Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring
Keywords: Crop, Surface, Monitoring, Stereoscopic, Multitemporal, Agriculture, Change Detection
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.