Classification of agricultural soil contamination using Machine Learning

Classification of agricultural soil contamination using Machine Learning
Author(s):
Mahalaxmi, Kaveri, Chaitra, Ashwini Telang, Giriraj Patil
Keywords:
Machine Learning, Degree of Contamination, Soil Degradation, Hydrocarbons etc.
Abstract
Soil contamination has severely increased over the last years, especially due to petroleum hydrocarbons, heavy metals and pesticides from industrial wastes and human activities. Even though in general soil quality research is facing an important technological challenge and several actions have been taken in order to assess, remediate and reduce the effects of contaminants on soils, suitable and standardized monitoring and remediation strategies of soil are required. In this sense, in the last decade there has been a growing emphasis on the utilization of residues and waste materials, coming from different industrial activities, in several remediation technologies (e.g., chemical degradation, photo-degradation) and bioremediation in order to clean up contaminated soils. The critical point regarding contaminated soil monitoring is the intrinsic difficulty in defining fixed monitoring variables and indicators as the establishment of any a priori criterion and threshold for soil quality can be still considered subjective.
Article Details
Unique Paper ID: 149903 Publication Volume & Issue: Volume 7, Issue 1 Page(s): 1042 - 1048
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