A Semi-Autonomous Robotic System for In Situ Soil Sampling, Analysis, and Mapping in Precision Agriculture

Abstract

Traditional soil sampling and analysis methods are labour-intensive, time-consuming, and lack the spatial resolution needed for precision agriculture, especially under increasing pressures of rising fertilizer costs, climate variability, and the demand for sustainable intensification. To address these challenges, we present a semi-autonomous robotic system for in-situ soil sampling, analysis, and mapping of key soil properties. The system integrates three core components: a Sample Acquisition System (SAS) for precise, automated soil collection; a Sample Analysis Lab (Lab) for rapid, on-site measurement of pH and macronutrients; and an adaptive sampling algorithm (IAS-Pseudo) that leverages pseudo-observations to maintain continuous operation while analysis is in progress. We validated the system through initial field demonstrations involving 30 samples collected on a pasture field. Results show that the SAS reliably acquires soil cores of 40–50g at a depth of 200mm, and the Lab delivers pH and macronutrient measurements within 10 minutes per sample, with mean errors of 7.8% (N), 13% (P), and 7.1% (K) relative to laboratory analysis. While nitrogen and potassium measurements showed strong agreement with lab ground truth, phosphorus accuracy remains limited by current sensor technology. The IAS-Pseudo strategy reduced mission duration by mitigating analysis delays while maintaining competitive prediction accuracy. These results represent a proof-of-concept validation of a semi-autonomous, integrated system, providing a foundation for future advances toward fully autonomous, scalable, and data-driven soil monitoring and nutrient management.