import pickle
import pandas as pd

with open("trained_model.pkl", "rb") as f:
    model = pickle.load(f)
with open("feature_scaler.pkl", "rb") as f:
    scaler = pickle.load(f)

features = ["pH","N","P","K","Ca","OM","CEC","SM","SS",
            "T","Rainfall","RH","Rad","WS","WD","Fert"]

# example row
X = pd.DataFrame([[6.5,40,20,150,800,3.0,15,25,2.0,
                   22,100,60,18,2.0,180,100]], columns=features)
pred = model.predict(scaler.transform(X))
# pred columns: Crop_Yield, N2O_Emission, CO2_Emission, CH4_Emission
print(pred)

Or download from the Hub:

python

from huggingface_hub import hf_hub_download
import pickle

model_path = hf_hub_download("CircuitNotion/agri-ghg-yield-gb", "trained_model.pkl")
scaler_path = hf_hub_download("CircuitNotion/agri-ghg-yield-gb", "feature_scaler.pkl")

Training data

Trained on CircuitNotion/agri-ghg-yield-synthetic (6,000 synthetic agronomic samples).

Code

Full pipeline: ntirushwajeanmarc/agriculture-ghg-yield-ml