Abstract
Applications of remote sensing using unmanned aerial vehicle (UAV) in agriculture has proved to be an effective and efficient way of obtaining field information. In this study, we validated the feasibility of utilizing multi-temporal color images acquired from a low altitude UAV-camera system to monitor real-time wheat growth status and to map within-field spatial variations of wheat yield for smallholder wheat growers, which could serve as references for site-specific operations. Firstly, eight orthomosaic images covering a small winter wheat field were generated to monitor wheat growth status from heading stage to ripening stage in Hokkaido, Japan. Multi-temporal orthomosaic images indicated straightforward sense of canopy color changes and spatial variations of tiller densities. Besides, the last two orthomosaic images taken from about two weeks prior to harvesting also notified the occurrence of lodging by visual inspection, which could be used to generate navigation maps guiding drivers or autonomous harvesting vehicles to adjust operation speed according to specific lodging situations for less harvesting loss. Subsequently orthomosaic images were geo-referenced so that further study on stepwise regression analysis among nine wheat yield samples and five color vegetation indices (CVI) could be conducted, which showed that wheat yield correlated with four accumulative CVIs of visible-band difference vegetation index (VDVI), normalized green-blue difference index (NGBDI), green-red ratio index (GRRI), and excess green vegetation index (ExG), with the coefficient of determination and RMSE as 0.94 and 0.02, respectively. The average value of sampled wheat yield was 8.6 t/ha. The regression model was also validated by using leave-one-out cross validation (LOOCV) method, of which root-mean-square error of predication (RMSEP) was 0.06. Finally, based on the stepwise regression model, a map of estimated wheat yield was generated, so that within-field spatial variations of wheat yield, which was usually seen as general information on soil fertility, water potential, tiller density, etc., could be better understood for applications of site-specific or variable-rate operations. Average yield of the studied field was also calculated according to the map of wheat yield as 7.2 t/ha.
Highlights
Remote sensing has been successfully used as an effective method for obtaining field information through analysis of reflectance or radiance of specific bands’ digital numbers [1,2]
The application of color cameras sharply decreases the high cost of remote sensing [12], since most digital cameras use a Bayer-pattern array of filters to obtain an RGB digital image, and the acquisition of near-infrared (NIR) band images usually requires an extra filter that converts digital numbers of either blue or red light in Bayer array into NIR readings through massive post-processing and calibration work [13]
Rasmussen et al investigated the reliability of four different vegetation indices derived from consumer-grade true color camera as well as a color-infrared camera that are mounted on unmanned aerial vehicle (UAV) for assessing experimental plots, and concluded that vegetation indices of UAV imagery have the same ability as ground-based recordings to quantify crop responses to experimental treatments, such shortcomings like angular variations in reflectance, stitching, and ambient light fluctuation should be taken into consideration [14]
Summary
Remote sensing has been successfully used as an effective method for obtaining field information through analysis of reflectance or radiance of specific bands’ digital numbers [1,2]. Aerial photography has been used to monitor crop growth status as regional or medium-scale applications of agricultural remote sensing ever since 1950s by using color or color-infrared cameras [5]. Agricultural application of UAV remote sensing by using color cameras instantly provides researchers and farmers with actual and intuitive visualization of crop growth status, since color images accentuate particular vegetation greenness and have been suggested to be less sensitive to variations of illumination conditions [10,11]. Rasmussen et al investigated the reliability of four different vegetation indices derived from consumer-grade true color camera as well as a color-infrared camera that are mounted on UAVs for assessing experimental plots, and concluded that vegetation indices of UAV imagery have the same ability as ground-based recordings to quantify crop responses to experimental treatments, such shortcomings like angular variations in reflectance, stitching, and ambient light fluctuation should be taken into consideration [14].
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