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.
Highlights
Unmanned aerial vehicles (UAVs) are very promising instruments in agricultural sciences [1,2].Flights are fairly independent of weather conditions and time
Most of the images but inall some images slight resulted from the motion of the platform created bywere wind;sharp, images were still blurriness useable and did not require different of the platform created by wind; all images were still useable and did not require different of the platform created by wind; all images still useable did not the require different processing for image analysis, andhowever, a batch-mode waswere feasible
The results of this study demonstrate the capability of image processing in agricultural fields to detect plant post-emergence
Summary
Unmanned aerial vehicles (UAVs) are very promising instruments in agricultural sciences [1,2]. Flights are fairly independent of weather conditions and time. Images can be captured on cloudy days, whereas image recording in similar conditions by satellite is not possible [3]. UAVs offer time-saving and cheaper image recording, enable flexible and immediate image processing and give a survey of the health of farm systems [4]. In addition to the time-saving and cheaper image processing, the flexible handling options for aim-oriented use, e.g., variable flight height and better image resolution [5,6], UAVs represent new opportunities in the agricultural sciences, especially in precision farming and precision phenotyping. Precision farming has Remote Sens. 2017, 9, 544; doi:10.3390/rs9060544 www.mdpi.com/journal/remotesensing
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