Abstract
Biomass monitoring is one of the main pillars of precision farm management as it involves deeper knowledge about pest and weed status, soil quality, water stress, and yield prediction, among others. This research focuses on estimating crop biomass from high-resolution red, green, blue imaging obtained with an unmanned aerial vehicle. Onion, as one of the most cultivated vegetables, was studied for two seasons under non-controlled conditions in two commercial plots. Green canopy cover, crop height, and canopy volume (Vcanopy) were the predictor variables extracted from the geomatic products. Strong relationships were found between Vcanopy and dry leaf biomass and dry bulb biomass. Adjusted coefficient of determination (\({\text{R}}_{\text{adj}}^2\)) values were 0.76 and 0.95, respectively. Nevertheless, crop management practices and leaf depletion at vegetative stages significantly affect the accuracy of the canopy model. These results suggested that obtaining biomass using aerial images are a good alternative to other sensors and platforms as they have high spatial and temporal resolution to perform high-quality biomass monitoring.
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Acknowledgments
Authors would like to thank to National Government for funding AGL2014-59747-C2-1-R project and Regional Government of Castilla-La Mancha for funding PEII-2014-011-P project. We also wish to thank the Water User Association SORETA located in Tarazona de La Mancha, Albacete, Spain and the Irrigation Users’ Association of “Eastern Mancha” for their support of this work.
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Ballesteros, R., Ortega, J.F., Hernandez, D. et al. Onion biomass monitoring using UAV-based RGB imaging. Precision Agric 19, 840–857 (2018). https://doi.org/10.1007/s11119-018-9560-y
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DOI: https://doi.org/10.1007/s11119-018-9560-y