## Inspiration

For three out of four of us, cryptocurrency, Web3, and NFTs were uncharted territory. With the continuous growth within the space, our team decided we wanted to learn more about the field this weekend.

An ongoing trend we noticed when talking to others at Hack the North was that many others lacked knowledge on the subject, or felt intimidated.

Inspired by the randomly generated words and QR codes on our hacker badges, we decided to create a tool around these that would provide an engaging soft introduction to NFTs for those attending Hack the North. Through bringing accessible knowledge and practice directly to hackers, Mint the North includes those who may be on the fence, uncomfortable with approaching subject experts, and existing enthusiasts alike.

## What It Does

Mint the North creates AI-generated NFTs based on the words associated with each hacker's QR code. When going through the process, the web app provides participants with the opportunity to learn the basics of NFTs in a non-intimidating way. By the end of the process, hackers will sign off with a solid understanding of the basics of NFTs and their own personalized NFTs. Along with the tumblers, shirts, and stickers hackers get as swag throughout the event, participants can leave Hack the North with personalized NFTs and potentially a newly sparked interest and motivation to learn more.

## How We Built It

**Smart Contract**
Written in Solidity and deployed on Ethereum (testnet).

**Backend**
Written in node.js and express.js. Deployed on Google Firebase and assets are stored on Google Cloud Services.

**Frontend**
Designed in Figma and built using React.

## Challenges We Ran Into

The primary challenge was finding an API for the AI-generated images. While many exist, there were different barriers restricting our use. After digging through several sources, we were able to find a solution. The solution also had issues at the start, so we had to make adjustments to the code and eventually ensured it worked well.

Hosting has been a consistent challenge throughout the process as well due to a lack of free hub platforms to use for hosting our backend services. We use Google firebase as they have a built-in emulator that allows us to make use of advanced functionality while running locally.

## Accomplishments that We're Proud Of

Those of us in the group that were new to the topic are proud of the amount we were able to learn and adapt in a short time.

As we continued to build and adjust our project, new challenges occurred. Challenges like finding a functional API or hosting pushed our team to communicate, reorganize, and ultimately consider various solutions with limited resources.

# What We Learned

Aside from learning a lot about Web3, blockchain, crypto, and NFTs, the challenges that occurred throughout the process taught us a lot about problem-solving with limited resources and time.

# What's Next for Mint the North

While Mint the North utilized resources specific to Hack the North 2022, we envision the tool expanding to other hackathons and tech education settings. For Web3 company sponsors or supporters, the tool provides a direct connection and offering to communities that may be difficult to reach, or are interested but unsure how to proceed.
## Inspiration:

We were inspired by the inconvenience faced by novice artists creating large murals, who struggle to use reference images for guiding their work. It can also help give confidence to young artists who need a confidence boost and are looking for a simple way to replicate references.

## What it does

An **AR** and **CV** based artist's aid that enables easy image tracing and color blocking guides (almost like "paint-by-numbers"!)
It achieves this by allowing the user to upload an image of their choosing, which is then processed into its traceable outlines and dominant colors. These images are then displayed in the real world on a surface of the artist's choosing, such as paper or a wall.

## How we built it

The base for the image processing functionality (edge-detection and color blocking) were **Python, OpenCV, numpy** and the **K-means** grouping algorithm. The image processing module was hosted on **Firebase**.
The end-user experience was driven using **Unity**. The user uploads an image to the app. The image is ported to Firebase, which then returns the generated images. We used the Unity engine along with **ARCore** to implement surface detection and virtually position the images in the real world. The UI was also designed through packages from Unity.

## Challenges we ran into

Our biggest challenge was the experience level of our team with the tech stack we chose to use. Since we were all new to Unity, we faced several bugs along the way and had to slowly learn our way through the project.

## Accomplishments that we're proud of

We are very excited to have demonstrated the accumulation of our image processing knowledge and to make contributions to Git.

## What we learned

We learned that our aptitude lies lower level, in robust languages like C++, as opposed to using pre-built systems to assist development, such as Unity. In the future, we may find easier success building projects to refine our current tech stacks as opposed to expanding them.

## What's next for [AR]t

After Hack the North, we intend to continue the project using C++ as the base for AR, which is more familiar to our team and robust.
## Inspiration

Greenhouses require increased disease control and need to closely monitor their plants to ensure they're healthy. In particular, the project aims to capitalize on the recent cannabis interest.

## What it Does

It's a sensor system composed of cameras, temperature and humidity sensors layered with smart analytics that allows the user to tell when plants in his/her greenhouse are diseased.

## How We built it

We used the Telus IoT Dev Kit to build the sensor platform along with Twillio to send emergency texts (pending installation of the IoT edge runtime as of 8 am today).

Then we used azure to do transfer learning on vggnet to identify diseased plants and identify them to the user. The model is deployed to be used with IoT edge. Moreover, there is a web app that can be used to show that the

## Challenges We Ran Into

The data-sets for greenhouse plants are in fairly short supply so we had to use an existing network to help with saliency detection. Moreover, the low light conditions in the dataset were in direct contrast (pun intended) to the PlantVillage dataset used to train for diseased plants. As a result, we had to implement a few image preprocessing methods, including something that's been used for plant health detection in the past: eulerian magnification.

## Accomplishments that We're Proud of

Training a pytorch model at a hackathon and sending sensor data from the STM Nucleo board to Azure IoT Hub and Twilio SMS.

## What We Learned

When your model doesn't do what you want it to, hyperparameter tuning shouldn't always be the go to option. There might be (in this case, was) some intrinsic aspect of the model that needed to be looked over.

## What's next for Intelligent Agriculture Analytics with IoT Edge