From Research to Deployment in Autonomous Agricultural Machinery: A Review of Path-Planning Technologies Against a Deployability Assessment Framework

Abstract

Global labour shortages in the agricultural sector, combined with diminishing arable land and a growing population, are driving investment in autonomous agricultural machinery. Autonomous systems that can navigate crop environments and perform planting, treatment, and harvesting alongside humans are required, but the gap between published research and commercially deployed systems remains wide across most operational scenarios. Why are agricultural robots still not widely deployed in real farms despite decades of research in autonomous navigation and path planning, and what is preventing full farm autonomy? This paper reviews the principal enabling technologies for autonomous agricultural integration, with a specific focus on path planning as the differentiator between research-stage and deployed systems. Current research in human–robot integration, open-field navigation, row identification and following, crop sensing, and power efficiency is synthesised and evaluated against a deployability criterion. A Deployability Assessment Framework is introduced, comprising structured tables that assign Technology Readiness Levels to twelve path-planning families and benchmark eleven commercial and research platforms against field-validated accuracy data. The analysis shows that point-to-point GNSS navigation has reached TRL 9 with over one million commercial units deployed, vision-based crop row following is at TRL 5–7 depending on crop and season, and whole-farm autonomy with dynamic re-planning is at TRL 3–5. The primary barriers are the absence of standardised evaluation benchmarks, the failure of perception models to generalise across seasons and crop types, and the decoupling of terrain and slip feedback from global path planners. Our review reveals that open-field GNSS navigation is commercially mature, but true whole-farm agricultural autonomy remains unsolved because current systems are not robust enough across seasons, terrain, sensing conditions, and operational transitions.