Soil Health Prediction Using Supervised Machine Learning Technique

Agriculture is one of the major fields in India that has been overlooked by technical touch. The application of artificial intelligence derivatives such as machine learning and deep learning to agricultural practises aids in crop production and soil health maintenance. The health of an agricultural field is primarily concerned with the preservation of soil nutrients, such as chemical and physical properties, by properly transmitting supplements. When soil health is managed scientifically, it gradually aids in high yield production and the long life of cultivation land. The soil data collected from soil testing centres is used to build the ontology. Ontology is constructed in such a way that it demonstrates the knowledge and relationship between soil and its chemical nutrients. The knowledge base is then used to connect the nutrient and soil type. Machine learning comes with useful and best-in-class algorithms for managing soil health and classifying it into healthy and unhealthy categories. In this study, obvious machine learning algorithms are used to efficiently classify the soil into two classes: healthy and unhealthy. To classify the data, algorithms such as logistic regression, Decision tree, Random tree classifier, Support Vector Machine, and XGBoost were used, and their algorithmic efficiency was increased through hyper parameter tuning using various techniques. Keywords: Soil health, chemical fertility, Supervised Learning, SVM, Decision Tree, Logistic Regression, Ensemble technique