Session: 15-01-01: ASME International Undergraduate Research and Design Exposition

Paper Number: 145591

145591 - Sampling-Based Regenerative Agriculture: Uncrewed Soil Health Monitoring System 

The significance of 'Regenerative Agriculture' is escalating in response to the challenges posed by global warming. The primary aim of regenerative agriculture is to restore soil and ecosystem vitality, rectify disparities, and ensure the preservation of our land, water bodies, and climate for future generations. An initial systematic examination uncovered a broad spectrum of interpretations for regenerative agriculture, even if it is commonly construed as a framework encompassing principles, methodologies, or outcomes geared towards enhancing soil health, biodiversity, climate resilience, and ecosystem functionality. Nonetheless, the absence of a universally accepted definition and limited bioeconomic evaluations impede the comprehensive comprehension and widespread adoption of regenerative agriculture. To realize regenerative agriculture from the most basic layers, we propose a novel system in which a robotic soil sampling-based soil health monitoring system.

Soil health monitoring is a key part of regenerative agriculture. This entails employing a robotic soil health monitoring system based on sampling methods. The system comprises unmanned ground vehicles (UGVs) equipped with a sophisticated core sampling mechanism and detection and planning capabilities. The smart core sampling mechanism utilizes linear, rotational, and vibrational motions and operates simultaneously to apply penetration force, aimed at minimizing reaction forces that could lead to system failure. We consider system failure when the reaction force is larger than the penetration force, which means the whole system can be lifted. The system targets a maximum penetration depth of approximately 3 feet (1 meter), capturing soil samples in sealed core samplers. A novel storing and feeding system facilitates the sampling of multiple cores. The detection and planning system utilizes LIDAR, high-resolution cameras, GPS, and proximity sensors for path planning, mapping, object detection, navigation, and obstacle avoidance. Additionally, the system incorporates soil and ambient sensors including pH (potential of hydrogen), NPK (nitrogen (N), phosphorus (P), and potassium (K)), moisture, temperature, and pressure sensors to gather data at sampling points. This data serves as the foundation for mapping farming areas and developing a feedback system to optimize sampling point selection for enhanced effectiveness.

The primary objective of this study is to establish a robust infrastructure for regenerative agriculture. To achieve this, we will enhance the soil health monitoring system by collecting comprehensive data using readily accessible unmanned robots and high-resolution sensors. Moreover, we will adopt AI-based data analysis methods to analyze the extensive data which is collected by the proposed uncrewed soil health monitoring system. This proposed system has the potential in terms of scalability by adapting the multi-robot system. The number of UGVs can be optimized based on the size of the targeted farming area and other types of robots such as unmanned aerial vehicles (UAVs) or unmanned surface vehicles (USVs) to form a heterogeneous robot team.

Presenting Author: Jackson Otis Rochester Institute of Technology

Presenting Author Biography: Jackson Otis is a junior student at the Department of Manufacturing and Mechanical Engineering Technology, College of Engineering Technology at Rochester Institute of Technology. He is in the BSMS program in the department. He is also a player of the RIT men's basketball team. His research interest lies in robotics and automation and he is working with Dr. Jun Han Bae as the main advisor to work towards his capstone project.

Authors:

Jackson Otis Rochester Institute of Technology
Jun Han Bae Rochester Institute of Technology