Purdue University Graduate School
Heusinger Thesis MS.pdf (4.26 MB)


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posted on 2021-12-18, 22:18 authored by Logan Joshua HeusingerLogan Joshua Heusinger
Farmers run complex operations to fully plant, manage, grow, and harvest crops through the seasons. To help alleviate the tough decision-making process, tools have been created to inform farmers about their machinery and field status. GPS localization and machine state identification provides useful information to farm managers. A tool was created that successfully utilizes GPS data taken from loggers on tractors, combines, and grain trucks to successfully identify the states of all the machines in the field, including, idle, active, on the go, and stationary unloading. Initial results of the algorithm provide a 96% success rate in determining the state of the combine during harvest. Additionally, the algorithm was accurate at determining the state of grain carts and grain trucks at the boundaries of the field 94% of the time. In addition to GPS state identification, LoRa was identified as a potential technology which could link grain trucks and combines in real time providing useful information for in field decision making. Using a mobile end node and stationary gateway, testing was done to evaluate the performance and range of LoRa at long range and speeds ranging from 8 km/h to 96 km/h. Testing revealed a packet reliability of 77% at 8 km/h and a packet reliability of 43% at 96 km/h. A sharp decline in packet reliability was identified around a speed of 16km/h.


Degree Type

  • Master of Science in Agricultural and Biological Engineering


  • Agricultural and Biological Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

John Evans IV

Additional Committee Member 2

John Lumkes

Additional Committee Member 3

Dennis Buckmaster