Many people love to plant impatiens – also known as the “Touch Me Not” -- in their gardens, yards and patios because of their bright, colorful flowers and tolerance to shade, said UF/IFAS ornamental plant breeder Zhanao Deng. He and his research team found two New Guinea impatiens varieties that can resist the deadly downy mildew disease throughout the flower’s entire life. To reach their findings, Deng and his team tested 16 garden impatiens and 16 New Guinea impatiens varieties in Florida. Their findings showed all New Guinea impatiens varieties resisted the pathogen after their first true leaves have emerged. Scientists used DNA sequencing and bioinformatics to find genes associated with downy mildew resistance. The DNA sequencing produced large datasets. To sort through all the information and arrive at their findings, Deng and his team used the UF HiPerGator supercomputer on the main campus in Gainesville.
AI could reduce the amount farmers spray to control strawberry pest
Artificial intelligence that might use smartphone images could lead to more accurate, early detection of a pest that ruins strawberries, a $300 million dollar-a-year industry in Florida. UF scientists have developed a smartphone app to detect the twospotted spider mite. If the method works, farmers will magnify images of strawberry leaves. Then, they label images to train deep learning networks -- a form of artificial intelligence -- to recognize the mites. Once the networks are trained, they can be used to identify the pests. Quicker detection would let farmers use lower levels of chemicals that might otherwise harm the environment. As it stands, most growers spray regularly, perhaps as often as every week. Twospotted spider mites cause $28 million to $34 million of damage on Florida crops annually. Additionally, it costs between $3 million and $5 million and can be even more if the grower’s management system is set up on a weekly basis.
AI may help UF researchers identify crop-destroying nematodes
Parasitic nematodes cause $125 billion in agricultural damage around the world each year, but University of Florida scientists hope to alleviate some of that destruction. To do so, UF researchers will use artificial intelligence to try to more rapidly identify nematodes. Researchers led by Peter DiGennaro, a UF/IFAS assistant professor of entomology nematology and Alina Zare a professor in the Herbert Wertheim College of Engineering and director of UF’s Machine Learning and Sensing Lab want to use AI technology to aid farmers in their battle against nematodes. 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 and potentially save growers from using costly management methods or losing crops to undiagnosed nematode problems.
UF scientists use AI technology to breed better-tasting strawberries
A tastier strawberry may come from a computer. New University of Florida research shows artificial intelligence can help scientists breed more flavor into the fruit, which is a $300 million-a-year industry in Florida.
Vance Whitaker, a UF/IFAS associate professor of horticultural sciences, used an algorithm that gives him the ability to predict how a strawberry will taste, based on the chemical constitution of its fruit. The computer method also takes less time than volunteer test panels.
Over seven years, 384 consumers came to the UF Sensory Lab in Gainesville to give their feedback on flavor and aroma of strawberry varieties. Whitaker and his team compared consumer preferences with results that came from an established algorithm and found the volatiles he needs to boost in strawberries he breeds in the future to improve their flavor.
UF researchers use AI datasets to track feral pigs, minimize disease risk
Feral pigs cost the agriculture industry at least $1.5 billion in damage, disease and control costs around the United States annually, running rampant on large swaths of grazing lands. The swine root in soil and eat most everything in sight.
Farmers and ranchers will benefit from research by University of Florida scientists who are using artificial intelligence to gather data on feral hog reproduction and movement.
Samantha Wisely, a UF/IFAS professor of wildlife ecology and conservation, will conduct her latest feral pig research at Archbold’s Buck Island Ranch, in southeastern Highlands County. There, she’ll collaborate with station researcher Raoul Boughton, who used AI to identify pigs from millions of pictures taken by remotely triggered wildlife cameras. Using these techniques, Wisely and others will further use this AI-generated dataset of pictures to track the movements of groups of pigs, called sounders, to predict how pathogens spread from pig to pig in Florida grazing lands.
