Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/10/1597 |
| _version_ | 1797567767642112000 |
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| author | Laura J. Thompson Laila A. Puntel |
| author_facet | Laura J. Thompson Laila A. Puntel |
| author_sort | Laura J. Thompson |
| collection | DOAJ |
| description | 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<sup>−1</sup> by reducing N rates by 31 ± 6.3 kg N ha<sup>−1</sup> 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<sup>−1</sup> 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. |
| first_indexed | 2024-03-10T19:47:13Z |
| format | Article |
| id | doaj.art-ef030976aacb456f9e7948d65f3bb4b0 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T19:47:13Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ef030976aacb456f9e7948d65f3bb4b02023-11-20T00:46:31ZengMDPI AGRemote Sensing2072-42922020-05-011210159710.3390/rs12101597Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in CornLaura J. Thompson0Laila A. Puntel1University of Nebraska Extension, University of Nebraska-Lincoln, 116 W 19th Street, Falls City, NE 68355, USADepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Keim Hall, 1825 N 38th Street, Lincoln, NE 68583-0915, USADetermining 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<sup>−1</sup> by reducing N rates by 31 ± 6.3 kg N ha<sup>−1</sup> 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<sup>−1</sup> 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.https://www.mdpi.com/2072-4292/12/10/1597precision agUAVspatial variabilityNDREnitrogenon-farm |
| spellingShingle | Laura J. Thompson Laila A. Puntel Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn Remote Sensing precision ag UAV spatial variability NDRE nitrogen on-farm |
| title | Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn |
| title_full | Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn |
| title_fullStr | Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn |
| title_full_unstemmed | Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn |
| title_short | Transforming Unmanned Aerial Vehicle (UAV) and Multispectral Sensor into a Practical Decision Support System for Precision Nitrogen Management in Corn |
| title_sort | transforming unmanned aerial vehicle uav and multispectral sensor into a practical decision support system for precision nitrogen management in corn |
| topic | precision ag UAV spatial variability NDRE nitrogen on-farm |
| url | https://www.mdpi.com/2072-4292/12/10/1597 |
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