Comparison of the Edge Detection Methods to Detect, Identify and Locate the Obstacles for Agricultural Robotic Vehicles

The obstacle detection in an agricultural field is an important step of the automation of the plantation. There are already developed autonomous agricultural vehicles that can track a path, and perform the specified processes on the plantation fields. These autonomous agricultural robotic machines need an upper level of control, which is mostly performed manually, for the design of the reference paths. Detection of the agricultural obstacles is necessary to accomplish these manual tasks in an automatic manner. In this study, statistical methods are employed to determine which of the five well-known edge-detection methods is best, for the high-level path planning in an agricultural automation of autonomous agricultural vehicles depending on field and image properties