LANTERN-RD: Enabling Deep Learning for Mitigation of the Invasive Spotted Lanternfly

TL;DR

This paper introduces LANTERN-RD, a comprehensive image dataset for the invasive Spotted Lanternfly, along with a CNN-based classification model and a mobile app to aid in detection and mitigation efforts.

Contribution

It provides the first curated dataset for SLF and its look-alikes, enabling computer vision research and practical applications for invasive species management.

Findings

The dataset includes diverse images with varied lighting and backgrounds.

A VGG16-based model demonstrates effective classification performance.

A mobile app prototype supports real-time SLF detection for public use.

Abstract

The Spotted Lanternfly (SLF) is an invasive planthopper that threatens the local biodiversity and agricultural economy of regions such as the Northeastern United States and Japan. As researchers scramble to study the insect, there is a great potential for computer vision tasks such as detection, pose estimation, and accurate identification to have important downstream implications in containing the SLF. However, there is currently no publicly available dataset for training such AI models. To enable computer vision applications and motivate advancements to challenge the invasive SLF problem, we propose LANTERN-RD, the first curated image dataset of the spotted lanternfly and its look-alikes, featuring images with varied lighting conditions, diverse backgrounds, and subjects in assorted poses. A VGG16-based baseline CNN validates the potential of this dataset for stimulating fresh computer vision applications to accelerate invasive SLF research. Additionally, we implement the trained model in a simple mobile classification application in order to directly empower responsible public mitigation efforts. The overarching mission of this work is to introduce a novel SLF image dataset and release a classification framework that enables computer vision applications, boosting studies surrounding the invasive SLF and assisting in minimizing its agricultural and economic damage.