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The 2017 Inspire Challenge

The Inspire Challenge is about challenging partners, universities, and others to use CGIAR data to create innovative pilot projects that will scale. We’re looking for novel approaches that democratize data-driven insights to inform local, national, regional, and global policies and applications in agriculture and food security in real time; helping people–especially smallholder farmers and producers–to lead happier and healthier lives.

In 2017, we received over 120 proposals to our Inspire Challenges from applicants all over the world to use our data to create pilot opportunities that scale. We wanted to show the power of big data analytics and ICTs to provide unprecedented, multi-disciplinary insights to researchers, delivering actionable information to farmers and inspiring others to use big data to create impact.

Of the 120 excellent proposals, we awarded five US$100,000 each to develop their innovative pilot projects.

PROPOSALS RECEIVED

WINNERS

US$ GRANT / WINNER

Meet the 2017 winners

The CGIAR Platform for Big Data in Agriculture awarded five research proposals US$100K each at the inaugural annual convention, during 19-22 of September.

The five winning teams have 12 months to implement small-scale proof of concept pilots to demonstrate viability.

The five successful pilots were placed on the trajectory to wider-scale implementation, including the possibility of receiving an additional US$250,000 of scale-up funding. They also have additional help in finding continued funding and widespread adoption within CGIAR, to ensure that these innovations become a part of how we operate system-wide. Read the winning proposals below.

Seeing is believing - Using smartphone camera data

Seeing is believing – Using smartphone camera data

Customized agricultural advice for specific crops based on local weather, soil data, pests and diseases, and input availability can improve management practices, productivity and profitability of smallholder agriculture. But personalized services do not incorporate potentially useful information on what farmers observe in their fields, such as crop color, texture and how the crop is growing. The project aims to provide personalized agricultural advice based on localized information as well as visible crop characteristics derived from farmers’ own smartphone pictures.
Farm.ink: Analysing livestock social media data for farmer chatbot

Farm.ink: Analysing livestock social media data for farmer chatbot

Thousands of historical posts on the topic of dairy farming & livestock can be found in farming Facebook groups in sub-Saharan Africa. These posts often contain detailed reports of livestock disease as well as queries and comments about productivity and management concerns but as yet they remain unanalyzed. The projects propose to combine social media data with ILRI data to create an open-source platform to analyze and visualize emerging livestock disease outbreaks and related issues in Kenya, and disseminate this information through Farm.ink's chatbot service.
Using IVR to connect farmers to market

Using IVR to connect farmers to market

The Inspire Challenge is an initiative to challenge partners, universities, and others to use CGIAR data to create innovative pilot projects that will scale. We look for novel approaches that democratize data-driven insights to inform local, national, regional, and global policies and applications in agriculture and food security in real time; helping people–especially smallholder farmers and producers–to lead happier and healthier lives. This proposal was selected as a 2017 winner, with the team receiving 100,000 USD to put their ideas into practice. Using IVR to connect farmers to market in Nepal The agriculture value chain in Nepal is informal and disaggregated, impeding the flow of information and resources. Development agencies
Pest and disease monitoring by using artificial intelligence

Pest and disease monitoring by using artificial intelligence

The project expects to radically transform pest and disease monitoring by using artificial intelligence (AI), advanced sensor technology and crowdsourcing capable of connecting the global Ag community to help smallholder farmers. It aims to increase the effectiveness of farm-level advice by leveraging three critical advances: 1) the democratization of AI thanks to open access platforms such as Google’s TensorFlow, 2) the miniaturization of technology allowing affordable deployment, and 3) the development of massive communication and money exchange platforms such as M-Pesa that allow rural extension to scale as a viable economic model enabling last mile delivery in local languages.
Real-time diagnostics for devastating wheat rust

Real-time diagnostics for devastating wheat rust

Wheat yellow rust, caused by Puccinia striiformis f. sp. tritici (PST), is currently considered the most damaging wheat disease globally, with yield losses sometimes higher than 60%. This project aims to develop and mainstream an affordable, mobile in-field pathogenomics platform called MARPLE (Mobile And Real-time PLant disEase) to revolutionize crop pathogen surveillance and diagnostics in real time. It will rely on the MinION mobile genome sequencer platform and assess its deployment in situ in Ethiopia.

