Machine Learning-Based Prediction Of Soil Quality Degradation From Agricultural Land Use Patterns

Abstract

One area of Global concern is soil degradation, since secondary controllers are unsuitable land use to support agricultural land usually through unsustainable land use practices, which drain out the nutrients, and negate the long-term yield. This study examines how machine learning can be applied to determine soil quality degradation on the basis of soil parameters and land use patterns. A sample of 3,000 samples comprised of variables like pH, organic matter, and nitrogen, phosphorus, irrigation frequency and crop type. Four machine learning algorithms that include Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were implemented and tested. The findings showed that ANN recorded the best prediction performance of 93.5 percent, but then RF contributed to 91.2 percent, SVM contributed to 88.7 percent, and DT contributed to 84.5 percent. The analysis of the feature importance included listed the organic matter (0.28), the nitrogen (0.24) and the frequency of irrigation (0.18), as the most significant variables affecting soil deterioration. A comparison with similar works proved that this framework presented a better performance as it combined multi-dimensional datasets and sophisticated algorithms. The results highlight the potential of machine learning to offer precise and data-based solutions to soil management to be able to engage in more sustainable agricultural activities and food security. The present study creates a strong framework that can be applied to larger datasets and in scenarios with fewer geographical coverage.