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PhD Thesis Proposal: Adam Gronewold



1:00pm - 3:00pm ET

Rm 101, MacLean ESC/Online

For info on how to attend via videoconference, email

"Spoon-Fed Fertilizer: Efforts toward a robotic management vehicle to nurse row crops toward more efficient outcomes"


Agricultural production needs revolutionary approaches to produce more with less. Precision agriculture has already taken great leaps in this pursuit, but disparate rates of innovation exist between perennial cropping systems and commercial row cropping. This thesis makes the case that compact, collaborative robotic vehicles can be used to great effect in enhancing yields and reducing input costs in row cropping systems. First, modern farming practices and the associated challenges are discussed, namely those related to cost, efficiency, and impacts on the environment. Attention is then given to existing strategies and technologies available to producers to use nitrogen fertilizer more efficiently. An argument is made that more favorable agronomic outcomes can be realized by using single-row robotic assistants to slowly spoon-feed crops over time, much like we nurse an infant in the early stages of its life.

I performed a study in the summer of 2022 to assess this assumption, comparing corn silage yields from conventional fertilizer regimes with those of modified fertilizer regimes and high-fertilization frequency nitrogen schedules. The study considers not only yield, but developmental rates of crops, soil nutrient content over time, and forage nutrients across the various treatments. The results of the study show that high-fertilization frequency does in fact lead to higher yields with more nutrient dense forage all while using reduced fertilizer amounts, demonstrating the potential of utilizing compact agricultural vehicles to manage fertilization at scale. Finally, the proposed research moving forward is discussed. The focus of the proposed work is directed at engineering cost-sensitive sensing technologies that may be implemented on a low-cost robotic platform in contribution of a satisficing control architecture that allows the conceptual vehicles to robustly raster a crop field in completion of management tasks.

Thesis Committee

  • Laura E. Ray (Chair)
  • Minh Q. Phan
  • Klaus Keller
  • Sarah Masud Preum
  • Lance Gibson


For more information, contact Theresa Fuller at