OK, Phone: How Are My Crops Looking?
Some cassava farmers may not be able to tell one plant’s debilitating brown streak from another’s troubling brown leaf spot—but a smartphone-friendly AI can.
Wired reports that researchers have developed a lightweight image-recognition AI that can identify diseases in the cassava plant based on pictures of its leaves. That could be useful, because cassava is one of the most commonly eaten tubers on the planet, but is grown predominantly in developing countries where access to expertise to diagnose unusual crop problems may be limited.
In a paper published on the arXiv, the researchers behind the new AI explain how they’ve used a technique known as transfer learning to retrain an existing image-recognition neural network using just a small number of new images. With just 2,756 pictures of cassava leaves captured from plants in Tanzania, the team was able to build software based on Google’s TensorFlow AI library that could reliably identify three crop diseases and two types of pest damage. It could, for instance, discern brown leaf spot with 98 percent accuracy. The AI is also small enough to load and run on on a smartphone and doesn't need to send data to the cloud for processing, though it isn’t yet available for people to use.
Automation increasingly appears to be heading for the field. Last month, tractor maker John Deere acquired a Silicon Valley AI firm—one that precision-targets weed killer using machine learning—for a cool $300 million. And a pilot project in the U.K. recently saw researchers tend a field full of barley using only robots.
Keep Reading
Most Popular
The inside story of how ChatGPT was built from the people who made it
Exclusive conversations that take us behind the scenes of a cultural phenomenon.
How Rust went from a side project to the world’s most-loved programming language
For decades, coders wrote critical systems in C and C++. Now they turn to Rust.
ChatGPT is about to revolutionize the economy. We need to decide what that looks like.
New large language models will transform many jobs. Whether they will lead to widespread prosperity or not is up to us.
Design thinking was supposed to fix the world. Where did it go wrong?
An approach that promised to democratize design may have done the opposite.
Stay connected
Get the latest updates from
MIT Technology Review
Discover special offers, top stories, upcoming events, and more.