Author ORCID Identifier

https://orcid.org/0000-0003-0842-701X

Date of Award

3-2025

Document Type

Thesis (Ph.D.)

Department or Program

Engineering Sciences

First Advisor

Laura E. Ray

Second Advisor

Minh Q. Phan

Third Advisor

Lance Gibson

Abstract

Enhancing agricultural production while reducing input costs remains a central challenge in modern row-crop management. Recent advances in computation, imagery, and sensors are enabling more efficient practices across various agricultural domains, and automation technologies are increasingly available to manage tasks central to perennial crop development. Automation in row-crop agriculture, by contrast, lags behind. This thesis explores utilizing small, unmanned ground vehicles to transform row cropping through the implementation of unconventional, in-season management strategies. The first focus of this work considers improvements to nitrogen fertilization using small, autonomous vehicles. An agronomy experiment in corn assessed the effects of gradually applying nitrogen fertilizer over time, with the goal of evaluating how this approach impacts both yield and nitrogen input requirements. Alongside parametric analyses, this study demonstrates the feasibility of using micro-UGVs to manage fertilization despite payload limitations. To unlock these precision practices, autonomous operations under-the-canopy must be improved substantially. Navigating densely planted row-cropping environments with cameras or ranging sensors is challenging due to sensor occlusion, lighting variability, and the difficulty of distinguishing flexible surfaces like leaves from rigid obstacles like stalks. A novel tactile-based perception system comprising a mechanical feeler sensor and supporting algorithms was engineered to detect nearby obstacles like rigid cornstalks while filtering out flexible features like weeds and leaves. Then, through simple kinematic relationships, the system was used to accurately determine the position of these obstacles, such that a blind robot can traverse the messy environment. Through simulation and real-world testing, the system demonstrated effective navigation in complex agricultural iii conditions, overcoming challenges like row curvature, planting gaps, dense weeds, and canopy variability—without relying on vision or ranging sensors. Additionally, mobility is crucial to navigating row crops; tight row spacing constrains vehicles and limits their ability to recover from immobilizing conditions. A second prototype vehicle is presented with innovative mechanical systems that restore mobility in response to incipient immobilization and extricate the vehicle from challenging terrain. The results of this thesis establish novel models and methods to overcome visual impairment and immobilization for agricultural robots, enabling new, unconventional forms of precision agriculture for row crops.

Original Citation

Adam M. Gronewold, Philip Mulford, Eliana Ray, Laura E. Ray, Tactile Sensing & Visually-Impaired Navigation in Densely Planted Row Crops, for Precision Fertilization by Small UGVs, Computers and Electronics in Agriculture, Volume 231, 2025, 110003, ISSN 0168-1699, https://doi.org/10.1016/j.compag.2025.110003.

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