Regression Models and Multi-Objective Optimization Using the Genetic Algorithm Technique for an Integrated Tillage Implement

This study presents an experimental and computational analysis of the specific draft (SD) and specific torque (ST) requirements of an energy-efficient tillage implement, the active–passive disk harrow (APDH). Soil bin trials were conducted to develop multiple regression models predicting SD and ST based on operational parameters such as gang angle (α), speed ratio (u/v), soil cone index, and working depth. Model’s accuracy was assessed through statistical indices such as R2, RMSE, MIE, and MAE. The high R2 and low RMSE confirmed the reliability of the developed models in capturing the relationships between input and output variables. A genetic algorithm-based multi-objective optimization was implemented in MATLAB R2016a to determine optimal operational settings that minimize total power consumption while maximizing soil pulverization. The optimized values of α and u/v were determined to be in the ranges of 35.91° to 36.98° and 3.27 to 3.87, respectively. Model validation with laboratory and field data demonstrated acceptable prediction accuracy despite minor deviations attributed to soil variability and measurement errors. The developed models provide a predictive framework for optimizing tillage performance, aiding in tractor-implement selection, and enhancing energy efficiency in agricultural operations.