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ecoByte Main Webpage
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ecoByte Recommendations
Inspiration
As first-years, we have always noticed that the garbage in the dining halls were always overflowing. Queen’s is constantly looking for initiatives to help bring us closer to achieving the 17 United Nations Sustainable Development Goals, and we noticed that Zero Hunger (2) and Responsible Consumption and Production (12) were some of the most relevant for our university campus.
What it does
ecoByte aims to tackle this problem and streamline the process of tracking waste. After someone finishes eating, their food waste is identified and recorded via cameras equipped with Google Lens, and the data is collected into our software. And at the end of the day, recommendations for future menu options are made to make sure that supply is not greater than the demand, minimizing excess. The program begins with options from a menu, allowing the user to display data, create new entries for specific foods, and analyze data from a particular date or dining hall. It then is automatically saved from that specific date to an Excel spreadsheet. ecoByte’s interface is easy for Queen’s food management team to navigate, only providing the most important data points, like displaying the total waste breakdown from any date through a pie chart. The management team can quickly scroll through the percentages of food waste by food type. When it comes to planning out the dining hall menus, users can use the ecoByte recommendations to help reduce food waste by matching the supply of food brought out to meet exactly what the students are eating.
How we built it
We used Python to build the back-end and created a mockup using Adobe Illustrator. Data inputted from the user was put into Excel using Pandas. The different tasks were separated using modular design.
Challenges we ran into
We struggled with figuring out how to actually collect the data in order to estimate food waste at Queen’s. When researching, we found Google Cloud’s Google Lens as a viable option to implement. Thus, we were able to move forward with our plan of having a camera to track food waste in real time without worrying too much about the intricate logistics behind visual detection. Google Lens identifies the food on each person’s plate in live time by comparing the video to a database of photos. Once it identifies the food, it will estimate the weight/amount of food waste. When the food is dumped into the garbage, the weight of the garbage will increase. By comparing the estimated weight from the camera with the physical weight added to the garbage bin, a more accurate estimate of each food’s amount of waste will be calculated and entered into ecoByte’s database.
Accomplishments that we're proud of
We’re proud of not only researching and identifying a relevant (yet critical) issue in our community, but being able to articulate a solution that is possible to implement, as this is our first ever hackathon. We’re really proud of being able to take our project and decompose it into smaller, more manageable tasks.
What we learned
The most valuable thing that we learned was how to work as a team and think critically when solving a problem. Because the topic was so broad, there was a lot of room for interpretation making it overwhelming initially. However, by working as a team to break down the problem step by step, we were able to make this application that we are really proud of. We worked together as a team to identify the specific social good we wanted to incite, our target audience, and the execution. The whole experience was a pleasure and something we will remember when looking back on our time at Queen’s.
What's next for ecoByte?
ecoByte has the potential to not only better our immediate community, but other larger institutions as well. We see ecoByte being a solution to food waste for universities and corporations across Canada. We hope that ecoByte is able to satisfy the needs of students and employers, all while bettering the planet.
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