Arko007 / agromind-crop-recommendation

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Model's Last Updated: February 11 2026
tabular-classification

Introduction of agromind-crop-recommendation

Model Details of agromind-crop-recommendation

AgroMind Crop Recommendation Model

Model Description

Scikit-learn classifier that recommends the optimal crop to cultivate based on soil nutrient levels and weather conditions.

Framework
  • Library : scikit-learn
  • Format : joblib/pickle (.pkl)
  • Scalers included : StandardScaler + MinMaxScaler
Input Features
Feature Range Unit
N (Nitrogen) 0–100 kg/ha
P (Phosphorus) 0–100 kg/ha
K (Potassium) 0–100 kg/ha
Temperature -10–50 °C
Humidity 0–100 %
pH 0–14
Rainfall 0–500 mm
Usage
from huggingface_hub import hf_hub_download
import joblib, numpy as np

repo = "Arko007/agromind-crop-recommendation"
model = joblib.load(hf_hub_download(repo, "crop_predict_model.pkl"))
sc = joblib.load(hf_hub_download(repo, "crop_predict_standscaler.pkl"))
ms = joblib.load(hf_hub_download(repo, "crop_predict_minmaxscaler.pkl"))

features = np.array([[50, 30, 40, 28, 65, 6.5, 200]])
pred = model.predict(sc.transform(ms.transform(features)))
Output

Integer crop ID (1–22) mapping to: Rice, Maize, Jute, Cotton, Coconut, Papaya, Orange, Apple, Muskmelon, Watermelon, Grapes, Mango, Banana, Pomegranate, Lentil, Blackgram, Mungbean, Mothbeans, Pigeonpeas, Kidneybeans, Chickpea, Coffee.

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agromind-crop-recommendation huggingface.co is an AI model on huggingface.co that provides agromind-crop-recommendation's model effect (), which can be used instantly with this Arko007 agromind-crop-recommendation model. huggingface.co supports a free trial of the agromind-crop-recommendation model, and also provides paid use of the agromind-crop-recommendation. Support call agromind-crop-recommendation model through api, including Node.js, Python, http.

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Arko007 agromind-crop-recommendation online free url in huggingface.co:

https://huggingface.co/Arko007/agromind-crop-recommendation

agromind-crop-recommendation install

agromind-crop-recommendation is an open source model from GitHub that offers a free installation service, and any user can find agromind-crop-recommendation on GitHub to install. At the same time, huggingface.co provides the effect of agromind-crop-recommendation install, users can directly use agromind-crop-recommendation installed effect in huggingface.co for debugging and trial. It also supports api for free installation.

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https://huggingface.co/Arko007/agromind-crop-recommendation

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Arko007
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