Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot

Autonomous weeding robots are a productive and more sustainable solution over traditional, labor-intensive weed control practices such as chemical weeding that are harmful to the environment when used excessively. To achieve a fully autonomous weed control operation, the robots need to be precisely guided through the crop rows without damaging rice plants and they should be able to detect the end of the crop row and make turns to change rows. This research attempted to integrate GNSS, compass and machine vision into a rice field weeding robot to achieve fully autonomous navigation for the weeding operation. A novel crop row detection algorithm was developed to extract the four immediate rows spanned by a camera mounted at the front of the robot. The extracted rows were used to determine a guide-line that was used to precisely maneuver the robot along the crop rows with an accuracy of less than a hundred millimeters in variable circumstances such as weed intensity, growth stage of plants and weather conditions. The GNSS and compass were used for locating the robot within the field. A state-based control system was implemented to integrate the GNSS, compass and vision guidance to efficiently navigate the weeding robot through a pre-determined route that covers the entire field without damaging rice plants. The proposed system was experimentally determined to deliver good performance in low weed concentrations with an accuracy of less than 2.5 degrees in heading compensation and an average deviation from the ideal path of 45.9 mm. Though this accuracy dropped when the weed concentration increased, the system was still able to navigate the robot without inflicting any serious damage to the plants.