Australian researchers are working on the world’s first real-time monitoring platform for rice fields.

Rice is the staple crop for more than half the world’s population and 10 per cent of all arable land is dedicated to rice farming, but there are currently no accurate maps outlining rice yields and land use using consistent methodology.

“Accurate and up-to-date information on how much rice has been planted and how much harvest can be achieved is crucial to achieving global food and water security,” says project leader Professor Budiman Minasny from the Sydney Institute of Agriculture at the University of Sydney.

“Working with our partners in Asia, we will use the Google Earth Engine to build the first real-time mobile application that will allow farmers, agricultural scientists, non-government organisations and government planners to manage land use to ensure food security in the world’s rice bowls.”

The real-time land-use data generated using Google Earth will be verified by field operators in India, China, Malaysia, Indonesia and Vietnam. This will allow the agricultural scientists to calibrate monitoring to ensure its accuracy worldwide.

These five partner countries make up more than 40 percent of the world’s population. India, China and Indonesia are the world’s three-largest producers of rice and together account about 60 percent of total world production.

Jointly developed with the Universiti Malaysia Terengganu, the expers are working on a mobile monitoring app – Paddy Watch – to allow farmers, scientists and agricultural economists to:

  • determine the extent of arable land under rice cropping in near real-time
  • estimate potential yields
  • manage water use and water security
  • account for greenhouse gas emissions (paddy rice releases methane)
  • develop policies for education, economic growth, gender equity and reducing social inequality

The Paddy Watch app will build on work already undertaken in Malaysia and Indonesia by Professor Minasny and colleagues at Universiti Malaysia Terengganu.

Using the Google Earth Engine and cloud computing technology the project will improve on that work using the latest deep learning techniques to forecast crop yields and water consumption.