Extraction algorithm of the center line of maize row in case of plants lacking

Abstract: Identification of crop centerlines has been one of the most essential links in the environmental perception, particularly for the detection of driving paths during operation for the emerging unmanned agricultural machinery at present. However, the current detection of centerlines presents a low accuracy in the extraction of lacking rows for the maize seedling. In this study, an algorithm was proposed to extract the centerlines of maize rows in the lacking seedlings. The collection date was in July 2020, and the experimental subjects were maize seedlings. The height of the seedling was 0.3-0.4 m and the seedling spacing was 0.2-0.3 m at the time of image collection. The height of the camera was 1.5 m and the pitch angle was about 30°. The images of maize seedling rows were also collected in different plots of the experimental fields to ensure the universality of samples. Firstly, the range of HSV color components was limited to segment the seedlings and the background. The average time of threshold processing per frame of the image was 0.013 s. Morphological processing was utilized to fill the holes in the crop areas of denoised images. Secondly, a strip Region of Interest (ROI) was set in the horizontal position at the bottom and middle of the images. The barycenter was extracted from the seedlings contour located in the ROI as the locating points. Specifically, the upper endpoint was determined by the fixed step size in the pixel point of the first line of the image. The row area of the crop within a limited range was scanned using a straight line through the locating points and upper endpoint, where the line that crossed the most seedlings was the optimal line of target seedlings. As such, the contour feature of the seedling was strengthened, and the lack of seedling in the bottom area was filled, when the optimal line was fused with the seedling area. Because the algorithm was used to extract the crop centerline under different conditions of seedlings lacking, the optimal lines at the bottom and the middle of rows were fused with the region to fill the lacking part of the row. Finally, the dynamic ROI was set, where the fitting profile of the maximum area within the region was the target centerlines of seedling rows. The experimental results showed that the algorithm fully met the extracting requirement for the centerlines of seedlings in the field with seedling deficiency, compared with the traditional. It was also utilized to deal with the low detection rate when there was a seedling deficiency. Experimental verification was also performed on 1 190 frames of maize seedlings images for the reliability of the algorithm in the lack of seedlings. The results showed that this algorithm required a relatively small amount of computation. Specifically, the average accuracy rate was 84.2%, and the average detection time of each frame was 0.092 s, indicating a better filling effect on the maize seedlings row with different crop lacked conditions. Consequently, the improved algorithm presented strong robustness and high accuracy for the recognition rate when seedlings were lacking. The finding can provide sound theoretical support to the operation of unmanned agricultural machinery in the field environment of seedlings lacking.