What do cranberry farmers, bugs, deep learning and a mid-western creative agency have in common? Join me to learn how with today’s tools, and a sense of why not, you can jump in and help solve real world needs. In this session, I am going to dive into how we are using deep learning to help cranberry farmers in the mid-west solve real world problems. A part of it is technology, but an equal emphasis is on the business needs that deep learning can help solve. Hopefully I can help show that in today’s world, the obstacles of solving hard technical problems is just the willingness to try something. Key Takeaways: – Data is still king and it helps if you can control collection and labelling of the data. We built an app for that. – You need the right partnerships. Getting SME’s outside your organization to get excited and contribute can really increase the odds of success – You do not need to invent your own models. Sometimes using a model developed for another domain can be adapted for your use case. – There is a tradeoff between accuracy and usability. You don’t need to sacrifice either, but finding the sweet spot can move an idea from an idea to an actual product. – You can take a conventional team of developers and get moving or you can wait and never get started. – It will be very hard to compete against Google and the other gorillas when it comes to general problem domains’ focus on a domain that you have an intimate understanding. Therein lies paths to innovative solutions.

1. The Cranberry fights back!
Deep Learning and Pest Management
in Cranberry Farming

2. CRANBERRIES-
THE WHAT AND THE
WHO AND THE
BUGZ

3. Cranberry Farming
3
Native to North Eastern America
#1 Fruit Crop in terms of acreage and value
in Wisconsin
$1B in value
Wisconsin produces about 1/2 of World Supply
21,000 acres of vines, and 140,000+ acres of
support lands
Production costs of $6,300 / acre
Employs 4000 people in the state

4. BUGS

5. Cranberry Bugs
Blackheaded Fireworm and
Cranberry Fruitworm are the
significant bugs
Current Solution: Call in IPM or
send sample to field stations
(2 week turn around time)
What it means to a Farmer
5

6. OVERALL
ARCHITECTURE

7. ARCHITECTURE + APP
System Overview
Application

8. Data Collection
11 Data/Image gatherers in the
field (Growers and Ocean Spray
Scientists)
>100 Images / Insect Class
Uploaded (Target…300/Class…5
classes)
Image classified with existing
model
8

9. Data Labeling
By SME’s: Research scientists
at Ocean Spray
Integration of labeling in App
is a next step (manual at the
moment)
9

10. Results so far
Number of participants: 11
Data gathering period: Started
in Early April and is Ongoing
Initial Results: 85% accuracy
10

11. The People Involved
3 Engineers from LC (2 Backend - Python + C# and 1 Mobile dev Objective C)
1 Account Executive from LC
1 Active Researcher at Ocean Spray, 3 more on Volunteer basis
10 Growers participating
Amount of Data Science/ ML experience prior to Application :(
Amount of Data Science/ ML experience prior to Application :):):) !!!
11

12. Future - Near Term
• Opening up access to application to all WSCGA growers
• Using TF Serving … making this a bit more robust
• Expansion of app scope to integrate weather data and anomaly
detection (Bugz showing up earlier or later than expected)
• WSCGA has applied for a USDA Specialty Crop Block Grant for funding
• Grant application chosen by the Wisconsin Department of
Agriculture, Trade and Consumer Protection (DATCP) to be included in
Wisconsin’s overall application to the USDA
• Will find out results of the funding application in the fall
12

13. 13
Be nice to
your toaster