Abstract
Abstract
Soil organic carbon (SOC) is central to soil function and to the global carbon cycle, yet most assessments still depend on laboratory analysis that can only be repeated a few times a year. We describe an Internet of Things (IoT) and machine learning framework that estimates SOC dynamics indirectly, through soft sensing of soil parameters that are measured continuously in a campus urban forest. The sensors do not measure SOC itself; they record soil moisture, pH, electrical conductivity (EC), temperature, and the macronutrient indicators nitrogen, phosphorus, and potassium (N, P, K) in the Telkom University Forest, Bandung, Indonesia. Monthly laboratory SOC measurements, collected from January to August 2025, provided the reference values used to calibrate and test the models. Raw readings were filtered against physical ranges and screened for outliers before being averaged to monthly values, which then served as model inputs. Of the algorithms we compared, Random Forest gave the best result, with an R² of 0.829, a mean absolute error (MAE) of 0.221, and a root mean square error (RMSE) of 0.363 for monthly SOC. While the model demonstrates moderate predictive accuracy, it robustly captures the seasonal dynamics of SOC, aligning with laboratory-validated trends. We therefore present the framework as a tool for early monitoring and trend detection that complements laboratory analysis instead of replacing it, and that brings soil-carbon assessment to a temporal resolution conventional sampling cannot reach.