# AI-LyD: An AI-Driven System Approach to Combatting Spotted Lanternfly Proliferation Through Behavioral Analysis

## TL;DR

AI-LyD is a new system that uses AI and insect behavior to control spotted lanternfly populations with low cost and high effectiveness.

## Contribution

AI-LyD integrates AI, insect behavior, and low-cost traps to create a scalable and sustainable pest management system for spotted lanternflies.

## Key findings

- AI-LyD reduced spotted lanternfly populations by 91% in field trials.
- The Aquabex water-barrier trap costs less than 50 cents per unit and traps 85% of nymphs.
- Incorporating SLF behavior into AI models improved detection accuracy to 96% in drone imagery.

## Abstract

The spotted lanternfly (SLF, Lycorma delicatula), is an invasive insect causing serious damage to agricultural industries and natural ecosystems. Current control methods either harm beneficial pollinators and pollute the environment, are expensive to maintain, or are inefficient at larger scales. Many applicational technologies (ex. artificial intelligence) used in combatting SLF also fail to utilize SLF’s unique behaviors. This study introduces AI-LyD, a novel integrated pest management (IPM) framework that combines insect behavior, artificial intelligence, and low-cost physical controls to manage SLF populations. The system predicts where SLF are most likely to spread, detects them automatically in drone-collected images, and reduces the bugs’ movement through the novel Aquabex water-barrier trap, which costs less than 50 cents per unit. When deployed across trial locations, AI-LyD reduced SLF populations by 91%. This work demonstrates that integrating SLF behavior into AI-based applications and solutions can provide a scalable, sustainable way to control SLF invasions.

The spotted lanternfly (SLF, Lycorma delicatula) is an invasive planthopper causing severe agricultural and environmental damage in 20 U.S. states. SLF control remains constrained by (1) overreliance on broad-spectrum pesticides that harm nearby ecosystems, (2) inefficiency and ecological risk of alternative methods, and (3) underutilization of SLF behavioral traits and artificial intelligence (AI) in IPM. This study introduces AI-LyD, an AI-driven IPM framework integrating behavioral ecology, predictive modeling, image-based detection, and low-cost physical controls. Incorporating SLF behavioral constraints, including cold-exposure requirements for egg hatching, into ecological models improved prediction accuracy (AUC = 0.821, Sensitivity = 0.888, Kappa = 0.642) and reconstructed SLF distributions consistent with current proliferation trends. A YOLO-based detection model leveraging SLF clustering behavior improved identification accuracy from 84% to 96% and reduced false positives from 42% to 8% in real-world drone-collected imagery. Exploiting SLF crawling, jumping, and hydrophobic behaviors, the novel Aquabex water-moat device with an optimized 60° opening trapped 85% of Stage I–IV nymphs and reduced adult invasions by 67%, at an estimated cost below USD $0.50 per unit. Field deployments across four locations in Hunterdon County, New Jersey, achieved a 91% population reduction (95% CI: 90.1–92.0%). Together, these results establish AI-LyD as the first operational, scalable SLF IPM system, and this paradigm can be applied to controlling other invasive species.