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

Abstract

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.