Webinar – Machine Learning with Metadata and Experimental Citizen Science in R
Webinar organized by the Geospatial Data Community of Practice of the CGIAR Platform for Big Data in Agriculture.
Machine learning is the scientific study of algorithms and statistical models that provides the computer the ability to automatically learn and improve from experience. In this workshop, we will work in a set of tools developed by Bioversity-CIAT to facilitate the analysis of metadata and experimental citizen science data, from collating data of different sources, gathering environmental variables, to model selection and visualization. All in a single pipeline in R that can be automated to improve predictions and recommendations for agriculture.
All data and R code files are available here.
Kauê de Sousa
Research Fellow at the Alliance of Bioversity International and CIAT
Kauê de Sousa, is a Ph.D. Fellow at the Inland Norway University and Research Fellow with the The Alliance of Bioversity and CIAT. His research focuses on assessing the role of crowdsourcing citizen science in providing information for climate adaptation in small-scale agriculture; specifically, in generating new insights for crop recommendations and farming adaptation based on genotype x environment interactions under extreme climatic events. His past experiences include working on the adaptation of perennial crops, farm diversification, forest dynamics and information services for the World Agroforestry Centre (ICRAF), the Food and Agriculture Organization of the United Nations (FAO) and the Tropical Agricultural Research and Higher Education Center (CATIE). Kauê is currently based in Hamar, Norway.
March 12, 2020
Geospatial Data Community of Practice
CGIAR Platform for Big Data in Agriculture