Machine learning can identify the sources of heavy metals in agricultural soil: A case study in northern Guangdong Province, China

This case study demonstrates how machine learning can effectively identify the sources of heavy metal contamination in agricultural soil, specifically in northern Guangdong Province, China. By analyzing soil composition data, machine learning algorithms can pinpoint the origins of pollutants, enabling targeted remediation efforts. This research offers a powerful tool for environmental management and agricultural sustainability, helping to ensure food safety and protect ecosystems from harmful contaminants.