A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation

Geipel, Jakob; Link, Johanna; Wirwahn, Jan A.; Claupein, Wilhelm

doi:10.3390/agriculture6010004

Cited by 65 publications

(59 citation statements)

References 35 publications

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“…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: Discussionsupporting

confidence: 78%

“…These results are comparable to our results for the barley DMY. In studies with spectrally uncalibrated RGB values and 3D features, R 2 values of 0.74 have been reported for the corn grain yield estimation [37] and 0.88 for the maize biomass estimation [32], which indicated lower correlations than our results using the RGB data for the barley FMY estimation (PCC = 0.95, RMSE% = 34.74%).…”

Section: Discussioncontrasting

confidence: 53%

“…Liu et al [50] used several different algorithms to estimate the nitrogen content (N%) of winter wheat based on multispectral data and achieved the best results with an R 2 of 0.79 and an RMSE% of 11.56% with the RF. 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.…”

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: Discussionsupporting

confidence: 78%

Section: Discussioncontrasting

confidence: 53%

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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“…Other aerial platform approaches, like an electric multicopter, may lead to better results due to its better flight stability and easier point to point navigation behaviour. Geipel et al [31] took the same MMS1 spectrometer like in the presented manuscript and mounted it to a hexacopter. With the same ground-truth information via sampling the above-ground BM they were able to measure higher correlations with BM and grain yield, taking into account a data acquisition system for all involved sensors [32].…”

Section: Discussionmentioning

confidence: 99%

Utilisation of Ground and Airborne Optical Sensors for Nitrogen Level Identification and Yield Prediction in Wheat

et al. 2018

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Abstract:A healthy crop growth ensures a good biomass development for optimal yield amounts and qualities. This can only be achieved with sufficient knowledge about field conditions. In this study we investigated the performance of optical sensors in large field trails, to predict yield and biomass characteristics. This publication investigated how information fusion can support farming decisions. We present the results of four site-year studies with one fluorescence sensor and two spectrometers mounted on a ground sensor platform, and one spectrometer built into a fixed-wing unmanned aerial vehicle (UAV). The measurements have been carried out in three winter wheat fields (Triticum aestivum L.) with different Nitrogen (N) levels. The sensor raw data have been processed and converted to features (indices and ratios) that correlate with field information and biological parameters. The aerial spectrometer indices showed correlations with the ground truth data only for site-year 2. FERARI (Fluorescence Excitation Ratio Anthocyanin Relative Index) and SFR (Simple Fluorescence Ratio) from the Multiplex ® Research fluorometer (MP) in 2012 showed significant correlations with yield (Adj. r 2 ≤ 0.63), and the NDVI (Normalised Difference Vegetation Index) and OSAVI (Optimized Soil-Adjusted Vegetation Index) of the FieldSpec HandHeld sensor (FS) even higher correlations with an Adj. r 2 ≤ 0.67. Concerning the available N (N avail ), the REIP (Red-Edge Inflection Point) and CropSpec indices from the FS sensor had a high correlation (Adj. r 2 ≤ 0.86), while the MP ratio SFR was slightly lower (Adj. r 2 ≤ 0.67). Concerning the biomass weight, the REIP and SAVI indices had an Adj. r 2 ≤ 0.78, and the FERARI and SFR ratios an Adj. r 2 ≤ 0.85. The indices of the HandySpec Field ® spectrometer gave a lower significance level than the FS sensor, and lower correlations (Adj. r 2 ≤ 0.64) over all field measurements. The features of MP and FS sensor have been used to create a feature fusion model. A developed linear model for site-year 4 has been used for evaluating the rest of the data sets. The used model did not correlate on a significant de novo level but by changing only one parameter, it resulted in a significant correlation. The data analysis reveals that by increasing mixed features from different sensors in a model, the higher and more robust the r 2 values became. New advanced algorithms, in combination with existent map overlay approaches, have the potential of complete and weighted decision fusion, to ensure the maximum yield for each specific field condition.

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“…UAVs have been used for different purposes in precision agriculture, such as optical remote sensing of biomass and fertilization demand in wheat (Geipel et al, 2016), multispectral imaging for weed detection (TorresSànchez et al, 2015), and thermal imaging for stress detection in spring wheat (Kusnierek and Korsaeth, 2014).…”

Section: Introductionmentioning

confidence: 99%

Development of a Hybrid Uav Sensor Platform Suitable for Farm-Scale Applications in Precision Agriculture

Pircher

Geipel

Kuśnierek

et al. 2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Today's modern precision agriculture applications have a huge demand for data with high spatial and temporal resolution. This leads to the need of unmanned aerial vehicles (UAV) as sensor platforms providing both, easy use and a high area coverage. This study shows the successful development of a prototype hybrid UAV for practical applications in precision agriculture. The UAV consists of an off-the-shelf fixed-wing fuselage, which has been enhanced with multi-rotor functionality. It was programmed to perform predefined waypoint missions completely autonomously, including vertical take-off, horizontal flight, and vertical landing. The UAV was tested for its return-to-home (RTH) accuracy, power consumption and general flight performance at different wind speeds. The RTH accuracy was 43.7 cm in average, with a root-mean-square error of 39.9 cm. The power consumption raised with an increase in wind speed. An extrapolation of the analysed power consumption to conditions without wind resulted in an estimated 40 km travel range, when we assumed a 25 % safety margin of remaining battery capacity. This translates to a maximal area coverage of 300 ha for a scenario with 18 m/s airspeed, 50 minutes flight time, 120 m AGL altitude, and a desired 70 % of image side-lap and 85 % forwardlap. The ground sample distance with an in-built RGB camera was 3.5 cm, which we consider sufficient for farm-scale mapping missions for most precision agriculture applications.

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A Programmable Aerial Multispectral Camera System for In-Season Crop Biomass and Nitrogen Content Estimation

Cited by 65 publications

References 35 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

Utilisation of Ground and Airborne Optical Sensors for Nitrogen Level Identification and Yield Prediction in Wheat

Development of a Hybrid Uav Sensor Platform Suitable for Farm-Scale Applications in Precision Agriculture

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