Communication-Guided Multi-Mutation Differential Evolution for Crop Model Calibration

TL;DR

This paper introduces DE-MMOGC, a novel multi-mutation differential evolution algorithm guided by communication, combined with ensemble Kalman filter, to improve crop model calibration under uncertainty, outperforming traditional methods.

Contribution

The paper presents a new multi-mutation differential evolution algorithm with communication guidance and dynamic operator selection for crop model calibration under uncertainty.

Findings

DE-MMOGC outperforms traditional optimizers in crop model calibration.

The algorithm improves correlation with real LAI values.

It effectively handles missing observations and uncertain weather data.

Abstract

In this paper, we propose a multi-mutation optimization algorithm, Differential Evolution with Multi-Mutation Operator-Guided Communication (DE-MMOGC), implemented to improve the performance and convergence abilities of standard differential evolution in uncertain environments. DE-MMOGC introduces a communication-guided scheme integrated with multiple mutation operators to encourage exploration and avoid premature convergence. Along with this, it includes a dynamic operator selection mechanism to use the best-performing operator over successive generations. To assimilate real-world uncertainties and missing observations into the predictive model, the proposed algorithm is combined with the Ensemble Kalman Filter. To evaluate the efficacy of the proposed DE-MMOGC in uncertain systems, the unified framework is applied to improve the predictive accuracy of crop simulation models. These simulation models are essential to precision agriculture, as they make it easier to estimate crop growth in a variety of unpredictable weather scenarios. Additionally, precisely calibrating these models raises a challenge due to missing observations. Hence, the simplified WOFOST crop simulation model is incorporated in this study for leaf area index (LAI)-based crop yield estimation. DE-MMOGC enhances the WOFOST performance by optimizing crucial weather parameters (temperature and rainfall), since these parameters are highly uncertain across different crop varieties, such as wheat, rice, and cotton. The experimental study shows that DE-MMOGC outperforms the traditional evolutionary optimizers and achieves better correlation with real LAI values. We found that DE-MMOGC is a resilient solution for crop monitoring.