Hurricanes and Invasive Plant Spread
Scientists project hurricane intensity and frequency will increase with climate change, which warrants a better understanding and prediction of how they will affect the dispersal and establishment of non-native plant invaders. UF/IFAS researchers, led by agronomy Associate Professor Luke Flory, are using a combination of on-the-ground plant research and hyperspectral sensing to evaluate the effects of hurricanes on Brazilian peppertree and Old World climbing fern. These are two of the most widespread and problematic invaders in South Florida, including such large areas as the Big Cypress National Preserve and Everglades National parks, and hurricanes may disperse seeds to new habitats. Flory and his team want to examine the post-hurricane conditions with remotely sensed data that can be used to map the distribution of invasive plants.
UF scientists look to space to help ranchers on Earth
UF/IFAS agronomist and agroecologist Chris Wilson is leading a project to develop a tool -- based on data from satellites -- that ranchers can use to improve their pasture and grazing management. Because of the size and complexity of ranchland ecosystems, scientists need tools to help monitor and quantify key ‘vital signs’ -- such as the amount of green grass -- over large areas where traditional field scouting is impractical. To gather that large dataset, Wilson and his team intend to use multiple orbiting satellite platforms that contain decades of historical data. Wilson's team uses Landsat from NASA and Sentinel II from the European Space Agency, among others. They combine these data with field data using a combination of statistical and AI-based models, in order to generate the most accurate and useful insight possible for ranchers to use in their pasture management.
AI-based Software ‘Agroview’ named UF 2020 Invention of the Year
The University of Florida office of licensing and technology named “Agroview” as a UF Invention of the Year. UF/IFAS agricultural engineer Yiannis Ampatzidis and his research team developed this artificial intelligence technology to help farmers save money and better care for their crops. The system utilizes images from drones and satellites and from the ground – along with artificial intelligence -- to assess plant stress, count and categorize plants based on their height and canopy area and estimate plant nutrient content. Agroview can reduce data collection and analysis time and cost by up to 90% compared to the manual data collection.
Detecting Tomato Diseases with 99% Accuracy
To help growers, plant pathologist Pamela Roberts and agricultural engineer Yiannis Ampatzidis use images from drone technology – including a so-called “multilayer perceptron neural network” -- to see if they could distinguish between bacterial spot and target spot on tomatoes. The earlier farmers can detect plant ailments, the sooner they can treat them before they damage crops. Such diseases can cost growers millions of dollars annually for the state’s third most valuable crop.
Controlling the Right Weeds and Pests
You don’t necessarily want to kill all weeds and pests that are on a farm. Some are beneficial. So scientists at UF/IFAS are experimenting to see if precision agricultural techniques will surgically control weeds and pests. Horticulturalist and weed scientist Nathan Boyd works with soil and water scientist Arnold Schumann on ways to improve precision sprayers, developing new models for weed and pest detection and integrating the models into new smart sprayers.
Using AI to Detect Insects
UF/IFAS agricultural engineer Yiannis Ampatzidis sees a day when citrus farmers use artificial intelligence to detect the pin-sized insects that can infect the fruit’s trees with the deadly greening disease. Ampatzidis and his team use novel technology to tap branches and remove Asian citrus psyllids from the citrus trees. Researchers then use an artificial-intelligence-based algorithm to analyze images, detect and count the psyllids that fall from the tree. Growers will eventually use this technology to know if their citrus trees have the psyllids that cause greening.
Measuring Crop Evapotranspiration Using Sensors, Drones, and Artificial Intelligence
UF/IFAS agricultural engineer Haimanote Bayabil is leading a project to develop artificial intelligence irrigation system methodology to estimate evapotranspiration, the component in the water cycle that is critical in crop maintenance. This project will use field experiments, data analytics, hydrologic and crop modeling, and development of AI for algorithm generating to bring this science to growers and farmers in the agricultural industry. This research seeks to advance current irrigation scheduling technologies in order to provide farmers and growers precision irrigation that increases crop productivity and saves water.
Using AI for Strawberry Yield Prediction
UF/IFAS agricultural engineer Wonsuk “Daniel” Lee is working on a project to improve prediction models for strawberry yields using field images and other variables. Using computer vision and artificial intelligence, strawberry flowers and fruits are automatically detected from images acquired from the field and their numbers are counted. These numbers are used in yield prediction models. The goal of this AI research is to develop a robotic system that can autonomously navigate strawberry fields, take images, create a distribution map of flower and fruit, and predict yield.