Stubble Burning Effect On Soil's Dielectric Behavior: An Exploration Of Machine Learning-Based Modelling Approaches

Stubble burning is a conventional technique of residue management that has affected the physio-chemical properties of the soils. In soil sciences, dielectric properties of soils using radio and microwavebased remote sensing have huge applications. Thus, presented paper has studied the burning effects of stubble on soil's physical, chemical and dielectric properties (epsilon ' and epsilon ''). Moreover, the experimentally observed soil's dielectric data has been explored with various classical Machine Learning (ML) and Neural Network (NN) based regression models. The soil samples were taken from the fields of Punjab, India, in the October-November months following a multistage soil sampling method. Then, Dak-12 open-ended coaxial probe (DOCP) has been used in alliance with a two-port Vector Network Analyzer (VNA) E5071C, Agilent Technologies, to investigate the dielectric properties of soil samples. The obtained results indicate that physio-chemical and dielectric properties have been strongly affected by burning as well as because of the presence of high concentrations of ash residues.epsilon ' and epsilon '' variations with depth indicate that ash residues can seep up to depths of 10 cm in a single burning process. Moreover, the continuous burning of stubble can have permanent effects on soil's properties. Among considered regression models, the Deep NN-based regression model has given the most accurate predictions of the regressor variables epsilon ' and epsilon '', with a root-mean-square-error (RMSE) of 0.06 and 0.07, respectively. Stubble burning has visible effects on physical, chemical as well as dielectric properties of soil. The burning of stubble damages natural ecosystem and essential nutrients which decrease fertility of soil. Also, the resultant residue ash becomes permanent part of soil profile and alters basic properties of soil. Moreover, exploration of ML-based regression models suggests the tremendous applications of data-centric models in soil and material sciences.

Automated summary

The study focuses on the effects of stubble burning on the physical, chemical, and dielectric properties of soil. The burning process significantly alters soil characteristics and increases the concentration of ash residue. The use of machine learning and neural network regression models accurately predicts the dielectric variables of soil. Stubble burning has visible effects on the physical, chemical, and dielectric properties of soil, leading to a decrease in soil fertility.