Abstract
"Optimized dataset for smart farming, featuring soil nutrients (N, P, K), environmental sensors (Temp, Humidity, pH), and rainfall logs for 22 crop varieties."
Description
This dataset is crucial for AgriTech startups building smart farming solutions. It integrates data from various IoT sensors to recommend the most viable crop for a specific plot of land (Classification Problem).
Sensor Inputs (Features):
- N - Ratio of Nitrogen Content in soil: Measured via NPK sensors.
- P - Ratio of Phosphorous Content in soil: Key nutrient metric.
- K - Ratio of Potassium Content in soil: Essential for plant growth.
- Temperature: Ambient temperature in degrees Celsius.
- Humidity: Relative humidity in %.
- ph: Soil acidity/alkalinity level (0-14 scale).
- Rainfall: Rainfall in mm.
Target Variable:
Label: The name of the recommended crop (e.g., Rice, Maize, Chickpea, Kidneybeans, Pigeonpeas, Mothbeans, Mungbean, Blackgram, Lentil, Pomegranate, Banana, Mango, Grapes, Watermelon, Melon, Apple, Orange, Papaya, Coconut, Cotton, Jute, Coffee).
Use Cases:
Used to train Multi-class Classification Models (like Random Forest or Decision Trees) to maximize yield and reduce fertilizer waste.
Source: Open Source Agri-Research Data - Hosted on GitHub.
📊 View Data Structure
To explore column names, data types, and sample rows, visit the official dataset page on External.
Preview on External
Cite This Dataset
External (2026). Precision Agriculture: Soil Sensors & Crop Data. [Dataset]. External. https://raw.githubusercontent.com/Gladiator07/Harvestify/master/Data-processed/crop_recommendation.csv
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Original source: External (2026). Visit official page for more details.
Indexed by IoTDataset.com on Jan 05, 2026
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