Be a part of the innovation revolution

By supporting some of the other excellent 2017 Inspire Challenge proposals

We’re encouraging you to support other inspiring projects to help spur the agricultural transformation around the world. Here are some innovations you can help put into practice.

Targeting sustainable intensification technologies with a hyper-local LandPKS tool

Targeting sustainable intensification technologies with a hyper-local LandPKS tool
This tool will guide spatial targeting of sustainable intensification technologies to provide ‘best fit’ options for risk reduction and enhancement of local adaptation of farmers within target geographies ...

Solanify: Crowd Sourcing of Potato Diversity Monitoring

Solanify: Crowd Sourcing of Potato Diversity Monitoring
This project will monitor potato diversity and distribution in the Andes by developing an open source application as a tool to help identify traditional potato varieties from photos using a visual recognition software ...

ICT-based citizen science for preventing Banana Xanthomonas Wilt

ICT-based citizen science for preventing Banana Xanthomonas Wilt
This innovation will provide farmers and extensionists with mobile phone-based Banana Xanthomonas Wilt (BXW) management advice that's based on their report of BXW incidence and severity ...

Geocoded soil data and AI to provide an estimate and indicator for soil health

Geocoded soil data and AI to provide an estimate and indicator for soil health
This project will generate new geocoded soil data and leverage the transformative potential of artificial intelligence to provide an estimate and an indicator of soil health ...

Local Market Enhancement Tool

Local Market Enhancement Tool
This project will build a platform with location-based information on when and where which products are sown and harvested by local farmers to avoid temporal overflows of local markets with certain goods and ensure a more continuous and balanced flow of seeding and harvesting throughout the year ...

Livestock monitoring from the sky

Livestock monitoring from the sky
This innovation will explore how data at different scales can be collected, validated and used to provide a near real-time prediction of livestock numbers in a specific location.  ...

RiceFocus: Scaling Crop Advisory Services to Smallholders

RiceFocus: Scaling Crop Advisory Services to Smallholders
This innovation will generate locally-relevant farmer advice that incorporates climatic variability and diversity of land management approaches within rice-dominant landscapes ...

Tomorrow’s Rain in Africa

Tomorrow's Rain in Africa
This project will build an artificial intelligence next-day rain forecast to drive better decision-making and results on and off the farm ...

DRuID: An informed-Decision platform to Reduce the risk of RIce Diseases

DRuID: An informed-Decision platform to Reduce the risk of RIce Diseases
The tool will allow for managing rice disease in real-time and for defining breeding priorities for specific regions ...

Cost of Inaction Calculator

Cost of Inaction Calculator
The Cost of Inaction (COI) Calculator will be an online platform that translates agricultural climate change risk into potential lost production to smallholders ...

COMPAdRE: COMmunication and PArticipative REsilience

COMPAdRE: COMmunication and PArticipative REsilience
This project will combine participatory approach of climate services with an ICT-centered monitoring and evaluation approach to incorporate feedback mechanisms and two-way communication loops ...

Beyond: Smart IoT Solution for Plant Diagnosis

Beyond: Smart IoT Solution for Plant Diagnosis
This innovation will help farmers diagnose pests, diseases and nutrient deficiencies of rice and cassava ...

From big data to small-scale decisions: what is the impact?

From big data to small-scale decisions: what is the impact?
This innovation will develop big data-based information systems that empower farmer’s management strategies ...

Enabling real-time wheat blast management advisories in Bangladesh and Brazil

Enabling real-time wheat blast management advisories in Bangladesh and Brazil
This proposal responds to the crop disease threats by leveraging mobile microscopy, citizen science, and machine learning to enable ICT-based real-time management advisories in Bangladesh and Brazil ...