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- ItemMonitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery(Basel : MDPI, 2016) Schirrmann, Michael; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Lentschke, Jan; Dammer, Karl-HeinzMonitoring the dynamics in wheat crops requires near-term observations with high spatial resolution due to the complex factors influencing wheat growth variability. We studied the prospects for monitoring the biophysical parameters and nitrogen status in wheat crops with low-cost imagery acquired from unmanned aerial vehicles (UAV) over an 11 ha field. Flight missions were conducted at approximately 50 m in altitude with a commercial copter and camera system—three missions were performed between booting and maturing of the wheat plants and one mission after tillage. Ultra-high resolution orthoimages of 1.2 cm·px−1 and surface models were generated for each mission from the standard red, green and blue (RGB) aerial images. The image variables were extracted from image tone and surface models, e.g., RGB ratios, crop coverage and plant height. During each mission, 20 plots within the wheat canopy with 1 × 1 m2 sample support were selected in the field, and the leaf area index, plant height, fresh and dry biomass and nitrogen concentrations were measured. From the generated UAV imagery, we were able to follow the changes in early senescence at the individual plant level in the wheat crops. Changes in the pattern of the wheat canopy varied drastically from one mission to the next, which supported the need for instantaneous observations, as delivered by UAV imagery. The correlations between the biophysical parameters and image variables were highly significant during each mission, and the regression models calculated with the principal components of the image variables yielded R2 values between 0.70 and 0.97. In contrast, the models of the nitrogen concentrations yielded low R2 values with the best model obtained at flowering (R2 = 0.65). The nitrogen nutrition index was calculated with an accuracy of 0.10 to 0.11 NNI for each mission. For all models, information about the surface models and image tone was important. We conclude that low-cost RGB UAV imagery will strongly aid farmers in observing biophysical characteristics, but it is limited for observing the nitrogen status within wheat crops.
- ItemComparison of Calibration Approaches in Laser-Induced Breakdown Spectroscopy for Proximal Soil Sensing in Precision Agriculture(Basel : MDPI, 2019) Riebe, Daniel; Erler, Alexander; Brinkmann, Pia; Beitz, Toralf; Löhmannsröben, Hans-Gerd; Gebbers, RobinThe lack of soil data, which are relevant, reliable, affordable, immediately available, and sufficiently detailed, is still a significant challenge in precision agriculture. A promising technology for the spatial assessment of the distribution of chemical elements within fields, without sample preparation is laser-induced breakdown spectroscopy (LIBS). Its advantages are contrasted by a strong matrix dependence of the LIBS signal which necessitates careful data evaluation. In this work, different calibration approaches for soil LIBS data are presented. The data were obtained from 139 soil samples collected on two neighboring agricultural fields in a quaternary landscape of northeast Germany with very variable soils. Reference analysis was carried out by inductively coupled plasma optical emission spectroscopy after wet digestion. The major nutrients Ca and Mg and the minor nutrient Fe were investigated. Three calibration strategies were compared. The first method was based on univariate calibration by standard addition using just one soil sample and applying the derived calibration model to the LIBS data of both fields. The second univariate model derived the calibration from the reference analytics of all samples from one field. The prediction is validated by LIBS data of the second field. The third method is a multivariate calibration approach based on partial least squares regression (PLSR). The LIBS spectra of the first field are used for training. Validation was carried out by 20-fold cross-validation using the LIBS data of the first field and independently on the second field data. The second univariate method yielded better calibration and prediction results compared to the first method, since matrix effects were better accounted for. PLSR did not strongly improve the prediction in comparison to the second univariate method.
- ItemEvaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing(Basel : MDPI AG, 2019) Vogel, Sebastian; Gebbers, Robin; Oertel, Marcel; Kramer, EckartOn a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity.
- ItemEvaluation of different sensing approaches concerning to nondestructive estimation of leaf area index (LAI) for winter wheat(Auckland : Massey University, 2017) Tavakoli, H.; Mohtasebi, S.S.; Alimardani, R.; Gebbers, R.Different approaches of non-destructive estimation of the LAI in winter wheat were compared. Plant height had weak relation with the LAI, while estimated biomass showed high logarithmic relationship (R2=0.839). NDRE and REIP were logarithmically well related to the LAI (R2=0.726 and 0.779 respectively). Saturation effect of NDRE and REIP was less than NDVI. Some RGB-based indices also showed good potential to estimate the LAI. Among the indices, Gm, GMB, RMB, and NRMB were better related to the LAI. The results indicated that digital cameras can be used as an affordable and simple approach for assessment of the LAI of crops.
