Machine learning in agriculture: Scientists are teaching computers to diagnose soybean stress
Iowa State University scientists are working toward a future in which farmers can use unmanned aircraft to spot, and even predict, disease and stress in their crops. Their vision relies on machine learning, an automated process in which technology can help farmers respond to plant stress more efficiently.
Arti Singh, an adjunct assistant professor of agronomy, is leading a multi-disciplinary research team that recently received a three-year, $499,845 grant from the U.S Department of Agriculture's National Institute of Food and Agriculture to develop machine learning technology that could automate the ability of farmers to diagnose a range of major stresses in soybeans. The technology under development would make use of cameras attached to unmanned aerial vehicles, or UAVs, to gather birds-eye images of soybean fields. A computer application would automatically analyze the images and alert the farmer of trouble spots.
"At its most basic, machine learning is simply training a machine to do something we do," Singh said. "When you want to teach a child what a car is, you show them cars. This is what we're doing to train computer algorithms, showing a large number of images of various soybean stresses to identify, classify, quantify and predict stresses in the field."
The research team has assembled an enormous dataset of soybean images, some healthy and some undergoing stress and disease, which they then labeled. A computer program goes through the labeled images and assembles algorithms that can recognize stress in new images. Singh said the machine learning program could be capable of spotting a wide range of common soybean stresses, including fungal, bacterial and viral diseases, as well as nutrient deficiency and herbicide injury.