AI-Powered Optimization of Robotic Swarms for Agricultural Automation

Resumo

Agricultural automation using robotic swarms is a promising solution to address the challenges of labor shortages, resource inefficiency, and increased demand for sustainable food production. However, optimizing the coordination, scalability, and performance of these robotic swarms remains a complex problem due to diverse environmental conditions and task requirements. This study leverages AI-powered optimization techniques, including reinforcement learning and swarm intelligence algorithms, to enhance the efficiency and adaptability of robotic swarms for agricultural tasks such as precision planting, weeding, and harvesting. The proposed methodology integrates real-time data analytics with adaptive task allocation, enabling dynamic swarm reconfiguration based on field conditions. Simulation results indicate a 25% increase in task efficiency and a 30% reduction in energy consumption compared to traditional rule-based approaches. Additionally, field trials demonstrated a significant improvement in crop yield prediction accuracy (R² = 0.92) and a 20% reduction in resource wastage. The findings underscore the potential of AI-optimized robotic swarms to revolutionize agricultural automation by improving productivity and sustainability. Future research will focus on integrating IoT and edge computing for real-time swarm management, aiming to further enhance scalability and operational resilience