Valasek, John (Texas A&M University), Lu, Han (Texas A&M), Shi, Yeyin (University of Nebraska-Lincoln)

Development and Testing of a Customized Low-Cost Unmanned Aircraft System Based on Multispectral and Thermal Sensing for Precision Agriculture Applications

Scheduled for presentation during the "UAS Applications - VI" (ThC4), Thursday, June 15, 2017, 16:20−16:40, Lummus Island

2017 International Conference on Unmanned Aircraft Systems, June 13-16, 2017, Miami Marriott Biscayne Bay, Miami, FL,

This information is tentative and subject to change. Compiled on April 25, 2024

Abstract

The ability to conduct useful science under the framework of precision agriculture is not only dependent upon the collection of high quality usable data of plants, soil, and water, but also dependent upon the type of vehicle the sensors are flown on, properly tuned sensors, and the way in which the vehicle is flown. To achieve this capability requires the proper matching and integration of air vehicle, sensors, mission design, and image processing techniques. Although commercial Unmanned Air Systems are starting to be equipped with autopilots, sensors, and simple data processing software, they are often limited to only one sensor, and often lack cross platform integration expandability. This paper develops methodologies and procedures for a highly integrated fixed-wing Unmanned Air System that is customized for precision agriculture science. It addresses sensor selection, vehicle platform selection, flight planning, and data processing procedures. The approach is validated by assessment of collected imagery and data from flights conducted on actual plots. Results presented in the paper show that by comparison to data collected during earlier flights with a non-integrated system, the approach presented here which matches vehicle characteristics to sensor characteristics and employs proper flight planning, mission design, and auto-triggering of the sensor produces better data quality, and improved mosaicking. The approach is judged to be a promising candidate for improved data collection for precision agriculture.