AgriSens

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  AGRI-SENS-CORE platform homepage featuring target region controls and multi-source satellite data resolution settings.

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  High-resolution interactive field map displaying spatial AOI boundaries overlaid on Esri World Imagery.

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  Real-time field risk summary panel displaying calculated CARI index scores and sub-index metrics.

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  Sentinel-2 NDVI time-series chart tracking canopy health dynamics over the user-selected date range against historical baseline averages.

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  Soil moisture and land surface temperature anomaly z-score diagnostic charts highlighting stress thresholds.

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  Detailed climate anomaly and vegetation tabular view flagging drought and heat stress risks.

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  Data export hub enabling downloads of time-series datasets (CSV), spatial boundaries (GeoJSON), and automated policy briefs.

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  System architecture documentation showing the end-to-end data ingestion, preprocessing pipeline, and CARI risk mathematical formulation.

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  Actionable remediation panel providing specific, calculated fertilizer and soil input proportions for high-risk fields.

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  Date Range Settings panel allowing users to configure custom start and end dates along with satellite cloud cover percentage thresholds.

Inspiration

A lot of small-scale farmers still have to rely on guesswork when it comes to soil nutrients, crop selection, or diagnosing plant diseases. Catching an infection or planting the wrong crop for a specific soil type can wipe out a season's income. I created AgriSens to give farmers a simple, AI-powered toolkit that turns complex agricultural and environmental data into actionable advice they can trust.

What it does

AgriSens gives users a clear view of field conditions using satellite data and automated calculations:

Vegetation Health Tracking (NDVI): Processes multi-spectral satellite imagery to monitor crop canopy health and density over time using Normalized Difference Vegetation Index (NDVI) calculations.

Thermal & Environmental Anomaly Detection: Analyzes thermal band feeds and climate variables to spot sudden moisture stress, micro-climate changes, or land degradation early.

Interactive Visualization Dashboard: Translates complex geospatial raster files and remote sensing indices into intuitive visual maps, time-series graphs, and actionable reports.

How I built it

I built AgriSens using a custom Python pipeline for remote sensing and geospatial data: Geospatial Processing: Wrote scripts using Python geospatial libraries to process multi-spectral satellite imagery, extract band values, and run index calculations like NDVI. Data Processing: Used Pandas and NumPy to structure multi-dimensional raster arrays, run anomaly detection logic, and clean up time-series data. Dashboard: Built a fast web interface with Streamlit (supported by a FastAPI backend) to render field maps and performance charts smoothly

Challenges I ran into

Working with satellite raster files was brutal on memory. Loading high-resolution imagery caused major performance lag on the web dashboard early on, so I had to restructure how I process and slice raster layers before rendering. Fine-tuning the anomaly detection logic was also tricky—I had to make sure normal seasonal shifts in vegetation weren't incorrectly flagged as crop stress.

Accomplishments that I'm proud of

I built a fully functional processing pipeline from scratch that converts complex satellite raster files into clear visual maps and automated crop health metrics.

What I learned

Building AgriSens taught me how to work with geospatial datasets, perform multi-spectral band math, and handle memory-heavy files in Python. I got much better at optimizing data processing pipelines and creating intuitive dashboards for complex scientific data.

What's next for AgriSens

I plan to connect AgriSens directly to Sentinel and Landsat APIs to pull new satellite imagery automatically and train predictive machine learning models for yield forecasting.

Built With

• data
• fastapi
• geospatial-data
• machine-learning
• ndvi
• numpy
• pandas
• python
• remote-sensing
• streamlit