  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.