- ItemMeasuring device for air speed in macroporous media and its application inside apple storage bins(Basel : MDPI, 2018) Geyer, Martin; Praeger, Ulrike; Truppel, Ingo; Scaar, Holger; Neuwald, Daniel A.; Jedermann, Reiner; Gottschalk, KlausIn cold storage facilities of fruit and vegetables, airflow is necessary for heat removal. The design of storage facilities influences the air speed in the surrounding of the product. Therefore, knowledge about airflow next to the product is important to plan the layout of cold stores adapted to the requirements of the products. A new sensing device (ASL, Air speed logger) is developed for omnidirectional measurement of air speed between fruit or vegetables inside storage bins or in bulk. It consists of four interconnected plastic spheres with 80 mm diameter each, adapted to the size of apple fruit. In the free space between the spheres, silicon diodes are fixed for the airflow measurement based on a calorimetric principle. Battery and data logger are mounted inside the spheres. The device is calibrated in a wind tunnel in a measuring range of 0–1.3 m/s. Air speed measurements in fruit bulks on laboratory scale and in an industrial fruit store show air speeds in gaps between fruit with high stability at different airflow levels. Several devices can be placed between stored products for determination of the air speed distribution inside bulks or bin stacks in a storage room.
- ItemThe Use of a Pressure-Indicating Film to Determine the Effect of Liner Type on the Measured Teat Load Caused by a Collapsing Liner(Basel : MDPI, 2017-4-13) Demba, Susanne; Paul, Viktoria; Ammon, Christian; Rose-Meierhöfer, SandraDuring milking the teat cup liner is the interface between the teat of a dairy cow and the milking system, so it should be very well adapted to the teat. Therefore, the aim of the present study was to determine the effect of liner type on the directly measuring teat load caused by a collapsing liner with a pressure-indicating film. The Extreme Low pressure-indicating film was used to detect the effect of six different liners on teat load. For each liner, six positions in the teat cup were specified, and six repetitions were performed for each position with a new piece of film each time. Analysis of variance was performed to detect differences between the six liners, the positions within a liner, and the measuring areas. The pressure applied to the teat by a liner depends on the technical characteristics of the liner, especially the shape of the barrel, and for all tested liners, a higher teat load was found at the teat end. In conclusion, with the help of pressure-indicating film, it is possible to determine the different effects of liner type by directly measuring teat load due to liner collapse. © 2017 by the authors. Licensee MDPI, Basel, Switzerland.
- ItemWeed Mapping with UAS Imagery and a Bag of Visual Words Based Image Classifier(Basel : MDPI, 2018-9-24) Pflanz, Michael; Nordmeyer, Henning; Schirrmann, MichaelWeed detection with aerial images is a great challenge to generate field maps for site-specific plant protection application. The requirements might be met with low altitude flights of unmanned aerial vehicles (UAV), to provide adequate ground resolutions for differentiating even single weeds accurately. The following study proposed and tested an image classifier based on a Bag of Visual Words (BoVW) framework for mapping weed species, using a small unmanned aircraft system (UAS) with a commercial camera on board, at low flying altitudes. The image classifier was trained with support vector machines after building a visual dictionary of local features from many collected UAS images. A window-based processing of the models was used for mapping the weed occurrences in the UAS imagery. The UAS flight campaign was carried out over a weed infested wheat field, and images were acquired between a 1 and 6 m flight altitude. From the UAS images, 25,452 weed plants were annotated on species level, along with wheat and soil as background classes for training and validation of the models. The results showed that the BoVW model allowed the discrimination of single plants with high accuracy for Matricaria recutita L. (88.60%), Papaver rhoeas L. (89.08%), Viola arvensis M. (87.93%), and winter wheat (94.09%), within the generated maps. Regarding site specific weed control, the classified UAS images would enable the selection of the right herbicide based on the distribution of the predicted weed species. © 2018 by the authors.
- ItemAnalyzing temporal and spatial characteristics of crop parameters using Sentinel-1 backscatter data(Basel : MDPI AG, 2019) Harfenmeister, Katharina; Spengler, Daniel; Weltzien, CorneliaThe knowledge about heterogeneity on agricultural fields is essential for a sustainable and effective field management. This study investigates the performance of Synthetic Aperture Radar (SAR) data of the Sentinel-1 satellites to detect variability between and within agricultural fields in two test sites in Germany. For this purpose, the temporal profiles of the SAR backscatter in VH and VV polarization as well as their ratio VH/VV of multiple wheat and barley fields are illustrated and interpreted considering differences between acquisition settings, years, crop types and fields. Within-field variability is examined by comparing the SAR backscatter with several crop parameters measured at multiple points in 2017 and 2018. Structural changes, particularly before and after heading, as well as moisture and crop cover differences are expressed in the backscatter development. Furthermore, the crop parameters wet and dry biomass, absolute and relative vegetation water content, leaf area index (LAI) and plant height are related to SAR backscatter parameters using linear and exponential as well as multiple regression. The regression performance is evaluated using the coefficient of determination (R2) and the root mean square error (RMSE) and is strongly dependent on the phenological growth stage. Wheat shows R2 values around 0.7 for VV backscatter and multiple regression and most crop parameters before heading. Single fields even reach R2 values above 0.9 for VV backscatter and for multiple regression related to plant height with RMSE values around 10 cm. The formulation of clear rules remains challenging, as there are multiple influencing factors and uncertainties and a lack of conformity. © 2019 by the authors.
