Fecha de publicación: 17/05/2019 Fuente: Wipo "precision agriculture" Precision agriculture (PA) is an integration of a set of technology aim to look up efficiency and productivity while behind the quality of the adjacent surroundings. It is a process that vastly relies on high-resolution information to enable greater precision in the management of inputs to production. This dissertation explored the usage of multispectral high resolution aerial imagery acquired by an unmanned aerial systems (UAS) platform to serve precision agriculture application. The UAS acquired imagery in the visual, near infrared and thermal infrared spectra with a resolution of less than a meter (15 - 60 cm). This research focused on developing two models to estimate cm-scale chlorophyll content and leaf nitrogen. To achieve the estimations a well-established machine learning algorithm (relevance vector machine) was used. The two models were trained on a dataset of in situ collected leaf chlorophyll and leaf nitrogen measurements, and the machine learning algorithm intelligently selected the most appropriate bands and indices for building regressions with the highest prediction accuracy.