Crop Recommendation System Using Soil Measures

Overview

This project is aimed at helping farmers select the most suitable crop for their fields using machine learning. Given the farmer’s budget constraints, they can only measure one essential soil parameter (Nitrogen, Phosphorous, Potassium, or pH). This project employs a feature selection approach to identify the most important soil feature that can predict the best crop for a given field.

Problem Statement

**A farmer needs assistance in selecting the optimal crop for their land. However, due to financial constraints, the farmer can only afford to measure one out of four essential soil metrics: ** • Nitrogen (N) content in the soil • Phosphorous (P) content in the soil • Potassium (K) content in the soil • pH value of the soil

The goal is to identify which soil measure is the most important predictor of crop suitability using machine learning.

Project Pipeline

1.	Data Preprocessing:
•	Loaded a dataset containing soil measurements and corresponding crops.
•	Removed missing values and performed data cleaning.
•	Reset the index to maintain consistency after cleaning.
2.	Exploratory Data Analysis (EDA):
•	Analyzed the correlation between the four essential soil measures: Nitrogen, Phosphorous, Potassium, and pH.
•	Generated a correlation matrix to understand the relationships between these features.
•	Visualized the correlation matrix using a heatmap for better interpretation.
3.	Feature Selection:
•	Based on the correlation matrix and analysis, identified which soil feature has the most predictive power for selecting a crop.
•	Used machine learning algorithms (decision trees, random forest, etc.) to validate the feature importance.

Results

•	The heatmap of the correlation matrix provides insights into the relationships between soil measures.
•	The chosen soil feature will serve as the most influential input for crop selection.

Dataset

•	The dataset contains information about various crops and soil properties like Nitrogen, Phosphorous, Potassium, and pH values.
•	Source of the dataset: [Dataset link if public].

Tools and Technologies Used

•	Languages: Python
•	Libraries:
•	Pandas for data manipulation
•	Seaborn and Matplotlib for data visualization
•	Scikit-learn for machine learning algorithms

Instructions to Run the Project

1. Clone the repository: git clone https://github.com/username/repository-name.git

2. Navigate to the project directory: cd repository-name

3. Install the required dependencies: pip install -r requirements.txt

4. Run the main analysis: python crop_recommendation.py

Conclusion

This project demonstrates how machine learning can be leveraged in agriculture to assist farmers in making data-driven decisions about crop selection, even with limited resources for measuring soil properties. By identifying the most important soil measure, farmers can allocate their limited resources effectively while improving their yields.