Extraction of row centerline at the early stage of corn growth based on uav images

Automatic extraction of crop row centerline is an important technology for agricultural automation, and it has a wide range of applications in automated operations, such as automatic agricultural navigation, automatic harvesting, automatic weeding and automatic seedling replenishment. In this study, the method of row centerline detection is proposed by combining image segmentation and the technique of feature point extraction, and it is applied to the extraction of corn missing seedling locations. Firstly, image segmentation is performed by combining the improved vegetation index ExGG and a double-threshold algorithm (the OTSU method combined with the Particle Swarm Optimization algorithm), and most of the pseudo-feature points are removed using median filtering to initially separate corn seedlings from weeds and soil. Then, the number of crop rows is obtained using the vertical projection method; the micro-region of interest(micro-ROI) is used to find the center of mass and extract the feature points. Finally, the remaining pseudo-feature points are removed by the location clustering method, and the crop row centerline is fitted using the linear regression method of least squares. This study extracts the location and number of missing seedlings of corn based on the information from the row centerline, providing technical support for the subsequent seedling replenishment operation. The experimental results show that the accuracy of the proposed method for detecting the centerline of corn seedling rows is 0.016 degrees, which is better than the Hough transform.