Krishikarm

Crop distress prediction from satellite and weather data.

Krishikarm uses KisanNet v3, a cross-attention transformer trained on 82,000+ real satellite observations across 127 Indian districts, to predict crop health risk in real time.

Installation

pip install krishikarm

Quick Start

from krishikarm import Predictor

predictor = Predictor()

result = predictor.predict({
    "lat": 12.97,
    "lon": 77.59,
    "T2M": 32,
    "RH2M": 65,
    "PREC": 2.5,
    "SOLAR": 18,
    "WIND": 3,
    "state": "KA",
    "irrig": "borewell",
    "crops": ["rice"],
})

print(result)
# {
#     "distress_score": 0.12,
#     "intervention_days": 26.4,
#     "risk_class": 0,
#     "risk_label": "Healthy"
# }

Model Architecture

KisanNet v3 is a cross-attention transformer with:

• 42 input features derived from weather, soil, atmosphere, and location data
• State and irrigation embeddings for regional context
• 4-layer, 8-head cross-attention fusion between satellite and context encoders
• Three prediction heads: distress score (0-1), intervention window (days), risk class (5 levels)
• 96.8% accuracy on held-out test data

Input Features

Risk Classes

Data Sources

All training data was collected from free, open APIs:

• NASA POWER -- Solar radiation, temperature, humidity
• Open-Meteo -- Weather forecasts, soil moisture, UV, air quality
• SoilGrids -- Soil composition (clay, sand, SOC, pH)
• Open-Elevation -- Terrain altitude

No proprietary data or paid APIs were used.

API Reference

Predictor

Predictor(weights_path=None, device=None)

• weights_path -- Path to a .pth checkpoint. Defaults to the bundled v3 weights.
• device -- "cpu" or "cuda". Defaults to CUDA when available.

Methods

• predict(observation: dict) -> dict -- Run prediction on a single observation.
• predict_batch(observations: list[dict]) -> list[dict] -- Batch prediction.
• info() -> dict -- Return model metadata.

KisanNetV3

The raw PyTorch model class, for advanced users who want to integrate the architecture into their own training pipeline.

Requirements

• Python >= 3.9
• PyTorch >= 2.0
• NumPy >= 1.24

License

MIT