Crop Surface Models Research Articles

Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras

BackgroundUnmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management.MethodIn this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial–temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated. Random forest (RF) model for AGB estimation as well as the PB was then developed. Furthermore, variable importance and sensitivity analysis of UAV variables were performed to study the potential of improving model robustness and prediction accuracies.ResultsIt was found that the canopy height extracted from the CSM (Hcsm) exhibited a high correlation with the ground-measured canopy height, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. We also observed that several VIs were highly correlated with AGB, and the modified normalized difference spectral index extracted from the multispectral image achieved the highest correlation. RF model with fusing RGB and multispectral image data substantially improved the prediction results of AGB and PB with the prediction of root mean square error (RMSEP) reduced by 8.33–16.00%. The best prediction results for AGB and PB were achieved with the coefficient of determination (r2), the RMSEP and relative RMSE (RRMSE) of 0.90, 0.21 kg/m2 and 14.05%, and 0.68, 0.10 kg/m2 and 12.11%, respectively. In addition, the result confirmed that the sensitivity analysis could simplify the prediction model without reducing the prediction accuracy.ConclusionThese findings demonstrate the feasibility of applying lightweight UAV with dual image-frame snapshot cameras for rice biomass estimation, and its potential for high throughput analysis of plant growth-related traits in precision agriculture as well as the advanced breeding program.

Dynamic plant height QTL revealed in maize through remote sensing phenotyping using a high-throughput unmanned aerial vehicle (UAV)

Plant height (PH) is a key factor in maize (Zea mays L.) yield, biomass, and plant architecture. We investigated the PH of diverse maize inbred lines (117 temperate lines, 135 tropical lines) at four growth stages using unmanned aerial vehicle high-throughput phenotypic platforms (UAV-HTPPs). We extracted PH data using an automated pipeline based on crop surface models and orthomosaic model. The correlation between UAV and manually measured PH data reached 0.95. Under temperate field conditions, temperate maize lines grew faster than tropical maize lines at early growth stages, but tropical lines grew faster at later growth stages and ultimately became taller than temperate lines. A genome-wide association study identified 68 unique quantitative trait loci (QTLs) for seven PH-related traits, and 35% of the QTLs coincided with those previously reported to control PH. Generally, different QTLs controlled PH at different growth stages, but eight QTLs simultaneously controlled PH and growth rate at multiple growth stages. Based on gene annotations and expression profiles, we identified candidate genes controlling PH. The PH data collected by the UAV-HTPPs were credible and the genetic mapping power was high. Therefore, UAV-HTPPs have great potential for use in studies on PH.

Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices

When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow-band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix-based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R2: 0.59–0.78, root mean square error (RMSE): 1.22–1.59 t/ha, and mean absolute error (MAE): 1.03–1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8–14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage.

Multi-Temporal Vineyard Monitoring through UAV-Based RGB Imagery

This study aimed to characterize vineyard vegetation thorough multi-temporal monitoring using a commercial low-cost rotary-wing unmanned aerial vehicle (UAV) equipped with a consumer-grade red/green/blue (RGB) sensor. Ground-truth data and UAV-based imagery were acquired on nine distinct dates, covering the most significant vegetative growing cycle until harvesting season, over two selected vineyard plots. The acquired UAV-based imagery underwent photogrammetric processing resulting, per flight, in an orthophoto mosaic, used for vegetation estimation. Digital elevation models were used to compute crop surface models. By filtering vegetation within a given height-range, it was possible to separate grapevine vegetation from other vegetation present in a specific vineyard plot, enabling the estimation of grapevine area and volume. The results showed high accuracy in grapevine detection (94.40%) and low error in grapevine volume estimation (root mean square error of 0.13 m and correlation coefficient of 0.78 for height estimation). The accuracy assessment showed that the proposed method based on UAV-based RGB imagery is effective and has potential to become an operational technique. The proposed method also allows the estimation of grapevine areas that can potentially benefit from canopy management operations.

