Simulation and Performance Analysis of Automated Systems in Precision Agriculture for Crop Yield Optimization

Abstract

Automated systems have emerged as a transformative force in precision agriculture by integrating sensing technologies, robotics, and advanced modelling frameworks for yield optimization. This study conducts a comprehensive simulation-based performance analysis of automated agricultural systems using
APSIM, AquaCrop, and agent-based modelling integrated within a dynamic digital twin environment. High-resolution data derived from soil-moisture sensors, multispectral aerial imagery, canopy-temperature indices, and location-specific climate measurements form the primary inputs for the simulation ecosystem. Several automated management scenarios, including dynamic irrigation scheduling, adaptive nitrogen delivery, and variable-rate seeding, are tested to evaluate their impact on agricultural productivity and resource-use efficiency. Simulation results show that automated irrigation systems reduce water consumption while maintaining yield stability, automated nitrogen delivery enhances nutrient-use efficiency, and variable-rate seeding improves plant population uniformity. Digital twin synchronization further strengthens predictive
accuracy and operational adaptability. The findings highlight the utility of simulation-first evaluation as an essential step toward the safe, sustainable, and economically viable deployment of autonomous farm technologies. By demonstrating an average simulated yield increase exceeding twenty percent compared with uniform manual management, this study affirms the critical role of modelling-supported automation in shaping data-driven, resilient, and high-performance agricultural systems.