Master’s Thesis at the University of Basrah Explores the Use of Artificial Intelligence to Improve Tractor Performance
A master’s thesis at the College of Agriculture, University of Basrah, examined the topic entitled “Prediction of Power and Fuel Losses in Two-Wheel and Four-Wheel Drive Tractors Using Artificial Neural Networks and the Levenberg–Marquardt Algorithm.”
The study, presented by researcher Banban Abdul Hakim, aimed to develop predictive models for estimating agricultural tractor performance indicators under different operating conditions, including varying tillage depths, multiple forward speeds, and different drive systems.
The findings revealed that tillage depth was the most influential factor in increasing draft force, wheel slip, and fuel consumption, followed by forward speed, while engine speed had a comparatively lower effect.
The study also demonstrated the superiority of the four-wheel-drive system in reducing wheel slip and power losses, thereby enhancing tractor efficiency and contributing to improved agricultural productivity.
Department of Media and Government Communication
