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Open AccessArticle

Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn

1
University of Nebraska Extension, University of Nebraska-Lincoln, 116 W 19th Street, Falls City, NE 68355, USA
2
Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Keim Hall, 1825 N 38th Street, Lincoln, NE 68583-0915, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1597; https://doi.org/10.3390/rs12101597
Received: 6 April 2020 / Revised: 14 May 2020 / Accepted: 15 May 2020 / Published: 17 May 2020
(This article belongs to the Special Issue Remote Sensing for Sustainable Agriculture and Smart Farming)
Determining the optimal nitrogen (N) rate in corn remains a critical issue, mainly due to unaccounted spatial (e.g., soil properties) and temporal (e.g., weather) variability. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors may provide opportunities to improve N management by the timely informing of spatially variable, in-season N applications. Here, we developed a practical decision support system (DSS) to translate spatial field characteristics and normalized difference red edge (NDRE) values into an in-season N application recommendation. On-farm strip-trials were established at three sites over two years to compare farmer’s traditional N management to a split-application N management guided by our UAV sensor-based DSS. The proposed systems increased nitrogen use efficiency 18.3 ± 6.1 kg grain kg N−1 by reducing N rates by 31 ± 6.3 kg N ha−1 with no yield differences compared to the farmers’ traditional management. We identify five avenues for further improvement of the proposed DSS: definition of the initial base N rate, estimation of inputs for sensor algorithms, management zone delineation, high-resolution image normalization approach, and the threshold for triggering N application. Two virtual reference (VR) methods were compared with the high N (HN) reference strip method for normalizing high-resolution sensor data. The VR methods resulted in significantly lower sufficiency index values than those generated by the HN reference, resulting in N fertilization recommendations that were 31.4 ± 10.3 kg ha−1 higher than the HN reference N fertilization recommendation. The use of small HN reference blocks in contrasting management zones may be more appropriate to translate field-scale, high-resolution imagery into in-season N recommendations. In view of a growing interest in using UAVs in commercial fields and the need to improve crop NUE, further work is needed to refine approaches for translating imagery into in-season N recommendations.
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Keywords: precision ag; UAV; spatial variability; NDRE; nitrogen; on-farm; corn; multispectral sensor; site-specific management; nitrogen use efficiency precision ag; UAV; spatial variability; NDRE; nitrogen; on-farm; corn; multispectral sensor; site-specific management; nitrogen use efficiency
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MDPI and ACS Style

Thompson, L.J.; Puntel, L.A. Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn. Remote Sens. 2020, 12, 1597.

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