Inspiration
The idea for ErosionWatch came from seeing firsthand the struggles of farmers in erosion-prone areas, especially in hilly regions. I’ve known farmers who’ve watched their fertile soil wash away after heavy rains, leaving them with less productive land and fewer options. I wanted to see if there was I anything I could do with technology to tackle such a critical issue. By providing useful tools to measure soil loss and guide them on how to protect their farmland, ErosionWatch is built to make a real difference for all farmers.
What it does
Basically, if a farmer is facing soil erosion that would remove his farmland, ErosionWatch helps them measure how much soil has been lost after heavy rains. By analyzing photos of reference pins or rulers placed in the soil, it calculates the erosion depth and highlights high-risk areas. The platform provides color-coded risk maps, practical recommendations like where to plant vetiver grass, and downloadable reports to help farmers take immediate action to understand their land and improve productivity.
Uniqueness
ErosionWatch stands out because it combines simplicity with practicality. Unlike generic tools, it’s specifically designed for farmers, focusing on soil erosion, a critical but often overlooked issue. Its ability to analyze photos using everyday items like rulers or pins makes it accessible without requiring expensive equipment. The platform also provides actionable recommendations tailored to real-world farming needs, ensuring it’s not just a diagnostic tool but a practical solution. This focus on usability and impact makes ErosionWatch unique. There are no tools exactly like it in the world.
How we built it
We built ErosionWatch using Python and Flask for the backend, leveraging OpenCV for image processing to detect reference pins and measure soil erosion. The frontend was designed with HTML5 and responsive design principles to ensure accessibility across devices. We used ReportLab to generate detailed PDF reports and integrated a risk assessment engine to provide actionable recommendations.
Challenges we ran into
One of the biggest challenges I faced was ensuring the accuracy of soil erosion measurements across different terrains and photo conditions. Developing a tool that works reliably for farmers with varying levels of technical expertise was another hurdle, we had to simplify complex processes without losing functionality. Balancing technical development with real-world usability is a constant challenge, but it pushed me to create a more accessible solution.
Accomplishments
We’re proud of creating a tool that directly helps farmers tackle soil erosion, a problem that impacts their livelihoods. Developing a platform that combines advanced image analysis with practical recommendations has been a major achievement. Most of all, I'm proud of building something that makes a real difference in sustainable agriculture, not just an app, but something that can change the world.
What we learned
Through building ErosionWatch, I learned how critical it is to provide farmers with useful tools. I realized the importance of balancing advanced technology, like image analysis, with user-friendly design to ensure accessibility. Working closely with farmers taught us the value of practical solutions tailored to real-world challenges, like soil erosion in steep terrains. Most importantly, we learned that even small interventions can have a big impact when they empower communities to protect their land and livelihoods.
Relevance to Mountain Coffee Farming
Mountain coffee farming on steep slopes faces severe soil erosion from heavy rains. ErosionWatch helps farmers measure soil loss, identify high-risk areas, and take action like planting vetiver grass to stabilize slopes. This protects soil health, improves coffee yields, and ensures sustainable farming practices.
What's next for ErosionWatch
ErosionWatch plans to expand its reach by equipping more farmers with tools to monitor and reduce soil erosion. It aims to scale globally, focusing on regions most affected by erosion. By integrating with local advisory systems, new features, offering multi-language support, it seeks to make its solutions accessible to a wider audience.
[AI-Generated Content: Heavily relied on AI for the project since I'm still growing as a coder. Additionally, I used AI to help me with this section, mostly for understanding how to structure my sentences and grammatical mistakes]
Additionally, the link below may take time to load because I'm using Render to host my application online.
Log in or sign up for Devpost to join the conversation.