Imagine walking into a field, plucking a leaf off a grapevine, scanning it and knowing within seconds if the plant is healthy, needs fertilizer or is showing signs of stress.

A team from the Department of Biological and Agricultural Engineering at University of California, Davis, has done just that.

Meet the Leaf Monitor, a mobile tool paired with a handheld spectrometer backed by artificial intelligence and predictive modeling that could revolutionize how farmers monitor and manage crop decisions by providing real-time nutrition and trait information in the field.

Researchers at University of California, Davis, are developing a chatbot powered by Artificial Intelligence to help growers, backyard gardeners, landowners and others identify and treat weeds. 

WeedChat will have access to and train itself on a broad array of papers, books, journals and other data that allow it to provide real-time accurate, comprehensive information about weeds and invasive plants, said Mohsen Mesgaran, an assistant professor in the Department of Plant Sciences.  

To manage johnsongrass, a noxious weed that crowds out cotton and sickens horses, farmers have tried herbicides, burning and hand-pulling. Now, researchers at University of California, Davis, have developed a more high-tech weapon against the invasive weed: artificial intelligence and machine learning.