Machine Learning

From Living Building Science

Welcome to the Machine Learning Team Page! One of our past goals last semester was to create a bee recognition and classification project to complement our finished flower recognition model and eventually integrate both of these models with the bee classification project of the previous ML team as well as the BeeKeeper GO app. Once successfully integrated, our models could potentially be used to enhance the experience of the app and make it more educational for users, as the classification/identification of flowers will allow users to learn while taking pictures. In addition, another goal of last semester was to use data analysis and computer vision to detect the occurrence of swarms in bee hives and the contributing factors/warning signs that will lead to a swarm occurring. Our current goals include improving and expanding our past projects as well as potentially creating machine learning and analysis tools to assist the other sub-teams in Living Building Science with their goals.

Spring 2021 Semester Overview

We started off this semester by doing the LBS vip. As we were originally in Beesnap, our projects have been geared toward machine learning applications with the goal of integrating with the Beekeeper GO application. However since that is no longer the case, we have shifted our focus to more relevant issues for this VIP. With the biodiversity team's focus on birds and Dr. Weigel's bird sound data that she has, we decided that analyzing and classifying birds would be helpful. We initially reached our to the biodiversity team but they said that there would probably not be anything for us to collaborate on, so we decided to do the bird classifications on our own. Additionally, we are expanding the bee hive analysis to include varroa mite detection with the intention to monitor the health of the hives on campus.

Hive Analysis

In Fall 2020, we created a script and tracking algorithm to draw the paths of bee flight in a given video. In order to use this data with to forecast bee swarming, we modified this algorithm to track the distance traveled by each bee in each frame and append that to a CSV file. This CSV file is to be used as a feature to analyze the bee movements on campus and determine their relative speeds and active points. We decided not to further improve this CSV tracking after we decided to cut down on the projects this semester.


The code for this project can be found under the bee repository linked below in the Spring 2021 Work Section.

Spring 2021 Work

Link to GitHub Repos: Hive Analysis, Varroa, Birds

Past Semester Projects

Fall 2020 Semester Poster

Spring 2020 Semester Poster


Team Members

Name Major Years Active
Sukhesh Nuthalapati Computer Science Spring 2020 - Present
Rishab Solanki Computer Science Spring 2020 - Present
Sneh Shah Computer Science Spring 2020 - Present
Daniel Tan Computer Science Fall 2020 - Present
Quynh Trinh Computer Science Fall 2020 - Present
Jonathan Wu Computer Science Spring 2020 - Present