Auto-configuration of an autonomous farming vehicle using Machine Learning

Precision agriculture has many benefits especially for the developing world. Autonomous tractors and automatic planting systems have high accuracy, resulting in a substantially improved return on investment for growers, making food planting more economical. Moreover, the tractors can collect information on soil conditions, which can lead to improved maintenance of the crops, prevent blights, and achieve higher efficiency and higher plant food quality. Autonomous tractors can increase the number of farming hours per day, as they can work even after dark, which will speed up the process of farming tasks. Farming autonomous vehicles can disrupt agriculture, and provide a solution to world hunger. Autonomous farming vehicles need to operate in harsh environments that may be unpredictable. The research of this Accelerate project aims to tackle the challenges of controlling an autonomous tractor on harsh terrains of a farm, by auto-configuration of the vehicle and feedback control using Machine Learning techniques.

Faculty Supervisor:

Benoit Boulet


Zeinab Sobhanigavgani


Innovative Vehicle Institute


Engineering - computer / electrical


Automotive and transportation




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