“We're able to tell them they have a problem, and with the AI assistant go into the field and see if that problem is a disease that's causing a decrease in the yield or it's actually drought,” Hughes says. This is particularly useful because the drought problem and the pest problem are intimately linked: Warm weather conditions lead some insects to breed more prolifically. In western Kenya, farmers have historically relied on predictable rains. So every January, farmers would begin preparing their fields, then sow their seeds in February, then expect rains in March. “Of course, this was information communicated over history,” says plant pathologist John Chelal, who grew up not far from where he and Hughes are testing the new system. “The common response I hear is, we don't know anymore. The rains have become quite unpredictable.” With this satellite data, though, they can react before it’s too late. “Maybe think about putting up some irrigation facility or organizing how they can even manually apply water,” Chelal says. “This is a really objective value. They can't see it with their eyes, but they can get a figure.” That value comes from satellites that are looking at a couple of things. For one, they’re using near-infrared sensors to measure land surface temperature, which helps calculate soil moisture. This helps form what’s known as the normalized difference vegetation index. “It can be used to see how healthy a crop is, and it can tell you what part is water and what part is bare soil, and so on,” says Jippe Hoogeveen, senior land and water officer with the UN’s Food and Agriculture Organization, who’s working with PlantVillage. At the moment this data doesn’t come at a particularly high resolution—pixels of 100 meters square. “But at the same time, the good thing about satellite data is you have consistent data over time and space,” says Livia Peiser, FAO technical land and water officer. “So you can very easily compare the trend of your pixel in the last 10 years.” Plus, that resolution will improve: In the near future, Peiser and Hoogeveen expect new satellites to sharpen the resolution to 10 meters square, giving farmers ever-better granularity. Another issue at the moment is that it’s hard for the satellites to parse the subtleties of this kind of small farming. For instance, the two staple crops in western Kenya are maize and cassava, which are often planted together. Farmers might harvest the maize first, thus changing the biomass signature of the field, while leaving the cassava to continue growing. They might also leave plant residue on the ground after harvesting the maize, further complicating the signature. “It's not going to be a perfect science,” Hughes says. “But at least it will be a science that is now involving the farmers, as opposed to what it's been traditionally, which is just a bunch of academics providing these tools for broader-scale policymakers to understand.” Satellite data won’t get us out of the mess we’ve made for ourselves. But it may well help the most vulnerable adapt to a climate gone mad. More Great WIRED Stories
Researchers from Penn State University have developed an app which can be used to diagnose crop health through photos. The app, called PlantVillage, uses artificial intelligence to analyse individual plants and provide information on how to treat them. The system also uses UN satellite data to monitor biomass on a plot of land, allowing small-scale farmers to assess the growth of their crops and compare their performance either historically or against the results of other local farmers.