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The Spotted Lanternfly (SLF) is an invasive planthopper that threatens thelocal biodiversity and agricultural economy of regions such as the NortheasternUnited States and Japan. As researchers scramble to study the insect, there isa great potential for computer vision tasks such as detection, pose estimation,and accurate identification to have important downstream implications incontaining the SLF. However, there is currently no publicly available datasetfor training such AI models. To enable computer vision applications andmotivate advancements to challenge the invasive SLF problem, we proposeLANTERN-RD, the first curated image dataset of the spotted lanternfly and itslook-alikes, featuring images with varied lighting conditions, diversebackgrounds, and subjects in assorted poses. A VGG16-based baseline CNNvalidates the potential of this dataset for stimulating fresh computer visionapplications to accelerate invasive SLF research. Additionally, we implementthe trained model in a simple mobile classification application in order todirectly empower responsible public mitigation efforts. The overarching missionof this work is to introduce a novel SLF image dataset and release aclassification framework that enables computer vision applications, boostingstudies surrounding the invasive SLF and assisting in minimizing itsagricultural and economic damage.

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