Detection navigation baseline in row-following operation of maize weeder based on axis extraction

Detection navigation baseline is primary for the automation of maize weeder in seedling. In the navigation technology based on machine vision, maize seeding or weed near the camera is photographed as a discrete area, while a plant far away from the camera is photographed as a strip area along with other plants in the same row. The two problems cannot be solved by one method. However, in this paper, an algorithm of detection navigation baseline in the row-following operation of maize weeder based on axis extraction was proposed to solve the both problems. Firstly, plants are distinguished from the background based on color feature, and the binary image is acquired. Secondly, plants are described as a set of connected components with numbers after connected components labeling and noise clearing. Thirdly, the axes of all connected components are extracted according to the calculation method of rotary inertia in physics. Next, the abnormal connected components with axes are deleted because the angles between the axes and X-axis are above angle threshold. Then, the judgment model is built based on angle tolerance and distance tolerance, the connected components in a same row based on this model through two-step traversal are merged, and a new axis is re-extracted as the axis of the plant row. Finally, the navigation baselines are detected based on the axes of the plant row. The experimental results show that the accuracy of this algorithm is more than 93%, and the computing time is less than 1.6 s, which can meet the accuracy and real-time performance requirements of maize weeder.