James Cook University researchers are developing a new tool to help farmers monitor crop health and accurately detect diseased sugarcane before it shows any visible symptoms, thereby avoiding expensive DNA analysis.
A new crop health monitoring tool being developed by JCU's Professor Mostafa Rahimi Azghadi's team utilises free satellite data and AI technology to assess the health of sugarcane crops.
The team's latest research, published in the journal Information Processing in Agriculture, describes the testing of the tool's ability to accurately determine the difference between healthy and diseased sugarcane, being the first to use satellite data to target 'asymptomatic' Ratoon Stunting Disease (RSD).
"RSD can affect the yield of sugar by up to 60 per cent and it's highly contagious. But being asymptomatic, you can't see it with the naked eye until the latter stages of the growing season," Prof Azghadi said.
"Depending on the sugarcane variety, our method was between 86 and 97 per cent accurate … which is on par or better than other crop disease detection tools."
Farmers usually identify RSD by cutting and sampling sugarcane by hand, then sending juice samples off to laboratories for DNA analysis.
"It's time consuming and expensive, especially if you want to do it at larger scale as every test costs about 10-15 dollars," said Prof Azghadi.
Prof Azghadi's team worked with Herbert Cane Productivity Services, who provided on-the-ground data about the disease prevalence in the Herbert River region.
"They provided data on both diseased and disease-free plants, which has been critical in helping us develop our technology," said the paper's first author and JCU engineering graduate, Ethan Waters.
Using freely available multi-spectral data from a European satellite called Sentinel-2, researchers tested a range of different vegetation indices and developed their disease detection tool using an AI training technique called machine learning.
"There are subtle differences between a healthy crop and a diseased crop. The naked eye can't see all the subtleties and only a well-trained machine learning algorithm can spot those differences," said Mr Waters.
Prof Azghadi explained that the research is supported by Australia's economic accelerator (AEA) program for technology driven solutions in farming, a new way the Australian Government is connecting university researchers with industry.
"RSD in sugarcane is just our first successful case study … our approach can be extended to other crops and other crop health challenges," he said.
"The long-term objective is to develop an early-warning tool that identifies disease risk and tracks overall crop health, making it easier to manage the health and vitality of farmer's crops.
"It'll be a bit like a regular check-up with your GP, but for sugarcane and other crops."
