GPS Guided Autonomous Navigation of a Small Agricultural Robot with Automated Fertilizing System

In this paper, the design, implementation, and testing of an autonomous agricultural robot with GPS guidance is presented. This robot is also responsible for weed detection and killing by spraying appropriate herbicide as well as fertilizing. This rover is powered by 5 12 V electric bike batteries and two electric motors. Machine learning algorithms such as Haar feature-based cascade classifiers has been utilized to detect three kinds of common weeds found in a corn field. The robot control system consists of GPS guided control of propulsion system and steering actuators, an image processing and detection system, and a spray control system for herbicide and fertilizer applications. Multiple microprocessors such as Raspberry Pi 3, Arduino, as well as an on-board computer have used to provide all control functions in an integrated fashion. Open sources software such as Mission Planner and ReachView have been used to provide autonomous guidance of the vehicle. This vehicle successfully participated in 2017 AgBot Challenge where it traversed the corn field autonomously and performed detection of weeds and spraying herbicide with certain degree of accuracy and applying fertilizer. The experimental results demonstrate that such autonomous AgBot technology is very promising and can be further developed to provide full functionality and greater degree of accuracy.