In many countries, particularly in West Africa, there is a strong social demand for increased cereal production. Responding to this demand involves the improvement of cereal varieties. Modern varietal breeding programs in the sub-region need to establish the relationship between plant genotype and phenotype to select high-yielding stress-tolerant plants and to enhance agricultural production. However, in most cases, accurate phenotyping of large mapping populations is a limiting factor. The Regional Study Centre for the Improvement of Drought Adaptation (CERAAS) has developed a robust drone-based data collection and spatial modelling process to better measure cereal crops' traits for the benefit of plant breeding programs. Herein, we report an unmanned aerial vehicle (UAV) driven crop characteristics analysis throughout the crop cycle. We present a fully automatic pipeline based on a multispectral imaging system for the indirect measurement of agronomic and phenological characters of crops in agricultural field trials. The pipeline is made up of different stages including image acquisition, georeferencing, generation of orthoimages, creation of masks to delimit individual plots, and calculation of proxies. The incorporation of the UAV into agricultural field experiments has the potential to fast-track the genetic improvement of adaptation to drought.