Systematic review on AI techniques in detection and navigation of agricultural machines and robots

Integrating artificial intelligence (AI) into farm machinery and robotics is increasingly pivotal for the advancement of smart agriculture. Modern AI-driven systems have transformed agricultural production by optimizing land and labor use, improving operational capacity, and ensuring greater timeliness. This review synthesizes key trends, applications and recent developments in the integration of AI into drones, farming machinery, and intelligent robots (FMIRs), with a particular emphasis on detection and navigation technologies applied to tasks ranging from planting to harvesting. Based on PRISMA guidelines, the study employs a bibliometric analysis based on keywords extracted from previous research in the field. The findings demonstrate how AI-based detection and navigation techniques enhance FMIRs automation, particularly in weed control and autonomous navigation, while identifying the most effective AI approaches currently in use. Furthermore, the re-view outlines innovation opportunities and persistent challenges, offering insights that can guide future research and drive the next generation of AI-enabled agricultural machinery and robotics.