Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Agüera-Vega, Francisco; Ramírez, Fernando Carvajal; Saiz, Mónica Pérez; Rosua, Francisco Orgaz

doi:10.1016/j.biosystemseng.2015.01.008

Cited by 163 publications

(87 citation statements)

References 28 publications

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“…5 Similarly, UAS-based monitoring of a sunflower crop found significant linear regressions between NDVI and grain yield as well as biomass and nitrogen content. 6 Research has also discovered good correlation between NDVI and yield or biomass in rice 7,8 and grain sorghum. 9 Using NDVI to estimate crop health can be complicated by the effects of ground that is visible through gaps in the canopy.…”

Section: Introductionmentioning

confidence: 96%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

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"Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment," J. Appl. Remote Sens. 11(2), 026035 (2017), doi: 10.1117/1.JRS.11.026035. Abstract. A small, fixed-wing unmanned aircraft system (UAS) was used to survey a replicated small plot field experiment designed to estimate sorghum damage caused by an invasive aphid. Plant stress varied among 40 plots through manipulation of aphid densities. Equipped with a consumer-grade near-infrared camera, the UAS was flown on a recurring basis over the growing season. The raw imagery was processed using structure-from-motion to generate normalized difference vegetation index (NDVI) maps of the fields and three-dimensional point clouds. NDVI and plant height metrics were averaged on a per plot basis and evaluated for their ability to identify aphid-induced plant stress. Experimental soil signal filtering was performed on both metrics, and a method filtering low near-infrared values before NDVI calculation was found to be the most effective. UAS NDVI was compared with NDVI from sensors onboard a manned aircraft and a tractor. The correlation results showed dependence on the growth stage. Plot averages of NDVI and canopy height values were compared with per-plot yield at 14% moisture and aphid density. The UAS measures of plant height and NDVI were correlated to plot averages of yield and insect density. Negative correlations between aphid density and NDVI were seen near the end of the season in the most damaged crops.

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Section: Introductionmentioning

confidence: 96%

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Stanton

Starek

Elliott³

et al. 2017

J. Appl. Remote Sens

View full text Add to dashboard Cite

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“…Berni et al [37] suggested that thermal infrared cameras mounted on UAVs show potential for environmental and agricultural applications (mapping canopy conductance and crop water stress). The results of Vonbueren et al [38], Vega et al [39] and Honkavaara et al [40] indicated that data from UAV-based spectral cameras can be used to monitor parameters (e.g., crop height, yield, aboveground biomass and nitrogen content) of various plants, such as grass, wheat and sunflowers. However, the use of UAVs for agricultural monitoring is limited by the weight of hyperspectral imaging systems, the complexity of image processing and the cost of sensors [41,42].…”

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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“…However, recently more attention has been given to the use of crop height modelling for yield estimation [10][11][12]. In this context, several studies have shown that crop productivity, in certain crop types (e.g., maize, potato, barley, wheat, corn, rice, sunflower and poppy [11,[13][14][15][16][17][18][19][20][21][22], can be assessed from biophysical characteristics, such as crop height and biomass. Crop height is an important factor for yield estimation and crop management [11,[19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

“…In this context, several studies have shown that crop productivity, in certain crop types (e.g., maize, potato, barley, wheat, corn, rice, sunflower and poppy [11,[13][14][15][16][17][18][19][20][21][22], can be assessed from biophysical characteristics, such as crop height and biomass. Crop height is an important factor for yield estimation and crop management [11,[19][20][21][22][23]. Crop height is a significant indicator of yield estimation in maize [10], whereas, in barley, crop height has been utilised to estimate biomass [11].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2017

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Abstract: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 R 2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R 2 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 (R 2 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.

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Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop

Cited by 163 publications

References 28 publications

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment

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

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

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