Soil organic carbon variability assessment using satellite imagery and artificial neural network - MUNDADA, SHYAMAL; JAIN, POOJA (The Indian Journal of Agricultural Sciences, 2025)
The present study was carried out during 2022–2024 at Indian Institute of Information Technology, Nagpur, Maharashtra to evaluate SOC stocks in the Dhamtari district of Chhattisgarh, India. Two machine learning models and their variants-Boosted Regression Tree, Boosted Regression Tree with Early Stopping, Multilayer Perceptron, and Multilayer Perceptron with Early Stopping were used for predicting Soil Organic Carbon (SOC). The findings of the research indicated that Multilayer Perceptron produced better results in both scenarios that is, without and with Early Stopping technique applied. Multilayer Perceptron with Early Stopping model recorded nearly the same RMSE for both calibration and validation datasets as 0.1618 and 0.1601, respectively. Produced soil maps will assist farmers in adopting accurate information for decisions which will boost farm output and offer security for food through the balanced use of nutrients.