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@article{149903,
        author = {Mahalaxmi and Kaveri and Chaitra and Ashwini Telang and Giriraj Patil},
        title = {Classification of agricultural soil contamination using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {7},
        number = {1},
        pages = {1042-1048},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=149903},
        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.},
        keywords = {Machine Learning, Degree of Contamination, Soil Degradation, Hydrocarbons etc.},
        month = {},
        }