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Modeling and Analysis of Ground-based Autonomous Agricultural Vehicles

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posted on 16.08.2019 by Gabriel J Wilfong
In the years to come, a growing global population will require more crop production than ever before. As technological advances improve across all industries, autonomous agricultural vehicles (AAVs) can be part of the solution to the rising demand for food. By improving and transforming conventional farming methods, AAVs have the potential to transform the way farming operations are completed. AAVs are a class of robotic machines that have the ability to complete agricultural tasks without requiring direct and constant control of a human operator. By removing the need for an operator, these agricultural robotic machines allow for new vehicle designs and new opportunities for different vehicle configurations and sizes.

A simulation model was developed that calculates the energy requirements of AAVs operating on row crops. This deterministic model was used to quantify the energy needs and energy expenditures of agricultural vehicles, and to investigate the effects of using AAVs in lieu of conventional human-operated agricultural machinery.

The energy model was demonstrated using a pre-defined scenario of a typical row-crop farming operation in the Midwest U.S. The purpose of the case study was to compare a conventional crop production operation with operations that have implemented autonomous machines. Four general vehicle configurations were chosen based on the traction machine size: large tractors (e.g.,~greater than 60 kW), small tractors (e.g.,~less than 60 kW), utility vehicles (e.g.,~John Deere Gator), and single row machines. The complete crop production operation was based on a farm size of 607 ha (1,500 acre) with half the land devoted to corn production. The four main operations were fertilizer application, pesticide spraying, no-till planting, and harvesting.

First, the energy model was used to compare a whole farm operation with three different machine configurations: using all conventional large machines, using all autonomous large machines, and using all autonomous smaller machines (55 kW tractor). The results show that from an energy standpoint, the most significant savings comes from the decreased amount of agrochemical application associated with AAVs. The total energy consumption of the large tractor AAV configuration is 36% less than the conventional operation (11,081 MJ/ha vs. 7,090 MJ/ha).

In order to have a better perspective on the effects of using AAVs, further analysis was conducted on an individual operation basis: fertilizing, pesticide spraying, no-till planting, and harvesting. Because AAVs can work 24-hours per day, the fertilizing operation for the single large tractor AAV could be completed in 1.6 working days, as opposed 2.4 working days for the conventional machine. It only required two small tractor AAVs to meet or exceed the performance of the conventional machine, yet for the same amount of money, four to five small tractor AAVs could be purchased.

The greatest benefit to utilizing AAVs is the intelligent application of pesticide, which can allow for 65--95% reduction in chemical use. The spraying operation highlighted the advantages of large machines (conventional and autonomous), namely speed of operation and width. It takes two small tractor AAVs, seven utility AAVs, or 12 single-row AAVs to match their performance.

For the no-till planting operation, two small tractor AAVs, seven utility AAVs, or 39 single-row AAVs are required to match the performance of conventional machinery. However, for the same cost as the conventional machine, six small tractor AAVs, 16 utility AAVs, or 55 single-row AAVs could be purchased. The benefit of using higher numbers of AAVs is seen in the amount of time required to complete the planting task, where the swarms of AAVs could finish planting in nearly 1/3 of the time.

Harvesting was previously analyzed during the whole farm scenario. In general, the energy consumption and costs are relatively the same between the conventional machine and the large AAV. The advantage of the autonomous harvesting is that is can operate continuously throughout the night. Continuous operation is possible for this scenario because corn can be harvested at night. However, soybeans cannot because the onset of dew at dusk does not allow for proper processing of the crop.

Along with the energy model, crop production efficiency metrics were studied that provided an objective method of analyzing the advantages and disadvantages associated with replacing and/or augmenting conventional farming vehicles with AAVs. Energy-per-unit-area shows the amount of energy that is consumed over the entire field, regardless of the task time required. Because labor energy consumption is insignificant compared to the other three inputs, energy-per-unit-area is also independent of the number of machines simultaneously in use. Working days and machinery capital cost are other metrics that proved beneficial when comparing AAVs to conventional machines.

Finally, a modeling tool was developed and demonstrated that allows a user to interact with the energy model in an intuitive way. Creating the modeling tool in Microsoft Excel allows for easy distribution to a wide audience, as opposed to using a more expensive software package. The energy model workbook is composed of five spreadsheets that contain instructions, inputs, outputs, and supporting data tables. A GUI was created using Microsoft Excel VBA that lets the user interact with an event-driven program. Data sets can easily be created and modified for the purpose of evaluating different farming operations. Additionally, options within the GUI allow for parameter studies where multiple data sets can be instantly created in order to analyze the effects of changing a single variable.


Degree Type

Doctor of Philosophy


Agricultural and Biological Engineering

Campus location

West Lafayette

Advisor/Supervisor/Committee Chair

John Lumkes

Additional Committee Member 2

Dennis Buckmaster

Additional Committee Member 3

John Evans

Additional Committee Member 4

David Cappelleri

Additional Committee Member 5

Roger Tormoehlen



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