Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Schirrmann, Michael; Giebel, Antje; Gleiniger, Franziska; Pflanz, Michael; Lentschke, Jan; Dammer, Karl‐Heinz

doi:10.3390/rs8090706

Cited by 175 publications

(179 citation statements)

References 55 publications

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“…Geipel et al [37] used SLR models based on a multispectral sensor to estimate the N content and achieved accuracies with an R 2 of 0.58-0.89 and an RMSE% of 7.6-11.7%. Schirrmann et al [42] achieved at the best R 2 value of 0.65 between the nitrogen content and the principal components of RGB image. Our results were on the same level with Liu et al [50], Geipel et al [37] and Schirrmann et al [42]; but with terrestrial approaches, even higher accuracies have been achieved [52].…”

Section: Discussionmentioning

confidence: 99%

“…Schirrmann et al [42] achieved at the best R 2 value of 0.65 between the nitrogen content and the principal components of RGB image. Our results were on the same level with Liu et al [50], Geipel et al [37] and Schirrmann et al [42]; but with terrestrial approaches, even higher accuracies have been achieved [52]. Furthermore, data from tractor-mounted Yara N-sensor has reported good correlations of R 2 0.80 with N-uptake in grass sward [54].…”

Section: Discussionmentioning

confidence: 99%

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Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

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Abstract:The timely estimation of crop biomass and nitrogen content is a crucial step in various tasks in precision agriculture, for example in fertilization optimization. Remote sensing using drones and aircrafts offers a feasible tool to carry out this task. Our objective was to develop and assess a methodology for crop biomass and nitrogen estimation, integrating spectral and 3D features that can be extracted using airborne miniaturized multispectral, hyperspectral and colour (RGB) cameras. We used the Random Forest (RF) as the estimator, and in addition Simple Linear Regression (SLR) was used to validate the consistency of the RF results. The method was assessed with empirical datasets captured of a barley field and a grass silage trial site using a hyperspectral camera based on the Fabry-Pérot interferometer (FPI) and a regular RGB camera onboard a drone and an aircraft. Agricultural reference measurements included fresh yield (FY), dry matter yield (DMY) and amount of nitrogen. In DMY estimation of barley, the Pearson Correlation Coefficient (PCC) and the normalized Root Mean Square Error (RMSE%) were at best 0.95% and 33.2%, respectively; and in the grass DMY estimation, the best results were 0.79% and 1.9%, respectively. In the nitrogen amount estimations of barley, the PCC and RMSE% were at best 0.97% and 21.6%, respectively. In the biomass estimation, the best results were obtained when integrating hyperspectral and 3D features, but the integration of RGB images and 3D features also provided results that were almost as good. In nitrogen content estimation, the hyperspectral camera gave the best results. We concluded that the integration of spectral and high spatial resolution 3D features and radiometric calibration was necessary to optimize the accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

et al. 2018

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“…Ultrasonic sensors are considered as low-cost and user-friendly approach, but are often limited by their spatial resolution and their susceptibility to wind [14]. RGB image-based detection of crop height is the most recently evolving approach for many different crops, including barley [12], maize [15], vineyards [16], wheat [17], sorghum [1], or alfalfa [18]. Especially, 3D point clouds generated from UAV-borne RGB images using SfM (structure from motion) techniques offer new options for deriving crop height information [1].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

et al. 2018

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3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV) has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage) with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage). Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R 2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm). The effect of crop development stage on the predicted crop height was found to be substantial (e.g., median deviation increased from 1% to 20% for eggplant) influencing the strength and consistency of the relationship between point cloud metrics and crop height estimates and, thus, should be further investigated. Altogether the results of the study demonstrate that point cloud generated from UAV-based RGB imagery can be used to effectively measure vegetable crop biomass in larger areas (relative error = 17.6%, 19.7%, and 15.2% for eggplant, tomato, and cabbage, respectively) with a similar accuracy as biomass prediction models based on measured crop height (relative error = 21.6, 18.8, and 15.2 for eggplant, tomato, and cabbage).

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“…The corresponding image analysis software is rapidly becoming available and reliable [4][5][6]. In comparison, aerial imaging platforms such as an unmanned aerial vehicle (UAV), have recently found application in field phenotyping [7,8]. The main advantage of a UAV is that it can cover larger areas, thus offering high-throughput field capture, albeit with a trade-off of resolution.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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Abstract:This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R 2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R 2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R 2 = 0.57), compared to MGP imagery (RMSE = 0.063, R 2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

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Monitoring Agronomic Parameters of Winter Wheat Crops with Low-Cost UAV Imagery

Cited by 175 publications

References 55 publications

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Estimating Biomass and Nitrogen Amount of Barley and Grass Using UAV and Aircraft Based Spectral and Photogrammetric 3D Features

Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

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