Replacing Manual Rising Plate Meter Measurements with Low-cost UAV-Derived Sward Height Data in Grasslands for Spatial Monitoring

Non-destructive methods to derive spatial information on the development of forage mass are of key importance in managed grasslands. Established methods are rising plate meter (RPM) and rapid pasture meter, which both require in-field work, are rather time consuming, and do not provide spatially continuous data. Therefore, the overall objective of this study is to investigate low-cost unmanned aerial vehicle (UAV)-based RGB image acquisition for grassland monitoring. The idea of this paper is to transfer the successfully introduced approach of crop surface models (CSMs) for ultrahigh resolution analysis of plant height to managed grasslands. The study area is the Rengen Long-term Grassland Experiment, Germany, which is a two-cut experiment and was established in 1941. We conducted RPM and UAV-based data acquisition over six growth periods in 2014, 2015, and 2016. In 3 years, 26 RPM and 46 UAV campaigns were conducted under varying weather conditions (cloudy/sunny). The UAV-based RGB imagery was photogrammetrically processed with Structure from Motion and Multi-view Stereopsis techniques, producing multi-temporal CSMs for grassland sward height analysis. The regression analysis of UAV-derived sward height (CSM-SH) against RPM-measured sward height (RPM-SH) resulted in R2 of 0.91, 0.87, and 0.83 for 2014, 2015, and 2016, respectively. The pooling of the data for all 3 years resulted in an R2 of 0.86. These findings prove the successful transfer of the CSM approach for grassland monitoring and the potential of UAV-based monitoring to replace manual or in-field measurements with RPM or rapid pasture meter.

A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera

Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.

Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery

Non-destructive monitoring of crop development is of key interest for agronomy and crop breeding. Crop Surface Models (CSMs) representing the absolute height of the plant canopy are a tool for this. In this study, fresh and dry barley biomass per plot are estimated from CSM-derived plot-wise plant heights. The CSMs are generated in a semi-automated manner using Structure-from-Motion (SfM)/Multi-View-Stereo (MVS) software from oblique stereo RGB images. The images were acquired automatedly from consumer grade smart cameras mounted at an elevated position on a lifting hoist. Fresh and dry biomass were measured destructively at four dates each in 2014 and 2015. We used exponential and simple linear regression based on different calibration/validation splits. Coefficients of determination R 2 between 0.55 and 0.79 and root mean square errors (RMSE) between 97 and 234 g/m2 are reached for the validation of predicted vs. observed dry biomass, while Willmott’s refined index of model performance d r ranges between 0.59 and 0.77. For fresh biomass, R 2 values between 0.34 and 0.61 are reached, with root mean square errors (RMSEs) between 312 and 785 g/m2 and d r between 0.39 and 0.66. We therefore established the possibility of using this novel low-cost system to estimate barley dry biomass over time.

Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine

Estimating crop biophysical and biochemical parameters with high accuracy at low-cost is imperative for high-throughput phenotyping in precision agriculture. Although fusion of data from multiple sensors is a common application in remote sensing, less is known on the contribution of low-cost RGB, multispectral and thermal sensors to rapid crop phenotyping. This is due to the fact that (1) simultaneous collection of multi-sensor data using satellites are rare and (2) multi-sensor data collected during a single flight have not been accessible until recent developments in Unmanned Aerial Systems (UASs) and UAS-friendly sensors that allow efficient information fusion. The objective of this study was to evaluate the power of high spatial resolution RGB, multispectral and thermal data fusion to estimate soybean (Glycine max) biochemical parameters including chlorophyll content and nitrogen concentration, and biophysical parameters including Leaf Area Index (LAI), above ground fresh and dry biomass. Multiple low-cost sensors integrated on UASs were used to collect RGB, multispectral, and thermal images throughout the growing season at a site established near Columbia, Missouri, USA. From these images, vegetation indices were extracted, a Crop Surface Model (CSM) was advanced, and a model to extract the vegetation fraction was developed. Then, spectral indices/features were combined to model and predict crop biophysical and biochemical parameters using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Extreme Learning Machine based Regression (ELR) techniques. Results showed that: (1) For biochemical variable estimation, multispectral and thermal data fusion provided the best estimate for nitrogen concentration and chlorophyll (Chl) a content (RMSE of 9.9% and 17.1%, respectively) and RGB color information based indices and multispectral data fusion exhibited the largest RMSE 22.6%; the highest accuracy for Chl a+b content estimation was obtained by fusion of information from all three sensors with an RMSE of 11.6%. (2) Among the plant biophysical variables, LAI was best predicted by RGB and thermal data fusion while multispectral and thermal data fusion was found to be best for biomass estimation. (3) For estimation of the above mentioned plant traits of soybean from multi-sensor data fusion, ELR yields promising results compared to PLSR and SVR in this study. This research indicates that fusion of low-cost multiple sensor data within a machine learning framework can provide relatively accurate estimation of plant traits and provide valuable insight for high spatial precision in agriculture and plant stress assessment.

