MACHINE LEARNING–BASED TRACTOR AUTOPILOT SYSTEMS AND THEIR IMPACT ON SMART AGRICULTURE IN UZBEKISTAN

Abstract

This article explores the next generation of tractor autopilot systems based on machine learning (ML) and artificial intelligence (AI) technologies and their impact on smart agriculture in Uzbekistan. Unlike traditional GPS-only navigation systems, ML-based autopilot systems can analyze field conditions, predict optimal driving paths, and automatically adapt to changing soil, crop, and weather environments. The study examines technological structure, decision-making algorithms, and economic as well as agronomic benefits of intelligent autonomous tractors. Additionally, the article discusses the integration of big data, IoT sensors, and predictive analytics into agricultural machinery. The findings show that AI-driven autopilot systems represent a major step toward fully autonomous and data-driven farming in Uzbekistan.