Precision Agriculture Robots for Input Optimization in Greenhouse Vegetable Production: Experimental Analysis

Abstract

The integration of robotic systems into agricultural environments represents a critical paradigm shift aimed at addressing the dual challenges of global food security and environmental sustainability. This study presents a comprehensive experimental analysis investigating the direct linkage between the deployment of precision agriculture robots and input optimization in greenhouse vegetable production. By conducting a longitudinal experiment over a complete crop cycle, this research evaluates how autonomous ground vehicles equipped with advanced multispectral imaging and targeted micro-dispensing capabilities influence the consumption rates of critical inputs, specifically irrigation water, synthetic fertilizers, and chemical pesticides. The methodology contrasts a conventionally managed greenhouse zone with a strictly robot-managed zone, measuring resource utilization, crop yield, and plant health metrics. The findings demonstrate substantial reductions in input usage without compromising agricultural output, thereby validating the economic and environmental viability of robotic interventions. Furthermore, this paper elucidates the underlying mechanisms by which high-resolution spatial and temporal data collection enables micro-level resource allocation, shifting the agricultural framework from uniform application to plant-specific optimization. The resulting evidence provides a foundational framework for future implementations of scalable robotic solutions in controlled environment agriculture, ultimately contributing to more resilient and resource-efficient food production systems.