Greetings Everyone, 

We are so excited to share details of our WILDLABS AWARDS project "Enhancing Pollinator Conservation through Deep Neural Network Development" and establish a place to share details along the way and gain input from this phenomenal collective in conservation tech! To start, below is a brief description of the project and our objectives. We look forward to sharing more details as they unfold!

--------

Project Description: Ocean Science Analytics (emerging from underwater environments to assist soil-side) in collaboration with The San Diego Pollinator Monitoring Program (SDPMP), led by Christina Simokat, will be developing deep neural network to detect pollinators within video data. The SDPMP conducts surveys of plant-pollinator networks in San Diego County's sage scrub communities, aiming to understand pollinator species and their interactions. Using camera traps and the deep learning technology VIAME (originally developed for underwater applications), the program collects video data on pollinator activity, particularly focusing on endangered species like Encinitas baccharis and post-fire recovery networks (check out our ArcGIS Dashboard from a recently conducted post-fire network composition study wherein we collected video data in summer 2023). This project's objectives include annotating tracks of insects in video data meticulously and developing neural networks for accurate detection of pollinator activity in large datasets, aiming to advance understanding of insect-plant interactions and potentially influencing policy changes and scientific advancements in pollinator conservation. Success entails optimizing technology for wider use, comprehensively evaluating pollinator-plant interactions, and uncovering insights into pollinator resilience and adaptation to environmental disturbances.

Our WILDLABS AWARD Team (from left to right): Christina Simokat, Mia Lorence (@mia_lorence), Yuli Martinez (@martiyu), and Liz Ferguson