- ItemTerahertz spectroscopy for proximal soil sensing: An approach to particle size analysis(Basel : MDPI, 2017) Dworak, Volker; Mahns, Benjamin; Selbeck, Jörn; Gebbers, Robin; Weltzien, CorneliaSpatially resolved soil parameters are some of the most important pieces of information for precision agriculture. These parameters, especially the particle size distribution (texture), are costly to measure by conventional laboratory methods, and thus, in situ assessment has become the focus of a new discipline called proximal soil sensing. Terahertz (THz) radiation is a promising method for nondestructive in situ measurements. The THz frequency range from 258 gigahertz (GHz) to 350 GHz provides a good compromise between soil penetration and the interaction of the electromagnetic waves with soil compounds. In particular, soil physical parameters influence THz measurements. This paper presents investigations of the spectral transmission signals from samples of different particle size fractions relevant for soil characterization. The sample thickness ranged from 5 to 17 mm. The transmission of THz waves was affected by the main mineral particle fractions, sand, silt and clay. The resulting signal changes systematically according to particle sizes larger than half the wavelength. It can be concluded that THz spectroscopic measurements provide information about soil texture and penetrate samples with thicknesses in the cm range.
- ItemNew Tropical Peatland Gas and Particulate Emissions Factors Indicate 2015 Indonesian Fires Released Far More Particulate Matter (but Less Methane) than Current Inventories Imply(Basel : MDPI, 2018-3-21) Wooster, Martin J.; Gaveau, David L.A.; Salim, Mohammad A.; Zhang, Tianran; Xu, Weidong; Green, David C.; Huijnen, Vincent; Murdiyarso, Daniel; Gunawan, Dodo; Borchard, Nils; Schirrmann, Michael; Main, Bruce; Sepriando, AlponDeforestation and draining of the peatlands in equatorial SE Asia has greatly increased their flammability, and in September-October 2015 a strong El Niño-related drought led to further drying and to widespread burning across parts of Indonesia, primarily on Kalimantan and Sumatra. These fires resulted in some of the worst sustained outdoor air pollution ever recorded, with atmospheric particulate matter (PM) concentrations exceeding those considered "extremely hazardous to health" by up to an order of magnitude. Here we report unique in situ air quality data and tropical peatland fire emissions factors (EFs) for key carbonaceous trace gases (CO2, CH4 and CO) and PM2.5 and black carbon (BC) particulates, based on measurements conducted on Kalimantan at the height of the 2015 fires, both at locations of "pure" sub-surface peat burning and spreading vegetation fires atop burning peat. PM2.5 are the most significant smoke constituent in terms of human health impacts, and we find in situ PM2.5 emissions factors for pure peat burning to be 17.8 to 22.3 g·kg-1, and for spreading vegetation fires atop burning peat 44 to 61 g·kg-1, both far higher than past laboratory burning of tropical peat has suggested. The latter are some of the highest PM2.5 emissions factors measured worldwide. Using our peatland CO2, CH4 and CO emissions factors (1779 ± 55 g·kg-1, 238 ± 36 g·kg-1, and 7.8 ± 2.3 g·kg-1 respectively) alongside in situ measured peat carbon content (610 ± 47 g-C·kg-1) we provide a new 358 Tg (± 30%) fuel consumption estimate for the 2015 Indonesian fires, which is less than that provided by the GFEDv4.1s and GFASv1.2 global fire emissions inventories by 23% and 34% respectively, and which due to our lower EFCH4 produces far less (~3×) methane. However, our mean in situ derived EFPM2.5 for these extreme tropical peatland fires (28 ± 6 g·kg-1) is far higher than current emissions inventories assume, resulting in our total PM2.5 emissions estimate (9.1 ± 3.5 Tg) being many times higher than GFEDv4.1s, GFASv1.2 and FINNv2, despite our lower fuel consumption. We find that two thirds of the emitted PM2.5 come from Kalimantan, one third from Sumatra, and 95% from burning peatlands. Using new geostationary fire radiative power (FRP) data we map the fire emissions' spatio-temporal variations in far greater detail than ever before (hourly, 0.05°), identifying a tropical peatland fire diurnal cycle twice as wide as in neighboring non-peat areas and peaking much later in the day. Our data show that a combination of greatly elevated PM2.5 emissions factors, large areas of simultaneous, long-duration burning, and very high peat fuel consumption per unit area made these Sept to Oct tropical peatland fires the greatest wildfire source of particulate matter globally in 2015, furthering evidence for a regional atmospheric pollution impact whose particulate matter component in particular led to millions of citizens being exposed to extremely poor levels of air quality for substantial periods. © 2018 by the authors.