Revisiting the Control Systems of Autonomous Vehicles in the Agricultural Sector: A Systematic Literature Review

The primary objective of this article is to systematically review and categorize the diverse control algorithms applied in autonomous vehicles within the agricultural sector from 2000 to 2023. This systematic literature review (SLR) was conducted using Scopus and Web of Science databases to ensure a comprehensive coverage of peer-reviewed research. The geographical scope of this review is global, encompassing studies from various regions to present a holistic perspective on the technological advancements in autonomous agricultural vehicles. By employing a systematic literature review (SLR) methodology, this study meticulously analyzed published articles to identify, extract, and synthesize data on various control algorithms, which include their application and effectiveness in enhancing agricultural productivity and sustainability. The findings reveal a significant evolution in autonomous vehicle control systems, highlighting a trend towards integrating artificial intelligence (AI)-based control algorithms. These advancements suggest potential navigation and operational efficiency improvements, contributing towards sustainable development goals (SDGs) related to sustainable agriculture. This research presents a novel systematic categorization of control algorithms for autonomous agricultural vehicles by integrating control strategies into a multi-dimensional framework based on algorithmic type (linear, nonlinear, AI-based), application context (path tracking, stability control, obstacle avoidance), and agricultural field type (dry, paddy). Unlike previous reviews that primarily classify algorithms based on technical specifications alone, this study uniquely maps these algorithms to real-world agricultural challenges, providing a structured framework that aligns control methodologies with practical implementation scenarios. This approach enhances clarity in understanding algorithm suitability, adaptability, and scalability across different agricultural settings. The study’s broad implications suggest that enhanced control systems could revolutionize the agricultural sector by improving precision farming techniques. Future research directions include further exploration of AI and machine learning integration with control algorithms and their scalability across various agricultural settings. This SLR provides foundational knowledge and direction for future innovation in the farming sector’s autonomous vehicle technology.