Abstract
Abstract. Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination R2 between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (n = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible.
Highlights
Crop-Surface Models (CSMs) (Hoffmeister et al, 2010) are a useful tool for monitoring in-field crop growth variability, enabling precision agriculture: It can reap great benefits from remote sensing (Mulla, 2013)
The suitability of using consumer grade cameras for close range surface measurements has been explored and verified in the past (Chandler et al, 2005, Habib et al, 2008), and while Digital Surface Models (DSMs) have been created for geomorphological, geophysical or vulcanological studies (James and Robson, 2012, Heng and Chandler, 2010, Chandler et al, 2002, James and Varley, 2012), there has been no recent research for monitoring crops using oblique stereo imagery
While CSMs are commonly created using stereo photographs acquired from nadir imagery taken from airborne carrier systems (Bendig et al, 2013) or by using Terrestrial Laser Scanning (TLS) systems (Hoffmeister et al, 2010, Tilly et al, 2014), this contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions
Summary
Crop-Surface Models (CSMs) (Hoffmeister et al, 2010) are a useful tool for monitoring in-field crop growth variability, enabling precision agriculture: It can reap great benefits from remote sensing (Mulla, 2013). The suitability of using consumer grade cameras for close range surface measurements has been explored and verified in the past (Chandler et al, 2005, Habib et al, 2008), and while Digital Surface Models (DSMs) have been created for geomorphological, geophysical or vulcanological studies (James and Robson, 2012, Heng and Chandler, 2010, Chandler et al, 2002, James and Varley, 2012), there has been no recent research for monitoring crops using oblique stereo imagery. While CSMs are commonly created using stereo photographs acquired from nadir imagery taken from airborne carrier systems (Bendig et al, 2013) or by using Terrestrial Laser Scanning (TLS) systems (Hoffmeister et al, 2010, Tilly et al, 2014), this contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. Programmable smart cameras allow for the possibility of automated multitemporal image acquisition, further lowering costs when compared to conventional multi-temporal monitoring where images have to be acquired manually
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