Worldwide, approximately 800 million people suffer from
malnutrition. That's about one in every nine people living on earth. How can we
significantly affect this statistic given limited resources and an unequal
distribution of farmlands and wealth around the globe?
One significant approach to reducing that number requires a major
increase in yield production. Optimizing fertilization can increase yield by up
to 50 percent, but it requires knowledge and resources that local farmers might
not have access to. Our Fertilyzer app, one of the winners of the recent
IBM
Watson + GBS Cognitive Challenge, just might hold the answer to helping farmers
increase their yields dramatically.
An internal IBM effort, the Challenge galvanized employees
to develop Watson-powered cognitive apps using Bluemix, IBM's cloud platform
for building and managing mobile applications. Our work on Fertilyzer was
inspired by ongoing talks we have had with one of our agricultural services clients.
Fertilyzer was awarded “Best Mobile Experience” by Challenge
judges for its intuitive interface, simple access to multiple information
sources, and easy-to-understand data visualizations. But this Android app is
more than just great to look at. Put together by an Israeli team of IBMers led
by Roi Zahut from IBM’s
Global Business
Services, fellow GBS employees Matan Mashiah and Yosef Ben-David, and Matan Ninio and myself from
Research,
Fertilyzer is a cognitive mobile app built using
Bluemix. It taps into Watson technology to recommend
optimized fertilization plans and track their progress.
The Fertilyzer model allows farmers to consider weather
conditions, soil types, and field history to generate fertilization plans and
predict cost, yield, and profit. Once all the predictions and related plans of
action are ready, Fertilyzer then uses IBM Watson
Tradeoff
Analytics to help the farmer make the right decision.
The app also includes a dashboard that helps keep the farmer updated on agricultural news and plan progression. Fertilyzer reads through an
immense range of news articles and tweets and sends relevant news and alerts to the farmer when it finds something important, like a forecasted price drop or
an upcoming storm, so farmers can act on new information.
Let's say a farmer is planning an upcoming season for a
specific crop, such as a russet potato variety. Fertilyzer's connection with
Watson Tradeoff Analytics can help him weigh different options, while stressing
certain variables, such as profit, and filtering out others, like risk. But if
news starts to trend before planting about an expected potato price crash in
his region, Watson's news filter can send an alert to the farmer's Fertilyzer
app, thereby allowing him to adjust crop plans before it's too late.
Ultimately, more-efficiently grown crops will improve yield, providing more
food to more people. In a small but substantial way, we hope Fertilyzer will
help communities tackle hunger and malnutrition.
Our next steps for development are to test the tool and gather
more data. We’ve built a great prediction model that takes into account a wide variety
of environmental parameters, but now we need to see how it will work in the
field (pun intended). We also plan to extend the tool’s news and social media
services to support more languages and learn how to detect even more events.
For more information about Fertilyzer,
contact me.
Labels: agriculture, bluemix, farming, fertilization, Fertilyzer, GBS, IBM Research - Haifa, IBM Watson, mobile application development, Tradeoff Analytics
