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Brightplate

UI/ UX Design · Project Management
2020

Client

​Adobe Creative Jam: Design For Change With SoDA Agencies

My Role
Product Designer
UI Designer
UX Researcher
Team
Will Gamez
Zephyr Swart
Time frame
2 Weeks
Tools
Adobe: Illustrator, XD, Photoshop
Miro
Zoom
Google Suite

Project Overview

This project was created for an Adobe Creative Jam. We were instructed to build a minimum 15 page mobile app over the course of 2 weeks using adobe XD, and adobe suite. With the goal of addressing food insecurities.

Design Brief **

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Brightplate

Eccomerce & Health
SMC x Adobe Creative Jam | SNAP Market
UI Design · Mobile design · Research
2020
View Project
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I’m an Interaction Designer, Product Designer, and Graphic Designer. Passionate about interactions with physical and digital products.

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My Role
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How might we? How might we? How might we? How might we? How might we? How might we?

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We had the chance to speak one-on-one with John Zegowitz the Chief Creative Officer for Schieffer Chop Shop. With John we shared our proposed concepts. John liked the concept of our end-to-end shopping and possibility of nutrition program. He gave us a challenge of how to diminish the stigmatization off food insecurity and can we make a "food Disneyland".

We had the chance to speak one-on-one with John Zegowitz the Chief Creative Officer for Schieffer Chop Shop. With John we shared our proposed concepts. John liked the concept of our end-to-end shopping and possibility of nutrition program. He gave us a challenge of how to diminish the stigmatization off food insecurity and can we make a "food Disneyland".

When scanning an image for each of the 4 variables in Teachable Machines the Ai was a lot better/ more responsive at picking up and displaying the proper identifying names. The only flower that was consistently recognized by the AI was "Bougainvillea" and regularly thought that everything even just me was "White Rose". I wasn't so stoked about this result from the teachable tech. I did decide to upload duplicates of the same photos. This boosted the likelihood of showing the proper label.

Paragraph lIGHT: When scanning an image for each of the 4 variables in Teachable Machines the Ai was a lot better/ more responsive at picking up and displaying the proper identifying names. The only flower that was consistently recognized by the AI was "Bougainvillea" and regularly thought that everything even just me was "White Rose". I wasn't so stoked about this result from the teachable tech. I did decide to upload duplicates of the same photos. This boosted the likelihood of showing the proper label.

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.

Paragraph Medium: When scanning an image for each of the 4 variables in Teachable Machines the Ai was a lot better/ more responsive at picking up and displaying the proper identifying names. The only flower that was consistently recognized by the AI was "Bougainvillea" and regularly thought that everything even just me was "White Rose". I wasn't so stoked about this result from the teachable tech. I did decide to upload duplicates of the same photos. This boosted the likelihood of showing the proper label.

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PROJECT TAG LINE
A text link
These images sum up the type of lawns I encountered. Screenshots
from Google Maps of the actual houses I passed.
Buttons & Form Elements
Button TextButton Text
Buttons & Form Elements
Card Sort for Food Insecurity: Jam Board
“You’ll never change a community you don’t know, and you can’t begin to know a community until you hear, feel, taste and tramp through it.”
-Robert Egger
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Unfortunately, I haven't been on a walk in that part of my neighborhood in a while and a lot of the houses have redone their lawns and or moved flower beds from sidewalk access. In my neighborhood, there weren't many variations of flowers. Almost all lawns are drought resistant (with rocks, succulents, or "wild" drought-resistant flowers), or leaf-based bushes and shrubs. The flowers like colorful roses and other like flowers were not available to access by sidewalk view. This narrowed down my sample size greatly so I ran into a couple of issues when training the model.
These images sum up the type of lawns I encountered. Screenshots from Google Maps of the actual houses I passed.
Other Flowers I found
I found other flowers but I wish there were more of them to extract a larger sample size of images.

I went for a walk and took photos of the different types of flowers in my neighborhood. I organized the images into like types of flowers. Then, imported them into the Teachable Machines and trained my model to identify the information.

Uploading the images on my PC was a lot better than on my phone. Here you can see the model working with an image I had it identify.

When scanning an image for each of the 4 variables in Teachable Machines the Ai was a lot better/ more responsive at picking up and displaying the proper identifying names. The only flower that was consistently recognized by the AI was "Bougainvillea" and regularly thought that everything even just me was "White Rose". I wasn't so stoked about this result from the teachable tech. I did decide to upload duplicates of the same photos. This boosted the likelihood of showing the proper label.

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