Machine Learning for Soil Classification: Challenges and Opportunities

Abstract

In agriculture and environmental science, soil classification is essential for making well-informed decisions about crop selection, land management, and environmental protection. However conventional methods of classifying soil require a lot of work and time, and they mostly rely on human expertise. This work investigates the possibilities of machine learning (ML) models to automate soil classification utilizing large datasets of soil samples to overcome the shortcomings of existing techniques. In this paper, many machines learning techniques, including support vector machines (SVM), decision trees (DT), random forests (RF), and neural networks (NN), are examined for the classification of soil. There are certain models that work better than others, though, and this is based on the qualities of the soil samples. In addition to that, experiments using Random Forest, Naïve Bayes, and k-Nearest Neighbor (k-NN) were also undertaken. Classification strategies are being chosen to create a classified model using data mining. The algorithm with the highest accuracy is Random Forest (97.23%), followed by Naïve Bayes (96.82%), and k-Nearest Neighbor (k-NN), which has the lowest accuracy (92.92%). The paper highlights the challenges of applying machine learning to soil classification, such as consistency and human specialist availability, to effectively categorize soil samples. The results indicate that, despite these challenges, ML models present a potential substitute for labor-intensive conventional methods in the classification of soil.