AI and Aerial Imaging Guide Efficient Hemp Fertilization
A recent University of Florida study highlights how drones, combined with artificial intelligence, can help farmers improve hemp cultivation by monitoring crop health.
Conducted by researchers at the UF Institute of Food and Agricultural Sciences (UF/IFAS) Tropical Research and Education Center (TREC) in Homestead, the study used drone-based imaging to determine optimal nitrogen fertilizer levels for hemp growth and flower production.
“Farmers are looking for ways to assess their crops throughout the year to make informed fertilizer decisions,” said Zack Brym, associate professor of agronomy at UF/IFAS TREC.
The team used drones equipped with multispectral cameras to capture aerial images of hemp fields. These images, taken one month before harvest, revealed differences in plant health based on color and size, helping identify the most effective nitrogen levels.
“We’ve shown that farmers with access to aerial images using red and near infrared (NIR) detection can spot differences in plant health by their color when scanning their fields,” Brym explained.
The study focused on a floral hemp variety called ‘Wife,’ grown over three years with six different nitrogen rates. Results showed that moderate nitrogen application—112 to 168 kilograms per hectare (about 100 to 150 pounds per acre)—produced the healthiest plants and highest flower yields.
“Since nutrients move through the soil so fast in Florida, farmers are applying fertilizer multiple times each year. Technology like the use of drone imaging will help determine how much fertilizer might be needed mid-season, promoting more efficient use of resources and supporting sustainable farming practices,” Brym added.
To enhance analysis, researchers used AI to process drone images and assess canopy reflectance, which measures how much light is reflected by the plants. This helps map and evaluate plant health more accurately.
“I was eager to see how effective the automated AI would be at identifying hemp canopy,” said Tamara Serrano, a lead author and former agroecology graduate student on Brym’s team. “To my surprise, the process wasn’t as seamless as expected and required manual corrections to address errors in canopy identification.”
Despite challenges, the study confirmed a strong link between canopy size and biomass, supporting drone imaging as a valuable tool for sustainable hemp farming.