Abstract
Corn (i.e. maize, Zea mays L.) is recognized globally as a critical crop for food security and silage production. Brown-midrib (BMR) corn, and similar varieties are particularly desirable in regions such as the Northeastern United States but come at the risk of decreased disease resistance, raising concerns due to the ubiquity of disturbances. Farms within this region are typically smaller than 60.7 hectares and have a low adoption for precision agriculture technologies and practices. Unmanned Aerial Vehicles (UAV) could provide field-scale observations which are instrumental to efficient and timely management of desired corn varieties. In the current research, UAV multispectral imagery was acquired to conduct an exploration of field specific corn dynamics throughout the 2024 growing season. Eight weeks of imagery were used to spectrally differentiate brown-midrib and non-brown-midrib (non-BMR) corn. A combination of several spectral pattern analyses provided clear evidence for surveying fields at approximately 90-106 days after sowing. Additionally, these tests reported a clear separation of brown-midrib corn fields using the red edge and near infrared wavelengths. A machine learning classification of corn varieties by field achieved overall thematic accuracies up to 98.7%. In a supplemental analysis used to estimate plot level yield measurements using the UAV imagery, narrow-band red edge image data (717 ± 6 nm) achieved the highest coefficient of determination (R2 = 0.498). The results of this spatial data exploration and analysis provide consistent support for the use of UAV as a precision agriculture technology and fundamental tests for ensuring that the data support field-specific insights.
Department
Agriculture, Nutrition, and Food Systems
Publication Date
1-6-2026
Journal Title
Agricultural Research
Publisher
Springer Nature
Digital Object Identifier (DOI)
Document Type
Article
Recommended Citation
Fraser, B.T., Congalton, R.G., Davis, P.H. et al. Use of Unmanned Aerial Vehicles in Precision Agriculture: An Exploration of Remotely Sensed Data and Methods for Monitoring Corn Varieties. Agric Res (2026). https://doi.org/10.1007/s40003-025-00923-x
Comments
This is an Authors Accepted Manuscript of an article published by Springer Nature in Agricultural Research in 2026, the Version of Record is available online: https://doi.org/10.1007/s40003-025-00923-x