Crop Recommendation dataset

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Sat, 06/29/2024 - 01:58
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In the realm of global agriculture, the imperative of sustaining an ever-expanding population is met with challenges in optimizing crop production and judicious resource management. SmartzAgri heralds a groundbreaking approach to modern agriculture. This innovative system represents a convergence of machine learning algorithms and Internet of Things (IoT) technology, aimed at reshaping traditional paradigms of crop recommendation. At the core of SmartzAgri lies a meticulous process: IoT devices intricately designed collect soil data, focusing on key parameters like Nitrogen (N), Phosphorus (P), Potassium (K), pH levels, moisture, and temperature. This real-time data is collected using different sensors and seamlessly transmitted to a dedicated web platform fortified by cutting-edge machine learning algorithms including Random Forest, XG-Boost, Naive Bayes, and Support Vector Machine (SVM). This ensemble of algorithms facilitates an intelligent analysis, enabling the system to predict with precision the most suitable crops for a given soil composition. In essence, SmartzAgri emerges as a sophisticated solution, marrying data-driven insights and real-time analysis to offer farmers nuanced recommendations for crop selection. This holistic approach holds the promise of enhancing precision in crop management, ultimately contributing to the elevation of agricultural productivity in a technologically advanced and informed manner.


Crop Recommendation Dataset

Dataset Description

This dataset is designed for crop recommendation systems and contains parameters that influence crop growth. It consists of two CSV files: a training dataset and a test dataset. The training dataset is used to train various machine learning algorithms, while the test dataset is utilized to evaluate the accuracy and performance of these algorithms.

Data Collection Methods

The data was initially obtained from Kaggle and subsequently modified to meet specific requirements.

File Formats

  • Train dataset: CSV format
  • Test dataset: CSV format


  • Crop Recommendation
  • Machine Learning
  • Agriculture
  • Data Analysis
  • Crop Prediction