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.

Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS). Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM) and multi-view stereopsis (MVS) techniques. Dense point clouds were used to generate a digital surface model (DSM) and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM) was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM). UAS-derived plant height (PH) and field measured PH in Cambridge were strongly correlated with R2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM) generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV) at capsule formation stage (R2 0.74), with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage.

Direct Georeferencing for Portable Mapping Systems: In the Air and on the Ground

During the last few years, the acquisition of geometric information about objects by using a moving sensor platform has gained increasing popularity in the surveying community. A large number of companies offer vehicle-based mobile mapping systems, which usually contain a fast profile laser scanner, a high-precision inertial measurement unit, and geodetic global navigation satellite system (GNSS) receivers to directly georeference the laser scans and build a consistent point cloud of the object. In contrast, there is a growing number of companies offering small and lightweight unmanned aerial vehicles (UAVs), which automatically fly over the area of interest and create dense point clouds of the environment by using camera images and photogrammetric-processing software. In this case, the georeferencing is usually realized by ground control points. This contribution summarizes activities performed by the authors to bring both fields closer together by developing a small (11 × 10 × 5 cm) and lightweight (240-g) direct-georeferencing unit, which is able to provide accurate position (<5 cm) and orientation (<1°) information in real time. The authors describe the development of the sensor unit and the sensor-fusion algorithms, address the topics of calibration and accuracy evaluation, and provide an overview of different applications in which the unit has been already used. These include the high-resolution acquisition of crop-surface models by using UAV-based imagery, the direct georeferencing of an autonomously flying robot with a laser scanner and multiple cameras, and the generation of laser point clouds using a human-carried mobile mapping system.

Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds

ABSTRACTThe use of unmanned aerial systems (UAS) as remote-sensing platforms has tremendous potential for obtaining detailed, site-specific descriptions of crop features, which would be very useful for precision agriculture. In sugarcane plantations, for example, cane height can be an indicator of yield and other parameters because it is highly influenced by the soil, total sugar content, leaf nitrogen content, temperature and light intensity. This article describes the generation of crop surface models (CSMs) from high-resolution images that were obtained using a UAS to estimate sugarcane height. Using a UAS with an on-board RGB camera, we created densified three-dimensional point clouds of the study area in two different flight line directions (North/South and East/West) using structure from motion (SfM) with multi-view stereo (MVS). Then, the digital surface model (DSM) and digital terrain model (DTM) were extracted and used to create CSMs. Maps of sugarcane height were created based on this information. We investigated the influences of different flight line directions (N/S and E/W) on sugarcane height estimations and their accuracy by comparing our maps with ground references. From the validation conducted using both flight lines, the average heights were closer to the field-verified data. The resulting maps showed differences in sugarcane height that were confirmed by field measurements. This method has potential for future use by sugarcane-related industries, researchers and farmers to estimate average crop height.

