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Check out the documentation for more information.
Soil Fertility Prediction Model This machine learning model predicts soil fertility status (e.g. fertile or infertile) based on several chemical soil parameters. It was trained using a Random Forest Classifier on real agricultural data.
🧠 Model Info Type: Random Forest Classifier
Framework: Scikit-learn
Input: 13 numerical soil features
Output: Fertility classification with confidence score
📥 Input Format The model expects a list of 13 numeric values in the following order:
[ Nitrogen (N), Phosphorus (P), Potassium (K), Soil Acidity (pH), Electrical Conductivity (EC), Organic Carbon (OC), Sulfur (S), Zinc (Zn), Iron (Fe), Copper (Cu), Manganese (Mn), Boron (B) ]
Example:
{ "inputs": [12.0, 5.5, 20.0, 6.7, 1.1, 0.6, 10.0, 1.2, 4.1, 0.4, 3.3, 0.2, 0.5] }
⚠️ Note: The input must be a JSON object with the key "inputs" and a list of exactly 13 values.
📤 Output Format The model returns a list with one dictionary containing the predicted label and a confidence score.
Example output:
[ { "label": "fertile", "score": 0.92 } ]
📦 Files Included random_forest_pkl.pkl — The trained model
inference.py — Required for Hugging Face model hub to run inference
requirements.txt — Python dependencies
README.md — This file
📚 Requirements Python packages needed (also listed in requirements.txt):
scikit-learn
numpy
🔧 Usage This model can be used via the Hugging Face Inference API once uploaded to the Model Hub.