From Data to Action Thanos Gentimis Digital Agriculture From Data to Action Thanos Gentimis

What is Digital Agriculture? ACTION

Fields in Digital Agriculture Agronomy Data Science Meteorology Machine Intelligence Farming Engineering

How does it work? Teach the machine how to: Detect anomalies Predict averages Suggest solutions

Have you used machine learning? *All images and logos belong to their respective owners and are used for illustration purposes only

How we normally do things Expert Asks Question Provides Dataset Analyst Prepares Data Designs Experiment Creates model Team Answers Question Evaluates process

Machine Learning Approach Data Collection Data Coming in Data Warehouse Clustering Trend Analysis Machine Learning Outlier Detection Analyst Explains Trends Evaluates Outliers Asks the right questions Subject Matter Expert

Neural Network

Main Ideas The algorithm “learns by example”. The bigger the dataset the better! Multiple types of input welcome!

Digital Agriculture Class Offered as a grad course Fall 2018. Future plans: Undergraduates Fall 2019 Extension agents Fall 2019 Summer course

Digital Agriculture Colloquium Joshua Woodard (Cornell) Ag Analytics Mid April

Take Home Messages Precision Ag. is here and it is evolving We need to connect with stakeholders Connect with Farmers Connect with Industry Different way of thinking Add other disciplines

Thank you! “The massive tangle of raw data that comes from precision agriculture is like a fertile field full of potential. But just like in farming, if the right tools and seeds are not used the field will never produce crops. We believe the right tools for this new and exciting area of Agriculture can be found in machine learning, since the datasets involved have long surpassed the ability for analysis and prediction of traditional models.”