Fresh Start

💻 Additional Demo: https://www.youtube.com/watch?v=PhMmWp0cp5I

Inspiration 🌱

Between coding, social media, and online homework, it gets hard to look at bright glass rectangles all day. However at OU, you get to look forward to a nice walk through beautiful nature, with plants covering practically everywhere. Seeing the OU gardeners at work inspired our team to remember some gardens we used to have at home, so we decided to create a tool to help anyone who wants to begin growing their own Fresh food, whether that's a small home garden, or a large farm.

What it does 👨‍🌾

Helps people start a small farm or garden by helping you Plan->Build->Maintain your farm, while minimizing your time spent online planning/researching about starting a farm.

Farm Mapping Layout Tool:

define width, height, soil variables of your total farm area.
drag and create different crop areas.
click on a crop area to edit its attributes such the type of crop, irrigation method, fertilizer type, fertilizer method, and density.

Crop Yield Prediction Feature:

Create a machine learning model that takes in the farm soil variables and a crop areas variables (above) as input and outputs the crop yield kg/m2 for a single crop area in your farm.

Personalized Schedule Task Generation:

Compiled all graphical data of a layout into meaningful json and MongoDB objects data format to be passed into a LLM to generate specific tasks based on the needs of your farm.

Equipment Suggestion Page to Help Build Farm:

Lists equipment, products, tools needed to build your farm compiled into one place so you don't have to spend time across multiple platforms shoping and researching.

Farm creation:

Set up your space, inputting size and other attributes
Create and arrange several plots of crops on an open canvas
Check out some useful statistics about your farm Farm dashboard:
View and compare multiple farm layouts
View your predicted crop yield generated by our custom AI model
View useful statistics
View a To-Do list

How we built it 🌳

MERN:

Using a MongoDB backend, Express and Node server-side logic and routing, and React front-end, our project was able to remain organized even while dealing with lots of webpages and data connections between those pages. Strategy:
We used a branching strategy that allowed us all to work on separate features, and kept a running whiteboard of our frontend navigation, backend data movement, and AI data generation.

Challenges we ran into 🚧

-Saving a Farm Layout plan was challenging because after a user is done designing their farm layout. we had to compile all of the data of a Layout and data of all of the Crop Areas, and send to the backend and save them to the MongoDB database in a reliable way so the Layout & Crop Areas it can be rendered again.

Accomplishments that we're proud of 🌾

-Training a model that is capable of predicting crop yield based on various features. -Integrating a LLM to generate farm-personalized tasks for the user. -Creating a Drag/Drop crop area functionality for crop visualization.

What we learned ✍️

We learned more about the client server relationship.

What's next for Fresh Start 🌼

To further fit the theme of this hack, we plan to create a feature where it streamlines the process of designing your farm layout, by using AI to suggest different farm layouts for maximum yield based on the users preferences & goals, making it faster for a user to design a layout.