But Janna Beckerman from Purdue University is skeptical about how useful automatic apps can be. “With plant diseases, I can’t just look at leaves and symptoms,” she says. “I need to go to a microscope. Sometimes I need to culture things. There are apps, but we always caution people that they need to follow up with a laboratory diagnosis.”
Judy Payne from USAID is more enthusiastic. “I’m impressed by the potential to help farmers identify hard-to-diagnose plant diseases,” she says. “While this technology is no substitute for the careful eyes of trained plant pathologists, it can go a long way to alleviate the challenges of distance and access faced by many farmers in the developing world.”
Still, she adds, the “technology needs to be well-tested and adapted to rural settings.” Indeed, when the team tested their program on more realistic images, taken from books, scientific papers, and other sources, its accuracy fell to just 31 percent. When they told the program what the crop was, something that real farmers would obviously know, its accuracy rose back to 45 percent.
That doesn’t sound great, but Salathé notes that a random guess would be accurate just 2 percent of the time. And “the best comparison is against a human,” says Hughes. “Could one human get better than 45 percent on classifying 26 diseases on 14 crops? Likely no. A whole department of experts with different capabilities might, but our approach intends to give a grower in Kent, Kentucky, or Kenya that department-level diagnosis.”
The team have made their image library free to access, and they hope that computer scientists will help to devise better algorithms. And they’re working hard to gather more images—of more crops plagued by many strains of various diseases, at different points of the infection cycle, under a range of lighting conditions and backgrounds. That’s the key, Salathé says. “Currently, the algorithm doesn’t have the chance to appreciate the diversity that’s out there. All we need to do now is to add more images, and the accuracy will go up.”
Hughes has teams of photographers snapping shots of rice in the Philippines and cassava in Tanzania. “I work mainly on cassava, and there alone the techniques could benefit tens of millions of farmers who are badly affected by mosaic and brown streak diseases,” says James Legg from the Consultative Group for International Agricultural Research, and one of Hughes’ collaborators. “They cause annual losses in Africa of more than $1 billion.”
“We are aiming for 3 million images within 3 years,” says Hughes. “We’ll make them open-access. And if you want to use our images, your code has to be open-access.”
“I see great potential for use of this automatic disease diagnosis by farmers worldwide,” says Saskia Hogenhout from the John Innes Centre. “Moreover, I am sure people will volunteer to contribute images of diseases and pests to the public database making it useful for plant-health agencies to control disease and pest invasions globally.”