المشروع الصيفي
تخصص الذكاء الاصطناعي · دفعة 2025
Tracking Aquatic Insect Emergence Using Machine Learning
A team of students led by Hubbard Brook Experimental Forest researchers will develop a machine learning approach to identify aquatic insects and contribute to a long-term ecological dataset being used to examine changing insect population dynamics. Students will use a set of existing images to train a supervised machine learning model to classify images of insects and, using additional climate, stream flow, stream chemistry, and stream algae data, create visualizations and communication tools for the Hubbard Brook Watershed Ecosystem Record (https://www.hbwater.org). This project will support ongoing research focused on understanding the impacts of climate change, specifically warming winters, on aquatic insect populations and will create a helpful, time-saving insect identification workflow that may be disseminated to other aquatic ecology research groups.
المشرف: Heili Lowman
مع أكاديمية كاوست
- دفعة 2025
جامعة ديوك · التدريب الصيفي في الذكاء الاصطناعي
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