Toward an automated low-cost three-dimensional crop surface monitoring system using oblique stereo imagery from consumer-grade smart cameras

Crop surface models (CSMs) representing plant height above ground level are a useful tool for monitoring in-field crop growth variability and enabling precision agriculture applications. A semiautomated system for generating CSMs was implemented. It combines an Android application running on a set of smart cameras for image acquisition and transmission and a set of Python scripts automating the structure-from-motion (SfM) software package Agisoft Photoscan and ArcGIS. Only ground-control-point (GCP) marking was performed manually. This system was set up on a barley field experiment with nine different barley cultivars in the growing period of 2014. Images were acquired three times a day for a period of two months. CSMs were successfully generated for 95 out of 98 acquisitions between May 2 and June 30. The best linear regressions of the CSM-derived plot-wise averaged plant-heights compared to manual plant height measurements taken at four dates resulted in a coefficient of determination R2 of 0.87 and a root-mean-square error (RMSE) of 0.08 m, with Willmott’s refined index of model performance dr equaling 0.78. In total, 103 mean plot heights were used in the regression based on the noon acquisition time. The presented system succeeded in semiautomatedly monitoring crop height on a plot scale to field scale.

Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging

ABSTRACTBiomass is one of the most important parameters in order for the farmer to choose the best canopy management within the field and it can be estimated using plant canopy height. In combination with a non-vegetation ground model, plant height can be obtained by quantifying the height of a canopy using crop surface models (CSMs). A modified Mikrokopter Okto unmanned aerial vehicle (UAV) acquired high-resolution multispectral images (4 cm) and a processing chain was developed to construct a 3D digital surface model (DSM) for the creation of precise digital terrain models (DTMs) based on Structure from Motion (SfM) computer vision algorithms. The DTM was then subtracted from the DSM to obtain a canopy height model (CHM) of a vineyard. The results show a good separation of ground pixels from vine rows, but their elevations were not quite in accordance with the actual height of the vines due to a smoothing effect of the reconstructed CHM. A further comparison between CHM and a vigour map obtained from normalized difference vegetation index (NDVI) values showed a good correlation. A preliminary assessment of biomass volume was made using the average canopy height and vine row width for three different homogeneous classes. This is a preliminary study on how a 3D model developed by UAV images can be useful for a simple and prompt biomass evaluation.

POTENTIAL OF MULTITEMPORAL TANDEM-X DERIVED CROP SURFACE MODELS FOR MAIZE GROWTH MONITORING

In this study, first results of retrieving plant heights of maize fields from multitemporal TanDEM-X images are shown. Three TanDEM-X dual polarization spotlight acquisitions were taken over a rural area in Germany in the growing season 2014. By interferometric processing, digital terrain models (DTM) were derived for each date with 5m resolution. From the data of the first acquisition (June 1st) taken before planting, a DTM of the bare ground is generated. The data of the following acquisition dates (July 15th, July 26th) are used to establish crop surface models (CSM). A CSM represents the crop surface of a whole field in a high resolution. By subtracting the DTM of the ground from each CSM, the actual plant height is calculated. Within these data sets 30 maize fields in the area of interest could be detected and verified by external land use data. Besides the spaceborne measurements, one of the maize fields was intensively investigated using terrestrial laser scanning (TLS), which was carried out at the same dates as the predicted TanDEM-X acquisitions. Visual inspection of the derived plant heights, and accordance of the individually processed polarisations over the maize fields, demonstrate the feasibility of the proposed method. Unfortunately, the infield variability of the intensively monitored field could not be successfully captured in the TanDEM-X derived plant heights and merely the general trend is visible. Nevertheless, the study shows the potential of the TanDEM-X constellation for maize height monitoring on field level.

