AgriVision: Smart Crop Detection from Space

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  predictions using satellite data

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  EuroSAT Predictions on satillite rgb matches

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  predictions using satellite data

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  predictions using satellite data

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  Accuracy and Loss

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  class distribution of EuroSAT dataset

Inspiration

Crop monitoring in India is challenging due to small, fragmented farms and diverse cropping practices. Inspired by the Track 1 challenge, we wanted to build an AI system that can identify crops from satellite imagery and scale to real-world use cases like yield prediction and food security planning.

What it does

AgriVision classifies satellite imagery into different crop/land cover types using deep learning.

• Input: Sentinel-2 satellite patch (EuroSAT dataset).
• Output: Predicted crop/land cover class (e.g., Annual Crop, Forest, Pasture).
• Visuals: EDA heatmaps, class distributions, accuracy/loss curves, confusion matrix, and map-style visualizations of predictions.

How we built it

• Dataset: EuroSAT (Sentinel-2 imagery, 10 land cover/crop classes) downloaded via Kaggle API.
• Data Cleaning & EDA: Removed corrupted images, visualized class balance, generated correlation heatmaps of spectral features.
• Modeling: Trained a Convolutional Neural Network (CNN) with data augmentation for crop classification.
• Evaluation: Accuracy, F1-score, confusion matrix, and prediction visualizations.
• Visualization: Created “map-like” classification grids to simulate land cover maps from model outputs.

Challenges we ran into

• Limited access to real AgriFieldNet/ISRO SAR datasets during the hackathon, so we had to prototype using EuroSAT.
• Handling dataset imbalance across classes.
• Designing visuals that communicate results effectively in limited hackathon time.

Accomplishments that we're proud of

• Built an end-to-end pipeline in Colab: from data ingestion → cleaning → modeling → evaluation → visualization.
• Achieved high accuracy on EuroSAT classes using our CNN model.
• Created intuitive visuals (heatmaps, confusion matrix, colorful map-style predictions) that make the model’s performance easy to understand.
• Demonstrated a clear roadmap to extend to real-world Indian agricultural fields (Track 1 vision).

What we learned

• How to process and analyze satellite imagery for agricultural applications.
• The importance of feature extraction and visualization in understanding geospatial data.
• How small tweaks (augmentation, normalization, dropout) can significantly improve CNN model performance.
• The gap between clean benchmark datasets (EuroSAT) and real, noisy, heterogeneous datasets (Indian farms).

What's next for AgriVision: Smart Crop Detection from Space

• Extend the pipeline to AgriFieldNet India dataset with SAR + optical time-series.
• Explore geospatial foundation models for better generalization across regions.
• Deploy as a web dashboard or Gradio app for farmers, policymakers, and researchers.
• Incorporate temporal satellite data (multi-season, multi-date) for crop monitoring and yield forecasting.

👉 Do you want me to also make a 1-slide summary (Problem → Solution → Results → Roadmap) for your hackathon pitch?

Built With

• accuracy-curves
• classification-report)-pandas-&-numpy-?-data-handling-&-preprocessing-matplotlib-&-seaborn-?-visualization-(heatmaps
• googlecolab
• kaggleapi
• keras
• matplotlib
• numpy
• opencv
• pandas
• pil
• python
• scikit-learn
• seaborn
• tensorflow