With the pandemic causing people not to move as much, eating healthy is as important as ever. During this time, one of our group members tried eating more produce but had minimal knowledge on what was good and for how long. We realized a barrier was present for produce selection and healthy eating for inexperienced buyers.
What it does
Bananalzyer utilizes a web application to take pictures of produce to be analyzed. Then, image processing trained by a neural network uses the image to provide data on ripeness, freshness, and days until the item goes bad. The data is presented to the customer to inform them to make the best decision.
How we built it
We built the web app using Bootstrap, HTML, CSS, and Flask. Utilizing Github and VS code to work together on the project between the four people. Then to analyze the fruit we used image processing trained by a neural network. We used several different image processing techniques and algorithms. We utilized machine learning to help classify good and bad fruit.
Challenges we ran into
During the project, with 4 people working together we ran into coordination issues. Also, given no final solution sometimes ideas conflicted. When designing the website we had minimal front end development experience so designing and redesigning the website was a big challenge.
Accomplishments that we're proud of
We are proud of developing an idea that will help the health of others by giving them access to more information to make better decisions. Team work was improved between the group during the process to improve efficiency through the event. Being able to submit a working application and being able to demo it effectively was a fulfilling achievement.
What we learned
We learned front end development and how to overcome the challenges involved with it. Also, we learned how to integrate the image processing into the web application to accomplish the intended functionality and make the idea come to life.
What's next for Bananalyzer
Unfortunately, due to time constraints, Bananalyzer could not meet all the functionality that was intended. Up next is incorporating more produce to chose from, improve the UI to make it easier to use, and provide more data on the produce. We plan on expanding our process by adding in questions related to the texture for the user to input after he/she has taken a picture of the produce. We understand there are other factors that go into the freshness of produce and will be working towards adding these factors to enhance the rating process. We also plan on improving the "Bananalyze" page by showing more detailed data as well as using charts so the consumer can visualize the data easier. This visualization is important so the customer can understand the important factors, freshness, ripeness, and how long the produce will last.