Late blight is a frequent illness of crops comparable to tomatoes and potatoes, able to wiping out whole crops on commercial-scale fields. Caused by a fungus-like pathogen, it first seems as black or brown lesions on leaves, stems, fruit or tubers. If circumstances are favorable, it may possibly shortly unfold to different crops via moist soil and as wind-scattered spores.
In the mid-nineteenth century, late blight famously brought on the Irish potato famine. Today it nonetheless causes greater than 6.7 billion in annual losses worldwide. Small farms and natural growers are sometimes the toughest hit, as a result of they’ve fewer sources to establish and deal with the illness.
But farmers might have a new weapon to add to the arsenal. The expertise, designed by researchers at North Carolina State University, depends on the science of delicate plant odors, and it may possibly acknowledge sick crops early by using a easy take a look at strip that plugs into a reader on a smartphone.
Plants emit signaling chemical substances from their leaves, not in contrast to the pheromones launched by people. “If a plant is diseased, the type and concentration of these volatile organic compounds changes,” stated Qingshan Wei, a biomolecular engineer at NC State University. By sampling a plant’s emission profile, a farmer can assess whether or not or not a pattern of their crop is contaminated, Dr. Wei stated.
[Like the Science Times page on Facebook. | Sign up for the Science Times newsletter.]
If the farmer suspects a late blight infection is underway, she can remove a leaf from a living plant and place it in a small, covered glass jar. After the leaf’s volatile compounds have accumulated for 15 minutes or so, the cap is removed and the air is pumped from the jar into a reader device attached to the back of a smartphone.
Inside the smartphone reader is a strip of paper specially treated with organic dyes and nanoparticle sensors developed by the researchers. Upon interacting with the plant’s volatile compounds, the strip changes color to indicate the presence or absence of the pathogen. It’s like a home-pregnancy kit for tomatoes, or a strep test for tubers.
In proof-of-concept testing, Dr. Wei and his team found that the technology could accurately detect changes in 10 different plant odor molecules just two days after plants were inoculated with the pathogen that causes late blight, even before the effects were visible to the eye. The team’s results were published Monday in the journal Nature Plants.
The test can also distinguish between late blight infections and other tomato diseases that appear similar, such as Septoria leaf spot and a fungus that causes “early blight.”
Currently, farmers must send leaf samples to specialized labs if they can’t identify the onset of disease by eye. This costs more and delays identification of the disease, increasing the odds that the pathogen will spread.
“Our technology uses tools that farmers already have in their pockets — their smartphones,” Dr. Wei said.
Dr. Wei estimated that the team’s prototype reader cost about $60 to make in the lab. The researchers used a 3-D printer to create a reader cartridge that fits on the back of a smartphone and allows plant odors to interact with test strips in a leak-free space. The test strips had to be designed with sensors and dyes that would remain stable for a long period of time and cost just a few cents per strip, Dr. Wei said.
Other research groups have developed portable lab-on-a-chip devices that can do similar pathogen tests. But few are as simple or as cost-effective as the new smartphone-based technology, Dr. Wei said. His team plans to conduct more field tests before seeking out manufacturing partners.
They hope to customize the technology for other crop pathogens, which continue to emerge as climate change and global trade increase the stress on agricultural systems. “This is an important step in improving global food security,” he said.
Get more stuff like this
Subscribe to our mailing list and get interesting stuff and updates to your email inbox.
Thank you for subscribing.
Something went wrong.