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Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots

View Article: PubMed Central

ABSTRACT

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

No MeSH data available.


Zoom in the biophysical parameters maps on May, the 25th, over atypical line of plots (line 35) of the trial, showing a diagonal pattern of QN and LAI values distribution.
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f16-sensors-08-03557: Zoom in the biophysical parameters maps on May, the 25th, over atypical line of plots (line 35) of the trial, showing a diagonal pattern of QN and LAI values distribution.

Mentions: In addition, the fine resolution of the maps derived at the pixel scale (20cm/pixel) allows getting very accurate information to analyze the intraplot variability. For instance, a diagonal pattern appears on the five plots on row 35, for both LAI and QN, slightly on the 29th of April and 8th of June (Figure 13 and Figure ) and very clearly on the 25th of May (Figure 16). These plots are indeed buffer plots between two nitrogen treatments. On the lower part of the trial (row 34 to 22), a nitrogen dressing (ammonium nitrate) of 50 kgN/ha as was applied mechanically on the 6th of April. On the upper part (row 36 to 52) no nitrogen was applied. The image of the 14/04 was acquired too early to display the effect of nitrogen, but this latter is obvious on the following dates (29/04, 25/05 and 08/06) for LAI as well as for QN. The diagonal pattern in the buffer line is due the nitrogen spraying method used.


Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots
Zoom in the biophysical parameters maps on May, the 25th, over atypical line of plots (line 35) of the trial, showing a diagonal pattern of QN and LAI values distribution.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3675559&req=5

f16-sensors-08-03557: Zoom in the biophysical parameters maps on May, the 25th, over atypical line of plots (line 35) of the trial, showing a diagonal pattern of QN and LAI values distribution.
Mentions: In addition, the fine resolution of the maps derived at the pixel scale (20cm/pixel) allows getting very accurate information to analyze the intraplot variability. For instance, a diagonal pattern appears on the five plots on row 35, for both LAI and QN, slightly on the 29th of April and 8th of June (Figure 13 and Figure ) and very clearly on the 25th of May (Figure 16). These plots are indeed buffer plots between two nitrogen treatments. On the lower part of the trial (row 34 to 22), a nitrogen dressing (ammonium nitrate) of 50 kgN/ha as was applied mechanically on the 6th of April. On the upper part (row 36 to 52) no nitrogen was applied. The image of the 14/04 was acquired too early to display the effect of nitrogen, but this latter is obvious on the following dates (29/04, 25/05 and 08/06) for LAI as well as for QN. The diagonal pattern in the buffer line is due the nitrogen spraying method used.

View Article: PubMed Central

ABSTRACT

This paper outlines how light Unmanned Aerial Vehicles (UAV) can be used in remote sensing for precision farming. It focuses on the combination of simple digital photographic cameras with spectral filters, designed to provide multispectral images in the visible and near-infrared domains. In 2005, these instruments were fitted to powered glider and parachute, and flown at six dates staggered over the crop season. We monitored ten varieties of wheat, grown in trial micro-plots in the South-West of France. For each date, we acquired multiple views in four spectral bands corresponding to blue, green, red, and near-infrared. We then performed accurate corrections of image vignetting, geometric distortions, and radiometric bidirectional effects. Afterwards, we derived for each experimental micro-plot several vegetation indexes relevant for vegetation analyses. Finally, we sought relationships between these indexes and field-measured biophysical parameters, both generic and date-specific. Therefore, we established a robust and stable generic relationship between, in one hand, leaf area index and NDVI and, in the other hand, nitrogen uptake and GNDVI. Due to a high amount of noise in the data, it was not possible to obtain a more accurate model for each date independently. A validation protocol showed that we could expect a precision level of 15% in the biophysical parameters estimation while using these relationships.

No MeSH data available.