Implementation of a Low-Cost Vision Method for Autonomous Navigation of Agricultural Robots

Adoption of precision agriculture in developing countries remains low due to lack of affordable technologies, and the benefits cannot be sufficient to justify the costs. In this context, a cost-effective navigation method can be implemented to enable autonomous driving in agricultural fields using only low-cost camera and conventional GPS. This method potentially enables agricultural autonomous machines to precisely navigate between crop rows and avoid obstacles in real-time operation. Such method can be helpful for small farmers to do cost-effective tasks of weeding and applying chemicals. This work aims to develop a low-cost vision navigation system based on a low-cost RGB camera vision system as an alternative to use of a precise RTK GPS system. By implementing an image processing method based on RANSAC algorithm, the tests showed the ability of implementing an accurate detection method of crop lines and boundary limits as keys of an efficient driving system. This vision system can be improved using repetitive training of the imaging process and taking advantage of recent advances in the field of machine learning. Development of this cost-effective autonomous navigation system is potentially of great interest for small farming systems to operate machines between crop rows and carry out agricultural tasks.