Agricultural Applications Using Machine Learning
As our agricultural systems are expected to sustain a population of around ten billion by 2050, and to increase their production by nearly 50 percent globally (FAO, 2018), agricultural producers, intermediaries, distributors and re-sellers are looking at novel alternatives to improve their efficiencies and reduce their risk exposure. Drivers affecting agricultural systems are often weather related and might include extreme events and environmental conditions that are favorable to different pests, as well as bio-geophysical characteristics of their terroir and crop. Here we will show some advances in machine learning that are progressively being incorporated to support those management decisions
Dr. Gaitan did his doctoral studies at the University of British Columbia (Vancouver, Canada) working with William Hsieh in machine learning applications in the environmental sciences. He also holds a Bachelor degree in Civil Engineering and a Master degree in Hydrosystems from the Pontificia Universidad Javeriana (Bogota, Colombia). He is a member of the American Meteorological Society’s (AMS) Artificial Intelligence Committee. He previously worked as a VP of Weather Forecasting at Arable Labs and as Research Scientist for the South Central Climate Science Center at the Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey.