Résumé
In Colombia up to 40% of yield variability is due to the effects of climate variations. Rapid phenotyping methods are needed to properly assess the crop and improve production rates. In this paper, we propose to focus on developing a noninvasive system for speeding up monitoring tasks in rice crops. Unmanned Aerial Vehicles are used to gather multispectral visual information for high-throughput crop monitoring. Geo-referenced digital surface models of the crop are generated based on image mosaicing techniques to allow for the autonomous computation of several vegetative indices. This paper presents the implemented system (hardware and software) and a field report of experiments carried out at different crop growth stages.