Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Sankaran, Sindhuja; Khot, Lav R.; Espinoza, Carlos Zúñiga; Jarolmasjed, Sanaz; Sathuvalli, Vidyasagar; Vandemark, George J.; Miklas, Phillip N.; Carter, Arron H.; Pumphrey, Michael O.; Knowles, N. Richard; Pavek, M. J.

doi:10.1016/j.eja.2015.07.004

Cited by 388 publications

(284 citation statements)

References 126 publications

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“…The result with a few samples with a small variation was slightly better than when using the average value as the estimate; this indicated that with the comprehensive machine learning method the estimation accuracy could be improved from the case of using only average values, as it revealed relatively small spatial variations. Although we obtained promising results using datasets from the 140 m or higher flight heights, the use of lower height data, and thus more precise CHMs, can improve the estimations, as shown in previous studies using flight heights of 50 m or less [4,21,25]. We assume that the spatial and radiometric resolution of the images are the fundamental factors impacting the quality of CHM thus we expect that alternatively a better-quality imaging system could also provide good results from higher altitudes; this would be advantageous if aiming at mapping larger areas.…”

Section: Discussionmentioning

confidence: 88%

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: 88%

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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“…Prior to the emergence of remote sensing technology, large AGB surveys were limited by labor and resources [4][5][6]. Hyperspectral technology has shown great potential for monitoring of crop parameters [15,16]. Hyperspectral sensors can obtain spectral features based on radiation from visible to near-infrared wavelengths, and advancements in technology have increased the number of features that can be collected [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

et al. 2017

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Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Württemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R 2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R 670 only: R 2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R 2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R 2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R 2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R 2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management.

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“…In addition to imaging sensors, LIDAR sensors are also used to estimate crop height and volume at high voxel resolutions (Christiansen et al 2017). A comprehensive review of high-resolution aerial phenotyping systems is provided by Sankaran et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

2018

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This article describes the design and field evaluation of a low-cost, high-throughput phenotyping robot for energy sorghum for use in biofuel production. High-throughput phenotyping approaches have been used in isolated growth chambers or greenhouses, but there is a growing need for field-based, precision agriculture techniques to measure large quantities of plants at high spatial and temporal resolutions throughout a growing season. A lowcost, tracked mobile robot was developed to collect phenotypic data for individual plants and tested on two separate energy sorghum fields in Central Illinois during summer 2016. Stereo imaging techniques determined plant height, and a depth sensor measured stem width near the base of the plant. A data capture rate of 0.4 ha, bi-weekly, was demonstrated for platform robustness consistent with various environmental conditions and crop yield modeling needs, and formative human-robot interaction observations were made during the field trials to address usability. This work is of interest to researchers and practitioners advancing the field of plant breeding because it demonstrates a new phenotyping platform that can measure individual plant architecture traits accurately (absolute measurement error at 15% for plant height and 13% for stem width) over large areas at a sub-daily frequency; furthermore, the design of this platform can be extended for phenotyping applications in maize or other agricultural row crops.

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Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review

Cited by 388 publications

References 126 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 Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models

Design and field evaluation of a ground robot for high-throughput phenotyping of energy sorghum

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