Data science with R: uses in the agricultural sector

Abstract

Talk on data science with R for the agricultural sector. It defines data science, data scientist and artificial intelligence, and explains machine learning and deep learning, performance versus data volume and the trade-off between complexity and interpretability. It reviews the massive use of information, software tools and agricultural applications (SIPRA in Colombia, Agrosavia information systems, SoilGrids and Chitale in India). It presents the data analysis workflow with the tidyverse, an aquaculture dashboard built with R and learning resources.