets with the method based on Hough transformation. The efficiency of this method might be significantly improved in direct application. The proposed method has been used in this paper for the case of two or three crop rows. The generalization to several crop rows is also given in the paper, but was not implemented. Also, the method could be expanded in case when the number of crop rows is not known in advance.