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Remote Sens. 2017, 9(6), 544; doi:10.3390/rs9060544

Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs)

Chair of Plant Nutrition, Department of Plant Sciences, Technical University of Munich, Freising 85354, Germany
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Academic Editors: Yoshio Inoue and Prasad S. Thenkabail
Received: 28 March 2017 / Revised: 22 May 2017 / Accepted: 28 May 2017 / Published: 31 May 2017
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Abstract

Precision phenotyping, especially the use of image analysis, allows researchers to gain information on plant properties and plant health. Aerial image detection with unmanned aerial vehicles (UAVs) provides new opportunities in precision farming and precision phenotyping. Precision farming has created a critical need for spatial data on plant density. The plant number reflects not only the final field emergence but also allows a more precise assessment of the final yield parameters. The aim of this work is to advance UAV use and image analysis as a possible high-throughput phenotyping technique. In this study, four different maize cultivars were planted in plots with different seeding systems (in rows and equidistantly spaced) and different nitrogen fertilization levels (applied at 50, 150 and 250 kg N/ha). The experimental field, encompassing 96 plots, was overflown at a 50-m height with an octocopter equipped with a 10-megapixel camera taking a picture every 5 s. Images were recorded between BBCH 13–15 (it is a scale to identify the phenological development stage of a plant which is here the 3- to 5-leaves development stage) when the color of young leaves differs from older leaves. Close correlations up to R2 = 0.89 were found between in situ and image-based counted plants adapting a decorrelation stretch contrast enhancement procedure, which enhanced color differences in the images. On average, the error between visually and digitally counted plants was ≤5%. Ground cover, as determined by analyzing green pixels, ranged between 76% and 83% at these stages. However, the correlation between ground cover and digitally counted plants was very low. The presence of weeds and blurry effects on the images represent possible errors in counting plants. In conclusion, the final field emergence of maize can rapidly be assessed and allows more precise assessment of the final yield parameters. The use of UAVs and image processing has the potential to optimize farm management and to support field experimentation for agronomic and breeding purposes. View Full-Text
Keywords: drone; farm management; high-throughput; maize cultivation; high-throughput phenomics; precision phenotyping; plant density; planting distance; unmanned aerial system (UAS) drone; farm management; high-throughput; maize cultivation; high-throughput phenomics; precision phenotyping; plant density; planting distance; unmanned aerial system (UAS)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Gnädinger, F.; Schmidhalter, U. Digital Counts of Maize Plants by Unmanned Aerial Vehicles (UAVs). Remote Sens. 2017, 9, 544.

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