Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV

Matteo Ziliani, Stephen Parkes, Ibrahim Hoteit, Matthew McCabe

Research output: Contribution to journalArticlepeer-review

61 Scopus citations


Monitoring the development of vegetation height through time provides a key indicator of crop health and overall condition. Traditional manual approaches for monitoring crop height are generally time consuming, labor intensive and impractical for large-scale operations. Dynamic crop heights collected through the season allow for the identification of within-field problems at critical stages of the growth cycle, providing a mechanism for remedial action to be taken against end of season yield losses. With advances in unmanned aerial vehicle (UAV) technologies, routine monitoring of height is now feasible at any time throughout the growth cycle. To demonstrate this capability, five digital surface maps (DSM) were reconstructed from high-resolution RGB imagery collected over a field of maize during the course of a single growing season. The UAV retrievals were compared against LiDAR scans for the purpose of evaluating the derived point clouds capacity to capture ground surface variability and spatially variable crop height. A strong correlation was observed between structure-from-motion (SfM) derived heights and pixel-to-pixel comparison against LiDAR scan data for the intra-season bare-ground surface (R2 = 0.77 − 0.99, rRMSE = 0.44% − 0.85%), while there was reasonable agreement between canopy comparisons (R2 = 0.57 − 0.65, rRMSE = 37% − 50%). To examine the effect of resolution on retrieval accuracy and processing time, an evaluation of several ground sampling distances (GSD) was also performed. Our results indicate that a 10 cm resolution retrieval delivers a reliable product that provides a compromise between computational cost and spatial fidelity. Overall, UAV retrievals were able to accurately reproduce the observed spatial variability of crop heights within the maize field through the growing season and provide a valuable source of information with which to inform precision agricultural management in an operational context.
Original languageEnglish (US)
Pages (from-to)2007
JournalRemote Sensing
Issue number12
StatePublished - Dec 11 2018

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: Funding: Research reported in this publication was supported by the King Abdullah University of Science and Technology (KAUST). Acknowledgments: We greatly appreciate the logistical, equipment and scientific support offered to our team by Alan King and employees of the Tawdeehiya Farm in Al Kharj, Saudi Arabia and to our research scientist Samer K Al-Mashharawi, without whom this research would not have been possible.


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