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https://github.com/themihirmathur/soiligator
Soiligator is an advanced machine learning project designed to optimize irrigation management by predicting whether irrigation is necessary based on environmental and soil-related data.
https://github.com/themihirmathur/soiligator
auc-score logistic-regression machine-learning matplotlib numpy pandas python random-forest-classifier roc-curve scikit-learn seaborn standardscaler support-vector-machine
Last synced: 15 days ago
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Soiligator is an advanced machine learning project designed to optimize irrigation management by predicting whether irrigation is necessary based on environmental and soil-related data.
- Host: GitHub
- URL: https://github.com/themihirmathur/soiligator
- Owner: themihirmathur
- Created: 2024-12-30T17:08:57.000Z (16 days ago)
- Default Branch: main
- Last Pushed: 2024-12-30T17:29:36.000Z (16 days ago)
- Last Synced: 2024-12-30T18:30:42.518Z (16 days ago)
- Topics: auc-score, logistic-regression, machine-learning, matplotlib, numpy, pandas, python, random-forest-classifier, roc-curve, scikit-learn, seaborn, standardscaler, support-vector-machine
- Language: Jupyter Notebook
- Homepage: https://github.com/themihirmathur/Soiligator
- Size: 228 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Soiligator 💦⏳ Soil Analysis and Irrigation Prediction
## Overview
**Soiligator** is an advanced machine learning project designed to optimize irrigation management by predicting whether irrigation is necessary based on environmental and soil-related data. Leveraging feature engineering and robust predictive models, Soiligator provides actionable insights that improve agricultural efficiency and sustainability.
## Key Features
- **Predictive Models**: Utilizes Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms for accurate irrigation predictions.
- **Feature Engineering**: Incorporates non-linear interaction terms and outlier handling for enhanced model performance.
- **Scalable Design**: Easily extendable to include additional features like soil type and crop variety.
- **Data Resilience**: Designed to handle label noise and outliers, ensuring robustness in real-world applications.
## Table of Contents
1. [Overview](#overview)
2. [Key Features](#key-features)
3. [Installation](#installation)
4. [Usage](#usage)
5. [Data Description](#data-description)
6. [Model Training and Evaluation](#model-training-and-evaluation)
7. [Results](#results)
8. [Future Work](#future-work)
## Installation
To use this project, install the required Python packages with the following command:
```bash
pip install -r requirements.txt
```
### Key Dependencies:
- **pandas**: Data manipulation and analysis
- **numpy**: Numerical operations
- **matplotlib & seaborn**: Data visualization
- **scikit-learn**: Machine learning model training and evaluation
Alternatively, install the libraries manually:
```bash
pip install pandas numpy matplotlib seaborn scikit-learn
```
## Usage
### Data Loading
Start by loading the dataset `modified_irrigation_dataset.csv`, which includes:
- **Moisture**: Soil moisture content.
- **Temperature**: Ambient temperature.
- **Humidity**: Air humidity level.
- **Irrigation_Needed**: Target label indicating whether irrigation is required.
### Running the Code
The implementation is available in a Jupyter Notebook: `soil_analysis.ipynb`. Execute the cells sequentially to:
1. Load and preprocess the dataset.
2. Engineer additional features.
3. Train machine learning models.
4. Evaluate and compare model performance.
## Data Description
The dataset comprises features representing soil and environmental conditions:
- **Moisture**: Measures the water content in the soil (0–100%).
- **Temperature**: Ambient temperature in degrees Celsius.
- **Humidity**: Air humidity as a percentage (0–100%).
### Engineered Features:
- **Moisture_Temp_Interaction**: Interaction term between soil moisture and temperature to capture non-linear effects.
- **Humidity_Squared**: Non-linear transformation of humidity to account for atmospheric retention properties.
### Data Challenges:
- **Outliers**: Synthetic outliers introduced in 5% of the data to test model resilience.
- **Label Noise**: Added noise to 5% of target labels to simulate real-world conditions.
## Model Training and Evaluation
### Preprocessing
- **Outlier Handling**: Removes or neutralizes extreme values.
- **Feature Scaling**: Standardizes features using `StandardScaler` for optimal model performance.
- **Train-Test Split**: Splits the data into 80% training and 20% testing subsets.
### Models Used:
1. **Logistic Regression**: A baseline model for binary classification.
2. **Random Forest Classifier**: An ensemble learning model for handling complex patterns.
3. **Support Vector Machine (SVM)**: A robust classifier for high-dimensional data.
### Evaluation Metrics:
- **Accuracy**: Overall correctness of predictions.
- **Confusion Matrix**: Breakdown of true positives, false positives, true negatives, and false negatives.
- **ROC Curve and AUC Score**: Measures the model's ability to distinguish between classes.
- **Precision-Recall Curve**: Highlights performance in handling imbalanced data.
- **Classification Report**: Includes precision, recall, F1-score, and support.
## Results
### Model Comparison:
- **Logistic Regression**: Achieved baseline performance with moderate accuracy.
- **Random Forest**: Outperformed other models, achieving high accuracy and robustness to noise and outliers.
- **SVM**: Demonstrated strong performance on standardized features but required longer training times.
### Visualization:
- **Confusion Matrix**: Provided for each model to analyze prediction errors.
- **ROC Curves**: Highlighted the trade-offs between sensitivity and specificity.
- **Precision-Recall Curves**: Demonstrated model effectiveness on imbalanced datasets.
## Future Work
1. **Hyperparameter Tuning**: Optimize models using Grid Search or Random Search to improve accuracy.
2. **Feature Expansion**: Include additional predictors such as:
- Soil type
- Crop type
- Real-time weather forecasts
3. **Time-Series Analysis**: Incorporate temporal data to predict irrigation needs over time.
4. **Deployment**: Package the model into a web or mobile application for practical use by farmers and agricultural experts.
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## Contribution
Contributions are welcome! Please fork the repository, make your changes, and submit a pull request. For any queries, feel free to contact the project owner.