Explaining Multi-objective Multispectral Models for Soil Parameters’ Retrieval

Estimating soil biophysical parameters from multispectral imagery is essential for precision agriculture, yet deploying accurate machine learning pipelines presents significant computational challenges. Individual soil trace elements often require specific model configurations, making traditional exhaustive hyperparameter optimization methods prohibitively time-consuming for resource-constrained embedded systems. In this study, we present a rigorous comparative analysis of two hyperparameter optimization strategies for multi-target soil estimation: the conventional, element-specific grid search, and the multi-objective Non-dominated Sorting Genetic Algorithm II (NSGA-II). Moreover, three explainable artificial intelligence methods were employed to analyze the behavior of the developed models and to provide insights into the contribution of input features to their predictions.