Improving Soil Characteristics Using Machine Learning in Different Environment - T. Poovizhi (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Given that soil composition influences nutrient cycling, biodiversity support, and water regulation, it is critical to the ecosystem’s health. It reduces greenhouse gas emissions by acting as a carbon sink and storing organic carbon. Maintaining arable land, preventing sedimentation in water bodies, and managing erosion are all significantly influenced by the texture and structure of the soil. Furthermore, because some soil constituents enhance the quality of the soil and water by absorbing and digesting pollutants, soil composition has an impact on pollution remediation. It is imperative to acknowledge the significance of soil content in order to sustain ecosystems and apply sustainable land management techniques. Research is mainly focused on evaluating precision, recall, true positive rate, and F-measure in order to forecast the Organic Matter Content in soil, such as that found in homes, farms, and forests. Machine learning methods like Naive Bayes, KNN, SVM, and Random Forest are employed in this study. The outcomes demonstrate that Random Forest outperforms other algorithms in the prediction of soil organic matter content..