Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early-to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients R 2 of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04 m·px −1 ). Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

Abstract:The study introduces a prototype multispectral camera system for aerial estimation of above-ground biomass and nitrogen (N) content in winter wheat (Triticum aestivum L.). The system is fully programmable and designed as a lightweight payload for unmanned aircraft systems (UAS). It is based on an industrial multi-sensor camera and a customizable image processing routine. The system was tested in a split fertilized N field trial at different growth stages in between the end of stem elongation and the end of anthesis. The acquired multispectral images were processed to normalized difference vegetation index (NDVI) and red-edge inflection point (REIP) orthoimages for an analysis with simple linear regression models. The best results for the estimation of above-ground biomass were achieved with the NDVI (R 2 = 0.72-0.85, RMSE = 12.3%-17.6%), whereas N content was estimated best with the REIP (R 2 = 0.58-0.89, RMSE = 7.6%-11.7%). Moreover, NDVI and REIP predicted grain yield at a high level of accuracy (R 2 = 0.89-0.94, RMSE = 9.0%-12.1%). Grain protein content could be predicted best with the REIP (R 2 = 0.76-0.86, RMSE = 3.6%-4.7%), with the limitation of prediction inaccuracies for N-deficient canopies.

Identification of powdery mildew (Erysiphe graminis sp. tritici ) and take-all disease (Gaeumannomyces graminis sp. tritici ) in wheat (Triticum aestivum L.) by means of leaf reflectance measurements Abstract: The ability to identify diseases in an early infection stage and to accurately quantify the severity of infection is crucial in plant disease assessment and management. A greenhouse study was conducted to assess changes in leaf spectral reflectance of wheat plants during infection by powdery mildew and take-all disease to evaluate leaf reflectance measurements as a tool to identify and quantify disease severity and to discriminate between different diseases. Wheat plants were inoculated under controlled conditions in different intensities either with powdery mildew or take-all. Leaf reflectance was measured with a digital imager (Leica S1 Pro, Leica, Germany) under controlled light conditions in various wavelength ranges covering the visible and the near-infrared spectra (380 -1300 nm). Leaf scans were evaluated by means of L*a*b*-color system. Visual estimates of disease severity were made for each of the epidemics daily from the onset of visible symptoms to maximum disease severity. Reflectance within the ranges of 490 780 nm (r 2 = 0.69), 510 780 nm (r 2 = 0.74), 516 1300 nm (r 2 = 0.62) and 540 1300 nm (r 2 = 0.60) exhibited the strongest relationship with infection levels of both powdery mildew and take-all disease. Among the evaluated spectra the range of 490 780 nm showed most sensitive response to damage caused by powdery mildew and take-all infestation. The results of this study indicated that disease detection and discrimination by means of reflectance measurements may be realized by the use of specific wavelength ranges. Further studies have to be carried out, to discriminate powdery mildew and take-all infection from other plant stress factors in order to develop suitable decision support systems for site-specific fungicide application.

Variable N management is one of the most promising practices of precision agriculture to optimize nitrogen-use efficiency (NUE) and decrease environmental impact of agriculture. The objective of this study was to test the performance of fertilization in winter wheat (Triticum aestivum L.) and triticale (Triticosecale Wittm.) determined by reflection measurements of on-the-go sensors under heterogeneous field conditions. In 2004 geo-referenced yield and N fertilization data were collected in four heterogeneous fields in southern Germany. Nitrogen demand of plants was determined throughout the growing season and the corresponding amount of N fertilizer was broadcast with the N-Sensor (Yara, Germany) in real-time. The sensor uses the red edge position (720-740 nm) as an indicator of crop N status and relates this to crop N demand. The sensor algorithm is designed to stimulate plant growth in areas with low biomass and reduce risk of lodging in areas with high biomass. Fertilization was evaluated by calculating site-specific N balance maps to delineate zones with N surplus in the soil. The results revealed some general limitations of this sensor approach in areas with yield-limiting factors other than N. Nitrogen surplus above 50 kg ha 21 was calculated for subfield areas dominated by shallow soils. The results of this study indicated that sensor-based measurements can be used efficiently for variable N application in cereal crops when N is the main growth-limiting factor. However, the causes for variability must be adequately understood before sensor-based variable rate fertilization can safely be used to optimize N side-dressing in cereals.

Phenotypic plasticity of two primitive wheat species (Triticum monococcum L. and Triticum dicoccum S.) was studied in response to early chilling stress. Selection pressure differentials, gradients and plasticity costs on plant morphogenesis, growth and reserve carbohydrate consumption were estimated. Regression analysis was applied to investigate differential developmental changes and patterns between treatments. Four-day-old seedlings of T. monococcum and T. dicoccum, differing in plant stature and reserve carbohydrates, were given an early chilling temperature (4 °C for 42 day) and compared with control plants grown at 23 °C. Early chilling stress resulted in a significant increase in leaf mass ratio (LMR) and relative growth rate (RGR), a reduction in flag leaf size, total biomass, specific leaf area (SLA) and reserve carbohydrate storage at flowering, together with advanced onset of flowering. Selection pressure within the early chilling environment favoured early flowering, smaller SLA, higher LMR and lower reserve carbohydrates, suggesting the observed responses were adaptive. Furthermore, a regression of daily cumulative plant biomass derived from a crop growth simulation model (CERES-Wheat) on crop vegetation period revealed a divergent developmental pattern in early-chilled plants. Using selection pressure gradient analysis, we found similar responses among these traits, except for SLA and sucrose, indicating that these two traits have indirect effects on fitness. Thus, the total effects of SLA and reserve sucrose on relative fitness seem to be buffered via the rapid growth rate in chilled plants. While lower SLA may reduce early chilling stress effects at an individual leaf level, a higher LMR and use of reserve carbohydrates indicated that compensatory growth of chilled plants during the recovery period relied on the concerted action of altered resource allocation and reserve carbohydrate consumption. However, a significant cost of plasticity was evident only for flowering time, LMR and fructan levels in the early chilling environment. Our results demonstrate that morphological and intrinsic developmental (ontogenetic) patterns in two Triticum species respond to early chilling stress.