Quantifying Nitrogen Status of Rice Using Low Altitude UAV-Mounted System and Object-Oriented Segmentation Methodology

Nitrogen deficiency can seriously reduce yield, while over-fertilization can result problems such as excess nutrient runoff and groundwater pollution. Hence, efficient methods for assessing crop nitrogen status are needed to enable more optimal fertilizer management. The ability to quantify the different nitrogen application rates by crops using digital images taken from an unmanned aerial vehicle (UAV) was investigated in comparison with ground-based hyperspectral reflectance and chlorophyll content data from 140 plots on a managed field. This research utilized new UAV system, comprised of remote-controlled helicopter (Hercules II) and digital camera (EOS 30D), was used to characterize spatial and temporal variation in crop production. Digital information was extracted based on an object-oriented segmentation method, and the color parameter was reduced and represented using principal component analysis (PCA). An estimating model was established after analyzing the relationship between the optimal color parameter and ground-based measurements. Model testing demonstrated that unknown samples could be associated with the controlled nitrogen application rates (0, 60, 90, and 120 kg N·hm−2 ): 91.6% %; N1 (60 kg N·hm−2 ): 70.83%; N2 (90 kg N·hm−2 ): 86.7%; N3 (120 kg N·hm−2 ): 95%. Overall, this result proved to provide a cost-effective and accurate way and the UAV was an exploratory and predictive tool when applied to quantify different status of nitrogen. In addition, it indicated that the application of digital image from UAV to the problem of estimating different nitrogen rates is promising.Copyright © 2009 by ASME

[1]  P. Scharf,et al.  Calibrating Corn Color from Aerial Photographs to Predict Sidedress Nitrogen Need , 2002 .

[2]  Principal component analysis of intraspecific responses of tartary buckwheat to UV-B radiation under field conditions , 2007 .

[3]  J. Ladha,et al.  Nitrogen Losses and Lowland Rice Yield as Affected by Residue Nitrogen Release , 1994 .

[4]  Elizabeth Pattey,et al.  Impact of nitrogen and environmental conditions on corn as detected by hyperspectral reflectance , 2002 .

[5]  Gary E. Varvel,et al.  Light Reflectance Compared with Other Nitrogen Stress Measurements in Corn Leaves , 1994 .

[6]  James A. Brass,et al.  Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support , 2004 .

[7]  M. Richardson,et al.  Quantifying Turfgrass Color Using Digital Image Analysis , 2003 .

[8]  David Lamb,et al.  PA—Precision Agriculture: Remote-Sensing and Mapping of Weeds in Crops , 2001 .

[9]  F. Mahler,et al.  Soil productivity management and plant growth in the Sahel: Potential of an aerial monitoring technique , 1996, Plant and Soil.

[10]  Kenji Yoshikawa,et al.  Mapping of periglacial geomorphology using kite/balloon aerial photography , 2003 .

[11]  Y. Inoue,et al.  A blimp-based remote sensing system for low-altitude monitoring of plant variables: A preliminary experiment for agricultural and ecological applications , 2000 .

[12]  HIGH RESOLUTION LOW ALTITUDE AERIAL PHOTOGRAPHY FOR RECORDING TEMPORAL CHANGES IN DYNAMIC SURFICIAL ENVIRONMENTS , 2004 .

[13]  C. Daughtry,et al.  Evaluation of Digital Photography from Model Aircraft for Remote Sensing of Crop Biomass and Nitrogen Status , 2005, Precision Agriculture.

[14]  Kazunobu Ishii,et al.  Remote-sensing Technology for Vegetation Monitoring using an Unmanned Helicopter , 2005 .

[15]  James H. Everitt,et al.  A three-camera multispectral digital video imaging system , 1995 .

[16]  James S. Schepers,et al.  Aerial Photography to Detect Nitrogen Stress in Corn , 1996 .

[17]  Irene Marzolff,et al.  Monitoring of gully erosion in the Central Ebro Basin by large-scale aerial photography taken from a remotely controlled blimp , 2003 .