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 (R²_LAI = 0.79, RMSE_{LAI [m²m⁻²]} = 0.18, R²_CHL = 0.77, RMSE_{CHL [µg cm⁻²]} = 7.02). The LAI and CHL predictions were used afterwards to calibrate a multiple linear regression model to estimate grain yield (R²_yield = 0.88, RMSE_{yield [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.

Groundwater pollution with nitrate is a big challenge for drinking water abstraction in regions with intensive agricultural land-use, specifically with high livestock densities on sandy soils in humid climates. Karst aquifers with high water flow velocities are extremely vulnerable to this problem. To cope with this situation, a field trial with an installation of ceramic suction cups under a randomised block design with a typical north-German cropping sequence of silage maize–winter wheat–winter barley was established in a karst water protection zone. Over three years, reduced nitrogen (N) application rates and N type (mineral or combined organic + mineral fertilisation) were tested for their effects on crop yields and leachate water quality below the root zone. Results showed no significant reductions in crop yields with 10/20% reduced N rates for cereals/maize and only slight reductions in cereal protein content. Nitrate concentration from adapted N rates was significantly lower in treatments with an application of organic fertilisers (−7.74 mg NO₃-N l⁻¹) with greatest potential after cultivation of maize; in only mineral fertilised plots the effect was smaller (−3.80 mg NO₃-N l⁻¹). Cumulative leaching losses were positively correlated with post-harvest soil mineral nitrogen content but even in unfertilised control plots losses >50 kg N ha⁻¹ were observed in some crop-years. Reduced N rates led to decreased leaching losses of 14% (6.3 kg N ha⁻¹ a⁻¹) with mineral and 29% (20.1 kg N ha⁻¹ a⁻¹) with organic + mineral fertilisation on average overall cops and years. The presented study revealed the general potential of adapted fertilisation strategies with moderately reduced N applications (−10/−20%) to increase leachate water quality without affecting significantly crop yields. However, regionally typical after-effects from yearlong high N surpluses in livestock intensive farming systems are a limiting factor.

Pumpkins are economically and nutritionally valuable vegetables with increasing popularity and acreage across Europe. Successful commercialization, however, require detailed pre-harvest information about number and weight of the fruits. To get a non-destructive and cost-effective yield estimation, we developed an image processing methodology for high-resolution RGB data from Unmanned aerial vehicle (UAV) and applied this on a Hokkaido pumpkin farmer’s field in North-western Germany. The methodology was implemented in the programming language Python and comprised several steps, including image pre-processing, pixel-based image classification, classification post-processing for single fruit detection, and fruit size and weight quantification. To derive the weight from two-dimensional imagery, we calculated elliptical spheroids from lengths of diameters and heights. The performance of this processes was evaluated by comparison with manually harvested ground-truth samples and cross-checked for misclassification from randomly selected test objects. Errors in classification and fruit geometry could be successfully reduced based on the described processing steps. Additionally, different lighting conditions, as well as shadows, in the image data could be compensated by the proposed methodology. The results revealed a satisfactory detection of 95% (error rate of 5%) from the field sample, as well as a reliable volume and weight estimation with Pearson’s correlation coefficients of 0.83 and 0.84, respectively, from the described ellipsoid approach. The yield was estimated with 1.51 kg m⁻² corresponding to an average individual fruit weight of 1100 g and an average number of 1.37 pumpkins per m². Moreover, spatial distribution of aggregated fruit densities and weights were calculated to assess in-field optimization potential for agronomic management as demonstrated between a shaded edge compared to the rest of the field. The proposed approach provides the Hokkaido producer useful information for more targeted pre-harvest marketing strategies, since most food retailers request homogeneous lots within prescribed size or weight classes.