Anomaly Detection in Soil Health Attributes using Machine Learning
Author: Chris Noel Manlunas
Role: Data Analyst
Date: December 2024
This project explores the detection of anomalies in critical soil health attributes—pH, Carbon, and Nitrogen—to evaluate and manage soil conditions effectively. By leveraging machine learning techniques (Isolation Forest), the project identifies irregular patterns that could indicate potential soil health issues, ensuring sustainable agricultural practices and improved crop yield.
- Trend Analysis: Track soil attribute variations over time.
- Anomaly Detection: Use machine learning to detect soil attribute outliers.
- Impact Assessment: Evaluate how anomalies affect overall soil health.
- Recommendations: Develop actionable strategies to mitigate anomalies and restore soil balance.