Project Info
This project did not submit a demo video on Devpost.
Inspiration
The UC Santa Cruz Arboretum & Botanic Garden served as the inspiration for Grotime.
What it does
Grotime, the proposed website, leverages predictive modeling to recommend the most suitable plants based on the prevailing weather conditions at the time of user access.
How we built it
Front end: Developed using Figma, HTML, and CSS. Back end: Implemented with Python, integrating a Weather API and utilizing Flask.
Challenges we ran into
Aggregating Weather Data: Faced difficulties in gathering weather data for extended periods to enhance predictive accuracy. Plant Data Acquisition: Encountered challenges in finding comprehensive data on various plants to train and optimize the predictive model.
Accomplishments we're proud of
Single Score Conversion: Successfully transformed the model's predictions into a cohesive single score for user convenience. Simplistic Design: Accomplished the implementation of a simplistic and user-friendly design for an enhanced user experience.
What we learned
Front-End and Back-End Integration: Discovered the complexities of seamlessly integrating front-end and back-end functionalities. Dataset Organization: Recognized the pivotal role of well-organized datasets in the success of predictive modeling.
What's next
Integration with Soil Data: Planning to integrate soil data for more accurate predictions and personalized plant recommendations. Expanding User Base: Aiming to extend the platform's utility beyond the UC Santa Cruz Arboretum, supporting gardeners in diverse locations.
https://weather-application-sooty.vercel.app/
Here're some of the project's best features:
- ML trained model to determine at what time cetain plants will grow the best
- Real-time Weather
- 5 days forecast
- Wind speed
- Userfriendly
- real time user location
Whats next for grotime?: implement a more interactive and clean UI. see the future implementation prototype made in figma here: https://www.figma.com/proto/1PSMmaW6ttrnOhq3ECjbVp/CruzHacks-Grotime-HIFI?node-id=1-2&scaling=min-zoom&page-id=0%3A1&starting-point-node-id=1%3A2&t=MBf4qzq4emWoqpTT-1&mode=design
Technologies used in the project:
- HTML5
- CSS3
- python
- Javascript
- Openweather-Api
Analysis
View
Metric
- 39
- 12
Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.
Technology
- CSSIn code
- HTMLIn code
- JavaScriptIn code
- PythonIn code
4 of 4 appear in the indexed code.
AI coding agents
No AI coding agent signals were found in this repository.
Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.
Codebase size
Source size
52 KB
Source files
10
Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.
Repository
ani-sivaa/itsgrotime-
50 files · 1.2 MB · @ 1157425
Structure
Interface
3 files · 6%Screens, components and styles rendered to the user.
Application logic
2 files · 4%Domain rules, services and shared utilities.
Supporting
Layers are inferred from where files sit in the tree, not from reading the code. A project that names its directories unconventionally will read oddly here — open the file browser to check anything the diagram implies.
Languages
- CSS35%
- JavaScript35%
- HTML22%
- Python6%
- Markdown2%
Share of indexed source by file size. Binary and vendored files are excluded.
This project’s features have not been analysed yet.
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