Welcome to our project repository for the Google Hack 4 Change Hackathon! Our team developed an innovative solution to address the challenges faced by small-scale farmers using satellite technology.
Small-scale farmers play a crucial role in global food production, but they often lack access to modern technology and resources. This limits their ability to make informed decisions about crop management, leading to reduced yields and economic hardship. The goal of our project is to leverage satellite data to provide actionable insights to these farmers, empowering them to make better decisions and improve their productivity.
Our solution, named "FarmWell" is an android application that utilizes satellite imagery and machine learning algorithms to provide valuable information to small-scale farmers. By analyzing satellite data, we offer insights into various aspects of farming, including:
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Crop Health Monitoring: We analyze satellite images to assess the health of crops, identifying potential issues such as pest infestations or nutrient deficiencies.
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Weather Patterns: Our application tracks weather patterns in the farmer's area, providing forecasts and alerts for potential extreme weather events.
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Yield Estimation: Using machine learning models, we estimate potential crop yields based on historical data and current conditions.
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Crop Planning: AgriSat Insight suggests optimal planting and harvesting times based on the analysis of satellite data and local climate conditions.
- User-friendly web interface.
- Real-time satellite imagery integration.
- Crop health analysis with disease and pest detection.
- Weather forecasts and alerts.
- Crop yield prediction.
- Customized crop planning recommendations.
- Interactive dashboard for farmers to visualize data.
Watch our project in action! Demo Video
- Tanishq Agarwal @tanishq5414
- Kaamil Mirza @kaamilmirza
- Arun Zuturu @arunzuturu
We would like to express our gratitude to the Google Hack 4 Change Hackathon organizers for providing us with the platform to work on meaningful solutions.
This project is licensed under the MIT License.