Parasitic nematode damage in agriculture totals $125 billion around the world each year. University of Florida Institute of Food and Agricultural Sciences (UF/IFAS) researchers hope to alleviate some of that destruction.
The UF/IFAS scientists will use artificial intelligence (AI) to try to more rapidly identify nematodes. Some nematodes live in the ground and harm plants, while others are beneficial, so it is important to distinguish which ones are which, said Peter DiGennaro. DiGennaro is a UF/IFAS assistant professor of entomology and nematology.
The research, led by DiGennaro and Alina Zare, is among 20 projects to get $50,000 each last year through the UF Artificial Intelligence Research Catalyst Fund. Zare is a professor in the Herbert Wertheim College of Engineering and director of UF’s Machine Learning and Sensing Lab.
“We have the AI algorithms already developed but not for this issue,” Zare said. “We will need to apply them to the nematode imagery and further develop and validate the algorithm for this issue.”
Growers need a quick way to identify plant parasitic nematodes in their soil to decide on a course of treatment, DiGennaro said. Artificial intelligence might help with this initial diagnosis of the nematode, making it quicker and cheaper to know what types of nematodes are in their fields. That could potentially save growers from using costly management methods or losing crops to undiagnosed nematode problems.
DiGennaro’s colleagues at the UF/IFAS Nematode Assay Lab receive about 7,000 samples each year from commercial growers, residents and golf courses in Florida. Lab specialists plan to view each sample with a digital microscope, which would capture about 15,000 images per sample, DiGennaro said. This can generate hundreds of thousands of images each year.
As it stands, when the lab receives a soil sample, specialists extract the nematodes from the soil and view them under a microscope. They identify each kind of harmful nematode, count how many of them there are and assess the potential for plant damage from nematodes.
AI technology has the power to automate some of the processes currently being done manually, Zare said. To speed up the nematode identification process, DiGennaro and Zare will create a machine-learning algorithm. The algorithm will speed up the accurate identification of parasitic nematodes. If the project sees success, scientists could also tell growers which management practices would be most suitable to use to protect their crops.
“If artificial intelligence helps make nematode identification accurate and practical, it might reduce the lab’s labor costs and decrease turnaround time for nematode diagnosis,” said Billy Crow, UF/IFAS professor of nematology and director of the UF/IFAS Nematology Assay Lab. “The quicker we can tell a grower what is going on, the quicker they can do something about it.”
Source: University of Florida Institute of Food and Agricultural Sciences
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