| Abstrakt: |
Machine learning algorithms have revolutionized soil classification by enabling accurate and efficient identification of different soil types based on various attributes. This paper provides a comprehensive overview of four prominent machine learning algorithms, namely Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks, in the context of soil classification. It discusses the strengths and weaknesses of each algorithm, their real-world applications in precision agriculture, environmental monitoring, construction, and land-use planning, and the potential benefits they offer to these industries. The paper also highlights the importance of performance metrics such as accuracy, precision, recall, F1 score, and computational efficiency when evaluating these algorithms for soil classification tasks. By understanding the capabilities of these algorithms, researchers and practitioners can make informed decisions about the most suitable approach for their specific soil classification needs. [ABSTRACT FROM AUTHOR] |
