bioWatch: a computer vision system for community-based identification and reporting of spotted lanternfly life stage

• Developed bioWatch mobile app for invasive spotted lanternfly detection. • YOLOv8 model trained on 1161 images; Overall precision of 0.854 achieved. • Early and late nymph stages classified with mAP@50 above 0.800. • App integrates geospatial mapping and AR to support citizen science. • bioWatch facilitates K–12 education on invasive species monitoring. Invasive species such as the Spotted Lanternfly (SLF, Lycorma delicatula ) pose significant ecological and economic threats worldwide. This study introduces bioWatch, an AI-powered mobile application designed to address the challenges of invasive species detection, monitoring, and management. The application utilizes a YOLO11n deep learning model, trained on 1161 annotated images, to classify SLF across three life stages: adult, early nymph, and late nymph. In testing, the model achieved an overall precision of 0.854 and an mAP@50 of 0.589. Early and late nymph stages demonstrated strong classification performance, with mAP@50 values of 0.834 and 0.811, respectively. Although the recall for the early nymph stage was lower (0.600), the model exhibited robust performance for adults and late nymphs (recalls of 0.730 and 0.896, respectively). The egg stage, while included in training, showed limited detection performance (precision of 1.000, recall of 0.000) due to severe class imbalance in the dataset (only seven annotated instances), rather than limitations of the model architecture. This model serves as a baseline to support the deployment of the bioWatch application, which is designed for modular integration and future updates. bioWatch integrates geospatial mapping, augmented reality, explainable AI visualization, and gamification features to enhance user engagement, with early user feedback highlighting its potential for biodiversity monitoring and K–12 education.