Researchers at Pennsylvania State University (PSU) say they have designed a smartphone-based neural-network program that can automatically identify diseases in the cassava plant with near-flawless accuracy.
The network is based on Google's open source TensorFlow machine-learning library, and Google's Pete Warden notes the TensorFlow Mobile app requires only about 25 million parameters, versus the hundreds of millions some networks need. "It only requires about 11 billion floating point operations to actually calculate its result, and some other networks require hundreds of billions of operations to do a similar job," Warden says.
He says that thanks to transfer learning, the network was trained to recognize cassava leaves on much less data. "It really comes down to the data, because garbage in, garbage out," says PSU's Amanda Ramcharan. She believes the growing affordability of smartphones and the continuing simplification of algorithms will combine to make such tools more widely available to farmers.
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