  Plant biomass estimation is critical due to the variability of different
environmental factors and crop management practices associated with it. The
assessment is largely impacted by the accurate prediction of different
environmental sustainability indicators. A robust model to predict
sustainability indicators is a must for the biomass community. This study
proposes a robust model for biomass sustainability prediction by analyzing
sustainability indicators using machine learning models. The prospect of
ensemble learning was also investigated to analyze the regression problem. All
experiments were carried out on a crop residue data from the Ohio state. Ten
machine learning models, namely, linear regression, ridge regression,
multilayer perceptron, k-nearest neighbors, support vector machine, decision
tree, gradient boosting, random forest, stacking and voting, were analyzed to
estimate three biomass sustainability indicators, namely soil erosion factor,
soil conditioning index, and organic matter factor. The performance of the
model was assessed using cross-correlation (R2), root mean squared error and
mean absolute error metrics. The results showed that Random Forest was the best
performing model to assess sustainability indicators. The analyzed model can
now serve as a guide for assessing sustainability indicators in real time.
