PlantVillage NURU: Pest and Disease Monitoring Using Artificial Intellingence
PlantVillage NURU: Pest and Disease Monitoring Using Artificial Intellingence
Technical Specification
Technology Readiness Level
TRL 9
Farm Scale Types
Big scale, Small scale
Production Systems
Open field
Technique Types
Diagnostics and detection techniques, Monitoring techniques
Application Ranges
Others
Company Name
CGIAR
Country Origin
United States
Contact Person
dph14@psu.edu; J.LEGG@cgiar.org
Special Requirement
Need for a special agricultural landscape
Need Special Training

Description

This 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 allows 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.
For More Information
Visit Website
Crops Used
  • Bell pepper
  • Maize
  • Potato
Crops Possible
  • Bell pepper
  • Brussels sprouts
  • Cauliflower
  • Cucumber
  • Head cabbage (white, red, savoy)
  • Leek
  • Lettuce
  • Onion
  • Tomato
Countries Used
  • Many
Tech Requirement Comment

Smartphone-based runs on iOS and Android.

Landscape Comment

No restrictions

Training Comment

No comment

Cost Detail

Free app

Example Cases/Additional information

The team annotated more than 200,000 cassava plant images, identifying and classifying diseases to train a machine learning model.

Mode of operation

Cloud based