Leaf Monitor, a mobile tool using artificial intelligence and predictive modeling, is being trialed in California to deliver real-time data on leaf nutrition and traits in the field.
"Having this information is very valuable for the farmers," said Alireza Pourreza, associate professor of Cooperative Extension and director of the Digital Agriculture Laboratory at the University of California, Davis. "In five seconds, they can have a sense of how much nutrition they have in a leaf."
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The model was funded by the U.S. Department of Agriculture's National Institute of Food and Agriculture's HiRes Vineyard Nutrition project, its Animal and Plant Health Inspection Service, and the California Table Grape Commission. Maha Afifi, director of viticulture research at the commission, said: "The evaluation of vine nutrient status is one of our top priorities. At the same time, exploring new technology tools like this project is a high priority for us because they will be important to the future of the table grape industry."
Testing and function
The tool uses a handheld spectrometer to measure leaf reflectance beyond visible light. Spectral data are uploaded to a cloud-based system trained with thousands of leaf samples, mainly grapevines and almonds. These were chemically analyzed to build the prediction model over five years.
"Nutrient deficiencies in plants often go unnoticed until late in the season, by which point the damage is already irreversible," said graduate student Parastoo Farajpoor. "This is why early detection is essential. Spectrometry provides a rapid and reliable way to identify these deficiencies before visible symptoms appear."
At Bullseye Farms in Yolo and Solano counties, Irrigation Manager Geoff Klein noted potential cost savings. "Right now, it doesn't really make sense to go out and take tissues in every single corner just because it's expensive. It'd be really cool if I could just walk out there and test a couple of different places."
Management applications
Leaf sampling typically takes up to two weeks for lab results. Klein added, "I feel like there are a lot of times we do need to put less [fertilizer] on, where we end up putting more, because that's what the nitrogen removal formula says. But with this app, we can use less because we know the actual conditions at the time."
The app can also map spatial variability. "What we know is every field has variability that is not necessarily visible to the farmer's eye," Pourreza said.
The Leaf Monitor prototype is available for free on the Digital Agriculture Laboratory website and requires a spectrometer. Current accuracy is around 65% across traits, with nitrogen and phosphorus results higher. A web version is in development.
For more information:
University of California, Davis campus
Tel: +1 530 752 1011
www.ucdavis.edu