Smart Agriculture Advisory System

Overview

The Smart Agriculture Advisory System is an application designed to provide farmers with personalized advice on crop management, pest control, and irrigation scheduling. By leveraging machine learning models, the system analyzes various environmental and soil parameters to recommend the most suitable crops for cultivation.

Dataset

The dataset used for this project is sourced from Kaggle: Crop Recommendation Dataset.

Features

• N (Nitrogen): Nitrogen content in the soil
• P (Phosphorus): Phosphorus content in the soil
• K (Potassium): Potassium content in the soil
• temperature: Temperature in degree Celsius
• humidity: Relative humidity in %
• ph: pH value of the soil
• rainfall: Rainfall in mm

Target

• label: Type of crop to be grown

Installation

Prerequisites

• Anaconda: A distribution of Python and R for scientific computing and data science.

Steps

1. Install Anaconda

   Download and install Anaconda from the official website based on your operating system.

2. Clone the Repository

   git clone https://github.com/your-username/smart-agriculture-advisory-system.git
   cd smart-agriculture-advisory-system

3. Create a Conda Environment

   conda create --name smart-agriculture python=3.8
   conda activate smart-agriculture

4. Install Required Libraries

   pip install -r requirements.txt

5. Install Jupyter Notebook

   conda install -c conda-forge notebook

Usage

1. Run Jupyter Notebook

   jupyter notebook

2. Open the Notebook

   In the Jupyter Notebook interface, open notebooks/Smart_Agriculture_Advisory_System.ipynb to explore the analysis, model training, and predictions.

Project Structure

• data/: Contains the dataset used for training and testing.
• notebooks/: Jupyter notebooks for data analysis and model building.
• models/: Saved machine learning models.
• scripts/: Python scripts for data processing and model training.
• requirements.txt: List of required Python libraries.

Model Details

Model Selection

Several machine learning models were evaluated for this multi-class classification problem. The final model chosen for deployment is a Random Forest classifier due to its high accuracy and robustness.

Model Training

The model was trained using the following steps:

1. Data Preprocessing: Handling missing values, scaling features, and encoding categorical variables.
2. Feature Selection: Identifying the most important features contributing to the prediction.
3. Model Training: Training the Random Forest classifier with hyperparameter tuning using GridSearchCV.

Model Evaluation

The model's performance was evaluated using accuracy, precision, recall, and F1-score. Cross-validation was also performed to ensure the model's robustness.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any enhancements or bug fixes.