Work

Dense Proximal Sensing of Soil Apparent Electrical Conductivity using Autonomous Field Robotics

Year: 2026

Type: article

Abstract: Purpose: Existing approaches for measuring soil apparent electrical conductivity (ECa) of micro-irrigated orchards are effective but time-consuming and labor-intensive, because of bulky sensor use cases and measurement assessment in inaccessible areas under tree canopies. Prior work suggests that robot-assisted autonomous sampling is feasible with a high degree of accuracy using existing theoretical planners, such as the Next-Best-Action Planner. However, it does not consider the scalability and time-sensitive nature of EMI-ECa measurements. This work addresses these challenges using an augmented planner that considers operational time alongside the priority of sampling locations. Methods: A real-time planning algorithm was implemented on a wheeled robot for autonomously sampling at informed site-specific locations across a large field. The planner focuses on balancing scalability and operational time. The scalability of the planner was increased by maximizing the number of sampling locations visited while preserving the priority of the informed site-specific locations. The operational time of the survey was considered using time as an alternative to monitor the progress of the mission. Sampling missions were then executed for the proximal sensing of ECa using an EMI sensor on a large survey site. Results: A dense sampling approach was considered for completing the survey under timing constraints. The proposed planner completed the survey during the allotted time of two hours ( $$\approx$$ 1h 44 m). Following the mission, the collected EMI-ECa data were processed to present the spatial and single-tree measurements, demonstrating the efficacy of the developed method. Conclusion: This work advances autonomous ECa surveying in orchards, providing a dense sampling approach that considers scalability, timing, and priority of informed locations.

Source: Precision Agriculture

DOI: 10.1007/s11119-026-10376-z

Authors: Aritra Samanta, Bhawana Acharya, Dimitrios Chatziparaschis, Elia Scudiero, Konstantinos Karydis

Institutions: University of California, Riverside, U.S. Salinity Laboratory

Language: English

Topic: Smart Agriculture and AI

Subfield: Plant Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

Open Access status: hybrid

Estimated APC paid: $4,190