Abstract
The efficient use of nitrogen fertilizer is a crucial problem in modern agriculture. Fertilization has to be minimized to reduce environmental impacts but done so optimally without negatively affecting yield. In June 2017, a controlled experiment with eight different nitrogen treatments was applied to winter wheat plants and investigated with the UAV-based hyperspectral pushbroom camera Resonon Pika-L (400–1000 nm). The system, in combination with an accurate inertial measurement unit (IMU) and precise gimbal, was very stable and capable of acquiring hyperspectral imagery of high spectral and spatial quality. Additionally, in situ measurements of 48 samples (leaf area index (LAI), chlorophyll (CHL), and reflectance spectra) were taken in the field, which were equally distributed across the different nitrogen treatments. These measurements were used to predict grain yield, since the parameter itself had no direct effect on the spectral reflection of plants. Therefore, we present an indirect approach based on LAI and chlorophyll estimations from the acquired hyperspectral image data using partial least-squares regression (PLSR). The resulting models showed a reliable predictability for these parameters (R2LAI = 0.79, RMSELAI [m2m−2] = 0.18, R2CHL = 0.77, RMSECHL [µg cm−2] = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R2yield = 0.88, RMSEyield [dt ha−1] = 4.18). With this model, a pixel-wise prediction of the hyperspectral image was performed. The resulting yield estimates were validated and opposed to the different nitrogen treatments, which revealed that, above a certain amount of applied nitrogen, further fertilization does not necessarily lead to larger yield.
Highlights
Efficient food production requires a balance between the minimization of environmental damage and the maximization of yield [1]
The variety of grain yield was significantly influenced by the amount of applied nitrogen fertilizer (Figure 6)
We presented a UAV-based hyperspectral pushbroom scanner system that is capable of recording high-resolution data of high spectral and spatial quality
Summary
Efficient food production requires a balance between the minimization of environmental damage and the maximization of yield [1]. Unused N leaches into groundwater and endangers the quality of drinking water. Polluted groundwater drains into rivers and lakes, contributing to the eutrophication of these water bodies [2]. An overdose of N fertilizer, within the legal limits, results higher costs without adding value in terms of additional yield. Further possible regulations for the application of fertilizers should only have a limited negative impact on yields. Directly comparing the harvested yield resulting from different N applications, one can identify the effects of reduced fertilization. New concepts of monitoring these effects during vegetative growth enables the development of precision farming applications, especially created for efficient N fertilization [3]
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