SOIL POLLUTION DETECTION USING MACHINE LEARNING: A REVIEW

Abstract

Soil pollution is a developing natural issue, which leads to extreme biological, horticultural, and general wellbeing issues. Conventional strategies for detection and to monitor soil pollution such as chemical analysis and manual sampling are very tedious, costly, and limited in scope. Machine learning (ML) presents a promising way to overcome these problems by automatic detection of pollutants, prediction of contamination trends, and optimization monitoring strategies. This paper reviews the present status, the difficulties and future capability of ML in the field of soil pollution detection and mitigation.