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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #353323
Title: Monitoring soybean growth and yield due to topographic variation using UAV-based remote sensing

Author
item ZHOU, JIANFENG - University Of Missouri
item FENG, AIJING - University Of Missouri
item Sudduth, Kenneth - Ken

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/25/2018
Publication Date: 6/24/2018
Citation: Zhou, J., Feng, A., Sudduth, K.A. 2018. Monitoring soybean growth and yield due to topographic variation using UAV-based remote sensing. International Conference on Precision Agriculture, June 24-27, 2018, Montreal, Quebec. Paper No. 5330.

Interpretive Summary: Remote sensing can be an efficient way to obtain information about spatial variation across fields and landscapes. Recent availability of unmanned aerial vehicle (UAV) technology has created interest in developing new remote sensing applications for use in precision agriculture. Field topography, including slope, elevation, and other variables, can strongly affect water availability across the landscape, and therefore crop yields. Sensor-based topography measurements are typically accomplished with global positioning system (GPS) receivers mounted on field equipment or by use of LiDAR sensors deployed in aircraft. A relatively new approach to topography mapping involves obtaining digital orthophotos with UAV-mounted cameras and processing those photos to obtain a digital elevation model (DEM). The goal of this study was to implement such UAV-based topography mapping in a small field, relate UAV measurements to those from in-field GPS equipment, and then investigate the relationship of topography to soybean growth and yield. Elevation data from UAV and GPS were found to be strongly and linearly related. Relationships of topography to soybean growth varied within the growing season, perhaps due to changing water availability. Soybean yield was highest in the flatter, higher-elevation summit positions of the field. This study has demonstrated the potential for using UAV-based topography mapping in precision agriculture. This approach may be useful to researchers and to farmers who are interested in obtaining topographic information at relatively low cost and high resolution.

Technical Abstract: Remote sensing has been used as an important tool in precision agriculture. With the development of unmanned aerial vehicle (UAV) technology, collection of high-resolution site-specific field data becomes promising. Field topography affects spatial variation in soil organic carbon, nitrogen and water content, which ultimately affect crop performance. To improve crop production and reduce inputs to the field, it is critical to collect site-specific information in a real-time manner and at a large scale. The goal of this study was to evaluate the feasibility of a remote sensing system based on a UAV and imaging sensors to quantify the influence of topographic variables on apparent soil electrical conductivity (ECa) and plant performance. The experiment was conducted in 6.2 ha area within a 20-ha research soybean field with varying topography. Geo-referenced ECa data were collected before planting soybean in 2017. Geo-referenced crop yield was measured using a yield monitor system in 2016 and 2017. A RGB camera and a multispectral camera were used to take images on the field at four critical times during soybean growth in 2017. The UAV system was flying at an altitude of 100 m or 50 m above ground level with an image overlap > 70%. The image data were processed to generate geo-referenced orthophotos and a digital surface model (DSM) which was used to develop a digital elevation model (DEM). Results showed that image-based elevation represented 95% of the variability in elevation as measured by a GPS system. Results show that the relationships of field topography, i.e. elevation and slope, to soil ECa and crop were significant. Field regions with the lowest elevation had significantly lower yield and the lowest normalized difference vegetation index (NDVI) values, indicating a negative effect on crop development. Meanwhile, field slope also showed significant relationships to crop development, with significantly lower NDVI and crop yield in regions having the highest slope. The study showed that it was possible to use UAV-based remote sensing for monitoring crop growth and yield differences due to topographic variation.