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Soil Moisture Web App

For my final semester project, I am helping develop a web application for a horticultural client that takes data from soil moisture probes, storing the data and displaying the information in a way that users can make informed decisions.

The application is being developed using Django, a Python framework, using PostgreSQL for the database, and is hosted on Amazon Web Services. Git is used for version control, using a Github repository. D3.js will be used to display data as meaningful graphs for the client.

Project progress:


Track Records Web App

This web application, being developed for The Battle Cave, stores and displays lap times from their racing simulators. The application allows users to view lap times and records for tracks on various games/platforms. Administrators are able to login and add new users, tracks, cars, and add times/records.

The application is being developed using Golang for the back-end coding and web server, with PostgreSQL for the database. The front-end is coded in HTML5, CSS3, jQuery, and uses Bootstrap to provide a clean, responsive design. Currently in development on a local machine, I am planning to host it on Amazon Web Services when it is completed, where it will be accessed via a subdomain of their website. Watch this space for updates!

Project progress:


Machine Learning

Detecting Varroa Mites on Bees

Varroa mites are small parasitic mites that attack varying species of honey bees, causing a disease within the bees called varroosis. The mites are approximately 3mm in size, and are visible to the naked eye as small, brown dots. A significant infestation can lead to the death of a colony, and the mites are attributed as one of many stress factors contributing to bee losses worldwide.

I am creating a machine learning application that can detect Varroa mites on bees. Given an image of bees, the system will be able to tell the type of each of the bees - Queen, Worker, or Drone, and be able to detect whether or not each bee has Varroa mites on it.

I am using Pytorch and Google Colab to develop the system.

Project progress:


UX Design

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