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Canopi

Quickstart

Quickstart

Section titled “Quickstart”

From zero to your first survival prediction in under two minutes.

1. Get an API key

Section titled “1. Get an API key”

Request access at canopitech.ai. You’ll receive a key that looks like:

cnpi_live_a8f2e9b1c4d7f0e3a6b9c2d5f8e1a4b7

All API requests require this key in the X-API-Key header.

2. Make your first prediction

Section titled “2. Make your first prediction”

Let’s predict survival for Douglas Fir planted by manual planting at a site on the western slopes of the Oregon Cascades.

cURL

Section titled “cURL”
Terminal window
curl -X POST https://api.canopitech.ai/v1/predict \
  -H "X-API-Key: cnpi_live_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "latitude": 44.12,
    "longitude": -122.03,
    "species_code": "PSME",
    "planting_method": "manual"
  }'

Python

Section titled “Python”
import httpx


response = httpx.post(
    "https://api.canopitech.ai/v1/predict",
    headers={"X-API-Key": "cnpi_live_your_api_key_here"},
    json={
        "latitude": 44.12,
        "longitude": -122.03,
        "species_code": "PSME",
        "planting_method": "manual"
    }
)


prediction = response.json()
print(prediction)

JavaScript

Section titled “JavaScript”
const response = await fetch("https://api.canopitech.ai/v1/predict", {
  method: "POST",
  headers: {
    "X-API-Key": "cnpi_live_your_api_key_here",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    latitude: 44.12,
    longitude: -122.03,
    species_code: "PSME",
    planting_method: "manual",
  }),
});


const prediction = await response.json();
console.log(prediction);

3. Read the response

Section titled “3. Read the response”
{
  "site": {
    "latitude": 44.12,
    "longitude": -122.03,
    "matched_latitude": 44.1087,
    "matched_longitude": -122.0451,
    "match_distance_km": 1.82,
    "region": "pnw"
  },
  "species_code": "PSME",
  "species_common_name": "Douglas Fir",
  "planting_method": "manual",
  "predictions": [
    {
      "horizon_years": 1,
      "survival_probability": 0.94,
      "risk_factors": [
        {"factor": "Vapor pressure deficit", "impact": "negative", "magnitude": 0.089},
        {"factor": "Precipitation", "impact": "positive", "magnitude": 0.072},
        {"factor": "Soil water capacity", "impact": "positive", "magnitude": 0.054}
      ]
    },
    {
      "horizon_years": 3,
      "survival_probability": 0.87,
      "risk_factors": [...]
    },
    {
      "horizon_years": 5,
      "survival_probability": 0.81,
      "risk_factors": [...]
    }
  ],
  "model": {
    "version": "pnw_v0_2_mycel",
    "codename": "mycel",
    "series": "Mycelium"
  },
  "generated_at": "2026-06-25T14:30:00Z"
}

Here’s what you got back:

  • site — Your requested coordinates plus the nearest data point Canopi matched to. The match_distance_km tells you how close the match is.
  • predictions — Survival probability at 1, 3, and 5 years. This site shows strong first-year survival (0.94) with gradual decline typical of a good planting site.
  • risk_factors — The top environmental factors influencing survival at this site. Negative impacts reduce survival; positive impacts support it. Here, vapor pressure deficit (a measure of atmospheric dryness) is the primary risk.
  • model — Which model version generated this prediction.

4. Compare planting methods

Section titled “4. Compare planting methods”

Now try the same site with both methods compared:

Terminal window
curl -X POST https://api.canopitech.ai/v1/predict/compare \
  -H "X-API-Key: cnpi_live_your_api_key_here" \
  -H "Content-Type: application/json" \
  -d '{
    "latitude": 44.12,
    "longitude": -122.03,
    "species_code": "PSME"
  }'

The /compare endpoint returns predictions for both manual planting and drone seeding, plus a method_differential showing the survival gap at each horizon. This is the question nobody else answers.

Next steps

Section titled “Next steps”