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.