Data science with R: uses in the agricultural sector (2021 edition)

Abstract

Second, updated edition of the talk on data science with R for the agricultural sector. It opens with probability as the language of uncertainty, statistical modeling and the explain-vs-predict dilemma. It then covers free software, R, open data, data science, artificial intelligence and machine learning, adding TensorFlow applications in agriculture and dairy production to the SIPRA, Agrosavia, SoilGrids and Chitale cases. It ends with the tidyverse analysis workflow, learning resources, papers on machine learning in plant tissue culture, and an example using Dryad data on cold response in in vitro cultured Arabidopsis lyrata clones.