## Inspiration 🐳

The inception of our platform was fueled by the growing water crises and the lack of accessible, real-time data on water quality. We recognized the urgent need for a tool that could offer immediate insights and predictive analyses on water quality. We aimed to bridge the gap between complex data and actionable insights, ensuring that every individual, community, and authority is equipped with precise information to make informed decisions.

## What it does❓

Our platform offers a dual solution of real-time water quality tracking and predictive analytics. It integrates data from 11 diverse sources, offering live, metric-based water quality indices. The predictive model, trained on a rich dataset of over 18,000 points, including 400 events, delivers 99.7% accurate predictions of water quality influenced by various parameters and events. Users can visualize these insights through intuitive heat maps and graphs, making the data accessible and actionable for a range of stakeholders, from concerned individuals and communities to governments and engineers.
We also developed an AR experience that allows users to interact with and visualize real time data points that the application provides, in addition to heat map layering to demonstrate the effectiveness and strength of the model.

## How we built it 🛠️

We harnessed the power of big data analytics and machine learning to construct our robust platform. The real-time tracking feature consolidates data from 11 different APIs, databases, and datasets, utilizing advanced algorithms to generate live water quality indices. The predictive model is a masterpiece of regression analysis, trained on a dataset enriched with 18,000 data points on >400 events, webscraped from three distinct big data sources. Our technology stack is scalable and versatile, ensuring accurate predictions and visualizations that empower users to monitor, plan, and act upon water quality data effectively.

## Challenges we ran into 😣

Collecting and consolidating a large enough dataset from numerous sources to attain unbiased information, finding sufficiently detailed 3D models, vectorizing the 1000s of text-based data points into meaningful vectors, hyperparameter optimization of the model to reduce errors to negligible amounts (1x10^-6 margin of error for values 1-10), and using the model's predictions and mathematical calculations to interpolate heat maps to accurately represent and visualize the data.

## Accomplishments that we're proud of 🔥

* A 99.7% accurate model that was self-trained on >18000 data points that we consolidated!
* Finding/scraping/consolidating data from turbidity indices & pH levels to social gatherings & future infrastructure projects!
* Providing intuitive, easily-understood visualizations of incredibly large and complex data sets!
* Using numerous GCP services ranging from compute, ML, satellite datasets, and more!

## What we learned 🤔

Blender, data sourcing, model optimization, and error handling were indubitably the greatest learning experiences for us over the course of these 36 hours!
# Harvest Hero: Cultivating Innovation

## Inspiration

Our journey began with a shared passion for addressing pressing challenges in agriculture. Witnessing the struggles faced by farmers globally due to unpredictable weather, soil degradation, and crop diseases, we were inspired to create a solution that could empower farmers and revolutionize traditional farming practices.

## Staggering Statistics

In the initial research phase, we delved into staggering statistics. According to the Food and Agriculture Organization (FAO), around 20-40% of global crop yields are lost annually due to pests and diseases. Additionally, improper crop conditions contribute significantly to reduced agricultural productivity.

Learning about these challenges fueled our determination to develop a comprehensive solution that integrates soil analysis, environmental monitoring, and disease detection using cutting-edge technologies.

## Building HarvestHero

### 1. **Soil and Environmental Analysis**

We incorporated state-of-the-art sensors and IoT devices to measure soil moisture and environmental conditions such as light, temperature, and humidity accurately. Online agricultural databases provided insights into optimal conditions for various crops.

### 2. **Deep Learning for Disease Classification**

To tackle the complex issue of plant diseases, we leveraged deep learning algorithms. TensorFlow and PyTorch became our allies as we trained our model on extensive datasets of diseased and healthy crops, sourced from global agricultural research institutions.

### 3. **User-Friendly Interface**

Understanding that farmers may not be tech-savvy, we focused on creating an intuitive user interface. Feedback from potential users during the development phase was invaluable in refining the design for practicality and accessibility.

### Challenges Faced

1. **Data Quality and Diversity**: Acquiring diverse and high-quality datasets for training the deep learning model posed a significant challenge. Cleaning and preprocessing the data demanded meticulous attention.
2. **Real-Time Connectivity**: Ensuring real-time connectivity in remote agricultural areas was challenging. We had to optimize our system to function efficiently even in low-bandwidth environments.
3. **Algorithm Fine-Tuning**: Achieving a balance between sensitivity and specificity in disease detection was an ongoing process. Iterative testing and refining were essential to enhance the model's accuracy.

## Impact

HarvestHero aims to mitigate crop losses, boost yields, and contribute to sustainable agriculture. By addressing key pain points in farming, we aspire to make a meaningful impact on global food security. Our journey has not only been about developing a product but also about learning, adapting, and collaborating to create positive change in the agricultural landscape.

As we look to the future, we are excited about the potential of HarvestHero to empower farmers, enhance agricultural practices, and play a role in creating a more resilient and sustainable food system for generations to come.
## Inspiration

While attending Hack the 6ix, our team had a chance to speak to Advait from the Warp team. We got to learn about terminals and how he got involved with Warp, as well as his interest in developing something completely new for the 21st century. Through this interaction, my team decided we wanted to make an AI-powered developer tool as well, which gave us the idea for Code Cure!

## What it does

Code Cure can call your python file and run it for you. Once it runs, you will see your output as usual in your terminal, but if you experience any errors, our extension runs and gives some suggestions in a pop-up as to how you may fix it.

## How we built it

We made use of Azure's OpenAI service to power our AI code fixing suggestions and used javascript to program the rest of the logic behind our VS code extension.

## Accomplishments that we're proud of

We were able to develop an awesome AI-powered tool that can help users fix errors in their python code. We believe this project will serve as a gateway for more people to learn about programming, as it provides an easier way for people to find solutions to their errors.

## What's next for Code Cure

As of now, we are only able to send our output through a popup on the user's screen. In the future, we would like to implement a stylized tab where we are able to show the user different suggestions using the most powerful AI models available to us.