2017 & 2019 Winner

PlantVillage Nuru: AI for pest & disease monitoring

Kenya

This Inspire Challenge proposal was selected as a 2017 pilot project and 2019 scale up winner, receiving a total of US$ 350,000 to put their ideas into practice. 

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 agricultural 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.
  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.

 

Team

Jan Kreuze | Email
CIP

David Hughes | Email
Penn State

James Legg | Email
IITA

Step by step

9

2021

2021 PROJECT UPDATES COMING SOON!

9

2020

JAN 2020

Integration with SMS system for greater AI reach

PlantVillage Nuru integrated with a hybrid information dissemination platform that uses an SMS system to send agronomic advice, enabling the reach of AI to be greater within Kenyan communities. The integrated platform system is used by young agronomists from PlantVillage’s Dream Team.

FEB - MAY 2020

Contextualized advice for changing climate adaptation

The effects of climate change observed in 2019 has demonstrated that advice on disease pressure must be contextualised within the context of climate change. The team integrated weather and past crop performance with available water resources to deliver contextualized advice.

Integration with epidemiology platform to track cassava disease

PlantVillage Nuru will be integrated with WAVE 2 (West African viral epidemiology platform) to help track and arrest the spread of cassava brown streak disease. This work began in January 2020 and will continue for the next three years.

 

Expanding agronomic advice to wheat and rice diseases

The team began work with CIMMYT to expand the app’s agronomic advice to include wheat and rice fungal diseases, training a TensorFlow model for the app to recognize symptoms of wheat rust and blast.

FEB 2020

Nuru’s AI tech used as a model for nutrition project in Ghana and Vietnam

Foundation Botnar will fund a new project with IFPRI to utilize PlantVillage Nuru’s AI to automate food tracking information needed by nutritionists.

The project will test the proposition that well-designed smartphone apps using AI can be a social force for positive eating habits, specifically for adolescent girls ages 15 to 17 years in schools Ghana and Vietnam.

MARCH 2020

** COVID-19 ADAPTATION **

The PlantVillage Nuru team moved all of their in-person fieldwork to remote in line with COVID-19 restrictions and to safeguard the health and safety of their staff and farmers. They continued their work remotely using their newest addition to the PlantVillage web platform, Ag3 Observatory, which allowed their Dream Team to see the issues farmers are facing in the field and then follow up with a phone call.

9

2019

OCT 2019 - Project awarded US$250K Inspire Challenge scale-up grant

The project was awarded a 2019 Inspire Challenge Scale-up grant of US$250K at the third annual convention of the CGIAR Platform for Big Data in Agriculture, 16-18 October 2019.

9

2018

JUNE 2018 - App made available on Google Play

Nuru AI was made freely-available for download onto Android devices through Google Play, June 2018. It has been downloaded by users on all continents and used extensively in Africa and South-East Asia. This is the first stage of the application roll-out; additional funding would allow roll-out at a broader scale.

OCT 2018 - Expansion to other crop diseases

Women potato farmers from the Dedza district in Malawi receive new and improved potato varieties from the International Potato Center. Photo: Hugh Rutherford/International Potato Center.

 

Women potato farmers from the Dedza district in Malawi receive new and improved potato varieties from the International Potato Center. Photo: Hugh Rutherford/International Potato Center.

While the project focused on cassava disease detection, it also trained an accurate model for potato diseases which has been field-tested and was deployed in India in October 2018 with 1,000 farmers as part of the Indian Government’s FarmerZone platform. The application is available in Hindi and Punjabi and will scale up its reach to 200,000 farmers within 24 months.

In collaboration with the United Nations Food and Agricultural Organization (FAO), the team has developed a tool to identify fall armyworm damage, and images of banana and sweet potato diseases are also being collected for future work.

The fall armyworm tool has demonstrated successful deployment of AI to farmers; it has been used in 66 countries and 28 languages by the UN FAO.

9

2017

Project awarded US$100K Inspire Challenge grant

The project was one of five winners of the Inspire Challenge 2017 and was awarded US$100K at the inaugural annual convention of the CGIAR Platform Big Data in Agriculture, 19-22 September 2017.

 

Developing a mobile AI assistant to diagnose cassava diseases offline

The team successfully developed a mobile AI assistant that works inside a standard smartphone and is capable of accurately diagnosing cassava diseases offline, without an internet connection. The assistant is called Nuru, which means ‘light’ in Swahili.

Under field conditions, Nuru was, on average, twice as accurate as the extension workers she was tested against.

Nuru is linked to PlantVillage and allows advice from experts (at CGIAR/FAO/governments) to be sent offline and in local languages (currently in Swahili, French, Twi, Hindi, and English).

Training the convolutional neural network

The AI relies upon TensorFlow, a machine learning environment where a convolutional neural network (CNN) is trained to recognize crop diseases based on images collected at IITA research plots.

The project overcame a number of significant challenges in getting an accurate CNN to work offline inside an Android app; in-phone CNN deployment is not yet a standard approach and most systems rely upon the cloud, which would not be functional in a smallholder farmer setting where connectivity is poor. The CNN is a work in progress and will improve over time with more training.

Building an expert portal on the PlantVillage platform

The project also built an expert portal on the PlantVillage platform for IITA scientists to examine records of diseases diagnosed by Nuru. Experts are automatically notified when a user receives a diagnosis, and they are shown the images and data (location, AI accuracy, time) associated with the diagnosis. This allows IITA to both check the accuracy of the AI assistant and note where and when diseases are being recorded.

An example of the portal in action was the identification of Cassava Mosaic Disease (CMD) in Oddar Meanchey Province, Cambodia where there had previously been only unconfirmed reports. The expert portal enabled IITA’s James Legg to interact directly with the Nuru user in Cambodia and discuss monitoring and control of CMD. This was a clear demonstration of Nuru’s potential to provide early warning support for the identification and management of new crop disease outbreaks.

Testing the system with farmers in Busia County, Kenya

Cassava Lab Uganda 16 Credit-S.Quinn_CIP

The system has been tested with farmers in Busia County, Kenya in partnership with Self Help Africa, a charity helping 28,000 farmers in the cassava value chain.

UPDATE: In Busia County, Kenya, PlantVillage is working with over 100 Lead Farmers who received smartphones at the beginning of 2019. Each Lead Farmer visits 20-40 Following Farmers per month to check on the health status of their farms using the Nuru application. PlantVillage has seen extraordinary engagement using Nuru to diagnose crop diseases and pests.

AI takes root

The team annotated thousands of cassava plant images, identifying and classifying diseases to train a machine learning model using TensorFlow. Once the model was trained to identify diseases, it was deployed in the app. Farmers can wave their phone in front of a cassava leaf and if a plant had a disease, the app could identify it and give options on the best ways to manage it.

For more information on how Nuru is helping farmers better identify and manage diseases quickly, watch Google’s video spotlight on the project below and read more on Google’s blog, The Keyword.

Partners

Project News and Resources

Success story from a cassava farmer in Busia County, Kenya

23 Mar 2020
PlantVillage

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AI takes root, helping farmers identify diseased plants

20 Jun 2018
The Keynote

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Grant will support expanded use of artificial intelligence for crop health

26 Nov 2019
Penn State News

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Revolutionary mobile app for monitoring crop pests and diseases

19 Sep 2017
RTB Blog

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The Bots Are Here—and They’re Protecting Our Crops

2 Sept 2019
Scientific American

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Project Brief

RTB – CGSpace

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