Digital soil mapping of soil subgroup class information in Coimbatore district using decision tree approach

The study aimed to evaluate the effectiveness of Digital Soil Mapping (DSM) compared to traditional soil mapping methods, which can help implementing precise near-real-time smart agricultural applications. Conventional soil surveys, while informative, often lack detail and are labour-intensive. DSM addresses these limitations by integrating soil data with environmental covariates and classification algorithms. Four hundred forty soil profile data points were collected from various sources and grouped according to the USDA Soil Taxonomy at the soil subgroup level. Utilizing Landsat 8 satellite data and 33 environmental covariates, the decision tree algorithm generated 56 rules to predict soil classes. Key influencing factors identified include agro-climatic zones, physiography, mean annual minimum temperature, the green wavelength region of spectral data, rainfall, and geology. The model was trained on 348 data points and validated on 92 data points, achieving a classification accuracy of 79.35% and a Kappa coefficient of 0.78, indicating high reliability. The study concludes that DSM is a viable alternative to conventional soil mapping methods, primarily using decision tree algorithms. It demonstrates that the accuracy of DSM can be significantly enhanced by incorporating a larger number of soil profile observations and relevant environmental covariates. The expert system approach provides a more detailed and up-to-date understanding of soil distribution, crucial for agricultural planning and natural resource management in the Coimbatore district, Western Tamil Nadu.