Image segmentation of maize stubble row based on genetic algorithm and threshold filtering noise

Green crop is generally used as the foreground in image segmentation of agricultural visual navigation system because of its obvious chromaticity difference from the basic image background. However, for the segmentation of maize stubble row, there are many backgrounds in the field harvested by combine harvester, such as naked land surface, maize residues, which has little color difference with the maize stubble row. To achieve accurate and quick segmentation of maize stubble row, an image segmentation approach of maize stubble row based on genetic algorithm and threshold filtering noise was proposed in this paper. Firstly, the RGB color space was selected to accomplish the segmentation, which is a basic color space and widely used in machine vision automatic guidance systems. Secondly, to reduce the difficulty of image segmentation, the region of interest (ROI) was selected by calculating the maximum of column gray value accumulation. Thirdly, the gray-scale image of the maize stubble row was obtained through the optimized gray-scale operator. Besides, the genetic algorithm was often used in global optimization, which was used to optimize the gray-scale operator. Specially, the maize stubble row image’s pixel was divided into 3 classes: land surface, residues in rows and stubble tip incision, and the 3 classes were selected as the sample to optimize the gray-scale operator by genetic algorithm. Then, the segmentation of single threshold method was used to segment the gray-scale image. Furthermore, the segmentation of single threshold method is widely used in image segmentation because of its high efficiency and convenience. Lastly, the morphological corrosion treatment (MCT) and threshold noise filtering algorithm (TNFA) were applied to guarantee the integrity of the maize stubble row region and the noise points on both sides of the maize stubble row removal. In order to verify the effect of the method proposed in this paper, 200 test images were captured from Scientific Observing and Experimental Station of Arable Land Conservation (North Hebei), Ministry of Agriculture in Baoding City, China in each October during 2014－2016. The acquisition was often on sunny day, aiming at collecting images under different natural lighting conditions, different positions in maize stubble row field. The results showed that the average relative object area error (ARAE) by our method was only 24.68%, while the AREA of the iteration method and the OTSU method were 90.67% and 86.42%, respectively. The average processing time of a test image based on this paper presented algorithm was 0.16 s, which was much more time-consuming than the OTSU method (0.07 s), while the processing time of our method was less than the iteration method (0.25 s). Therefore, the method presented in this paper achieved better performance than other methods in maize stubble row segmentation, and was effective to segment the maize stubble row in the complicated backgrounds. The presented method can provide precise basis for detection of guidance line in maize straw covering field.