POTENTIAL OF MULTITEMPORAL TANDEM-X DERIVED CROP SURFACE MODELS FOR MAIZE GROWTH MONITORING

Abstract. In this study, first results of retrieving plant heights of maize fields from multitemporal TanDEM-X images are shown. Three TanDEM-X dual polarization spotlight acquisitions were taken over a rural area in Germany in the growing season 2014. By interferometric processing, digital terrain models (DTM) were derived for each date with 5m resolution. From the data of the first acquisition (June 1st) taken before planting, a DTM of the bare ground is generated. The data of the following acquisition dates (July 15th, July 26th) are used to establish crop surface models (CSM). A CSM represents the crop surface of a whole field in a high resolution. By subtracting the DTM of the ground from each CSM, the actual plant height is calculated. Within these data sets 30 maize fields in the area of interest could be detected and verified by external land use data. Besides the spaceborne measurements, one of the maize fields was intensively investigated using terrestrial laser scanning (TLS), which was carried out at the same dates as the predicted TanDEM-X acquisitions. Visual inspection of the derived plant heights, and accordance of the individually processed polarisations over the maize fields, demonstrate the feasibility of the proposed method. Unfortunately, the infield variability of the intensively monitored field could not be successfully captured in the TanDEM-X derived plant heights and merely the general trend is visible. Nevertheless, the study shows the potential of the TanDEM-X constellation for maize height monitoring on field level.

EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

Abstract. Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY

Abstract. In the context of an increasing world population, the demand for agricultural crops is continuously rising. Especially rice plays a key role in food security, not only in Asia. To increase crop production of rice, either productivity of plants has to be improved or new cultivation areas have to be found. In this context, our study investigated crop growth of paddy rice (Oryza Sativa J.) in Germany. An experimental field in the vegetation period of 2014 with two nitrogen treatments was conducted using remote sensing methods. The research project focussed on two main aspects: (1) the potential of UAV-based and hyperspectral remote sensing methods to monitor selected growth parameters at different phenological stages; (2) the potential of paddy rice cultivation under the present climate condition in western Germany. We applied a low-cost UAV-system (Unmanned Aerial Vehicle) to generate high resolution Crop Surface Models (CSM). These were compared with hyperspectral in-field measurements and directly measured agronomic parameters (fresh and dry aboveground biomass (AGB), leaf-area-index (LAI) and plant nitrogen concentration (PNC)). For all acquisition dates we could determine single in-field structures in the CSM (e.g. distribution of hills) and different growth characteristics between the nitrogen treatments. Especially in the second half of the growing season, the plants with higher nitrogen availability were about 25 – 30 % larger. The plant height in the CSM correlates particularly with fresh AGB and the LAI (R2 &gt; 0.8). Thus, the conducted methods for plant growth monitoring can be a contribution for precision agriculture approaches.

NON-DESTRUCTIVE MONITORING OF RICE BY HYPERSPECTRAL IN-FIELD SPECTROMETRY AND UAV-BASED REMOTE SENSING: CASE STUDY OF FIELD-GROWN RICE IN NORTH RHINE-WESTPHALIA, GERMANY

In the context of an increasing world population, the demand for agricultural crops is continuously rising. Especially rice plays a key role in food security, not only in Asia. To increase crop production of rice, either productivity of plants has to be improved or new cultivation areas have to be found. In this context, our study investigated crop growth of paddy rice (Oryza Sativa J.) in Germany. An experimental field in the vegetation period of 2014 with two nitrogen treatments was conducted using remote sensing methods. The research project focussed on two main aspects: (1) the potential of UAV-based and hyperspectral remote sensing methods to monitor selected growth parameters at different phenological stages; (2) the potential of paddy rice cultivation under the present climate condition in western Germany. We applied a low-cost UAV-system (Unmanned Aerial Vehicle) to generate high resolution Crop Surface Models (CSM). These were compared with hyperspectral in-field measurements and directly measured agronomic parameters (fresh and dry aboveground biomass (AGB), leaf-area-index (LAI) and plant nitrogen concentration (PNC)). For all acquisition dates we could determine single in-field structures in the CSM (e.g. distribution of hills) and different growth characteristics between the nitrogen treatments. Especially in the second half of the growing season, the plants with higher nitrogen availability were about 25 – 30 % larger. The plant height in the CSM correlates particularly with fresh AGB and the LAI (R&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; &amp;gt; 0.8). Thus, the conducted methods for plant growth monitoring can be a contribution for precision agriculture approaches.