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

2. What is Digital Agriculture?
ACTION

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

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

5. Have you used machine learning?
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6. How we normally do things
Expert
Asks Question
Provides Dataset
Analyst
Prepares Data
Designs Experiment
Creates model
Team
Answers Question
Evaluates process

7. 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

8. Neural Network

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

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

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

12. 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

13. 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.”