Researchers use AI tools to predict beetle spread and help farmers protect crops
According to the University of Hawaiʻi at Mānoa College of Tropical Agriculture and Human Resilience, research led by Dr. Pascal Aigbedion-Atalor is using machine learning to identify areas at risk from the coconut rhinoceros beetle and support early pest management efforts.
The spread of invasive pests continues to create challenges for farmers across the United States. To help combat one growing threat, researchers are using machine learning technology to predict where harmful insects could appear next and provide farmers with valuable time to respond.
“Stopping invasive pests before they spread is one of the best ways to protect American agriculture,” said Dr. Kathy Munkvold, FFAR scientific program director for Cultivating Thriving Production Systems Priority Area. “This research gives farmers a head start against a devastating introduced pest, helping to protect crops, livelihoods and the future of Hawaii’s agriculture.”
A new project funded by the Foundation for Food & Agriculture Research (FFAR) and conducted by scientists at the University of Hawaiʻi is focusing on the coconut rhinoceros beetle, a destructive insect that threatens crops, landscapes, and local economies.
The research team has received $300,000 through FFAR’s Rapid Outcomes from the Agricultural Research (ROAR) program. Their goal is to develop advanced computer models capable of predicting where the beetle is most likely to spread in the future.
The coconut rhinoceros beetle is originally from Southeast Asia and has become a serious concern in Hawaii. Since first being detected on Oahu in 2013, the pest has spread to several islands and continues to threaten agricultural production and natural landscapes.
“Stopping invasive pests before they spread is one of the best ways to protect American agriculture,” said Dr. Kathy Munkvold, FFAR scientific program director for Cultivating Thriving Production Systems Priority Area. “This research gives farmers a head starts against a devastating introduced pest, helping to protect crops, livelihoods and the future of Hawaii’s agriculture.”
The beetle damages palm trees and other plants by boring into them and feeding on developing plant tissue. As a result, affected trees often produce fewer healthy leaves and fruit. The pest also threatens banana production, plant nurseries, landscaping operations, and tourism-related landscapes.
Researchers believe that better forecasting could make a significant difference in reducing damage. By using machine learning algorithms, scientists can analyze environmental conditions that favor the beetle’s survival and reproduction. These systems can identify patterns that help predict future infestation hotspots.
The project is creating detailed risk maps showing areas that may be vulnerable to beetle activity. These maps will be used to guide monitoring efforts and improve pest management strategies.
One key benefit of the research is the ability to place monitoring traps in the most effective locations. The traps use pheromones to attract beetles and help detect infestations before they become widespread. Early detection allows farmers and agricultural officials to act quickly and reduce economic losses.
According to researchers, preventing pests from spreading is often far less expensive than controlling established infestations. Early intervention can protect crops, reduce treatment costs, and improve long-term farm profitability.
The project could also have benefits beyond Hawaii. Invasive pests cause major economic losses throughout U.S. agriculture, costing producers an estimated $137 billion every year. The methods developed through this research could eventually be adapted to monitor and manage other invasive pests affecting crops across the country.
Food security is another important concern. Hawaii imports much of its food supply, making local agricultural production especially valuable. Protecting farms from invasive pests can help strengthen local food systems and support agricultural resilience.
Economic forecasts show the importance of taking action. Estimates suggest continued spread of the coconut rhinoceros beetle could result in losses ranging from $500 million to $1 billion over the next decade. Researchers hope their predictive tools will help reduce those risks by providing advance warning and improving monitoring programs.
The project also supports the U.S. Department of Agriculture’s priority of protecting American agriculture from invasive species. By combining agricultural expertise with advanced technology, scientists aim to provide practical tools that help farmers make informed decisions.
As invasive pests continue to threaten crops and rural economies, machine learning is becoming an increasingly valuable tool. The ability to predict pest movement before outbreaks occur could help farmers protect crops, improve profitability, and strengthen the future of agricultural production.
Photo Credit: Foundation for Food & Agriculture Research