bhoomini

Inspiration In paddy cultivation, up to 150,000 liters of water per acre per day (representing ~36% of applied irrigation) is lost to deep soil percolation and underground rock fractures. Driven by the vision of conserving groundwater in regions like Telangana, we set out to build a dual-action agricultural solution: physical percolation reduction combined with real-time root-zone intelligence.

What it does Percolation Reduction: Uses a bio-based superabsorbent soil amendment (starch, biochar, alginate, and urea) to retain moisture in the root zone and reduce deep percolation by 40%.Bio-Electric Sensing Network: Harnesses the natural graphitic carbon structure of biochar to create a conductive pathway in the soil.AI Analysis & Insights: Captures root-level electronic signals to monitor water needs, nutrient status, pest risks, and soil conditions, providing farmers with actionable recommendations.

How we built it Formulated the bio-polymer soil amendment (Sujalam Bio Polymer blend).Designed a conductive biochar network that transmits micro-electronic signals directly from crop roots to an IoT receiver.Implemented AI processing models to filter signal noise, cross-reference historical soil/weather data, and generate yield predictions and irrigation schedules 1 .

Challenges we ran into Optimizing the biochar particle contact points to maintain stable electrical conductivity across varying soil moisture levels.Filtering environmental noise from soil signals to isolate accurate indicators for water and nutrient status

Accomplishments that we're proud of Biochar's graphitic structure can serve a dual purpose: significantly boosting water/nutrient retention while acting as a passive bio-sensor layer.Integrating root-zone electronic feedback enables far more targeted irrigation and fertilizing than traditional surface sensors

What we learned Python, TensorFlow, IoT, Machine Learning, Biochar, Soil Sensors, Signal Processing

Built With

• python
• tensorflow