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Arko007/
agromind-fertilizer-prediction

Name: agromind-fertilizer-prediction
Availability: InStock

scikit-learn

tabular-classification

fertilizer-recommendation

agriculture

apache-2.0

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AgroMind Fertilizer Prediction Model

Model Description

Scikit-learn classifier that predicts the most suitable fertilizer based on soil conditions, crop type, and environmental factors.

Framework

Library: scikit-learn
Format: pickle (.pkl)
Includes: classifier model + LabelEncoder for fertilizer names

Input Features

Feature	Type
Temperature	int (0–100)
Humidity	int (0–100)
Moisture	int (0–100)
Soil Type	encoded int (Black=0, Clayey=1, Loamy=2, Red=3, Sandy=4)
Crop Type	encoded int (Barley=0, Cotton=1, … Wheat=10)
Nitrogen	int (0–100)
Potassium	int (0–100)
Phosphorus	int (0–100)

Usage

python

from huggingface_hub import hf_hub_download
import pickle, numpy as np

repo = "Arko007/agromind-fertilizer-prediction"
with open(hf_hub_download(repo, "classifier.pkl"), "rb") as f:
    clf = pickle.load(f)
with open(hf_hub_download(repo, "fertilizer.pkl"), "rb") as f:
    le = pickle.load(f)

features = np.array([[28, 65, 40, 2, 6, 50, 40, 30]])  # [temp, hum, mois, soil, crop, N, K, P]
pred_idx = clf.predict(features)
fertilizer = le.inverse_transform(pred_idx)

Output

Fertilizer name (string) via LabelEncoder inverse transform.