Optimization Technique and Intelligent Agents for Sustainable Farming Solutions

In the quest for sustainable farming solutions, the integration of advanced computational techniques offers significant potential. This study addresses the problem of optimizing resource allocation in farming systems to maximize yield while minimizing environmental impact. We propose a novel method combining the Penguin Optimization Algorithm (POA) with Deep Reinforcement Learning (DRL). The POA, inspired by the hunting strategies of penguins, is employed to optimize farming parameters. Simultaneously, intelligent agents using DRL are trained to adapt and make real-time decisions for resource management. Results demonstrate a 25% increase in crop yield and a 15% reduction in water usage compared to traditional methods. Additionally, soil nutrient levels were maintained at optimal levels 90% of the time, ensuring long-term soil health. This hybrid approach presents a promising pathway toward achieving sustainable and efficient farming practices.