Big Data and Machine Learning Solutions to Improve Agriculture

Published Mar-08-19

A farming app and machine learning models to predict the performance of seed varieties.

CGIAR, France

The Story:

Big Data and Machine Learning Solutions to Improve Agriculture From using sophisticated algorithms to make accurate crop predictions to helping farmers speed up plant growth big data is making a huge impact in agriculture across the globe. Advances in computer power, data communications and data storage have created opportunities for the development of more sophisticated tools to make farming more efficient, precise and profitable. Among those looking to take advantage of the opportunities that data can provide is CGIAR, a global partnership of organizations engaged in research for a food-secure future.

One of the ways it is trying to do just that is with its Inspire Challenge, an open innovation competition that encourages the use of big data to advance agricultural research and development. Winning teams receive grants of $100,000 to develop their innovative pilot projects.

Innovative Ideas

The 2018 iteration of the big data contest attracted more than 130 proposals from applicants all over the world in four challenge categories.

• Disrupting Impact Assessment
• Empowering Data-Driven Farming
• Monitoring Pests and Diseases
• Revealing Food Systems

Five winners received grants to put their ideas into practice. They were:

An integrated data pipeline for small-scale fisheries: about 40 million small-scale fishers go out fishing every day but virtually none of the activities or yields are documented. This project is for an automated data pipeline to highlight temporal and spatial changes in fish production.

CubicA, a new farmer advisory app: Ugandan farmers already receive weather and agricultural information in their own language from a free hotline called CubicA. This project will use big data and machine learning to combine previous caller data with satellite images, weather forecasts and other data sources to give more specific information to farmers.

Machine learning for smarter seed selection: BioSense will develop machine learning models that predict the performance of seed varieties under specific conditions at designated farms.

Revealing informal food flows through free Wi-Fi: the flow of food through traditional and informal markets is largely invisible, yet this is the main source of food for many of the world's poor. This project will provide free internet access to markets in Hanoi, Vietnam to characterize and monitor food flows between traders, retailers and consumers. The aim is to identify better policy and planning options to upgrade
distribution channels

Using Commercial Microwave Links (CML) to estimate rainfall: the project wants to improve the accuracy of real-time rainfall measurements for more precise crop-yield monitoring. It will demonstrate the potential of Commercial Microwave Links (CML) technology to estimate rainfalls in crop production monitoring and improve the design of the rainfall-based index insurance in Kenya.

Putting Ideas into Action

The winning teams have 12 months to implement their pilot projects and demonstrate viability and impact. If they succeed they will be eligible for an additional scale-up grant. The project that shows the most significant impact will be awarded a further $250,000 to help it continue to make a difference.

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