Reinforcement Learning based Decentralized Approach for Precision Agriculture and Environment Monitoring

Environmental sensing and precision agriculture are critical to maximizing agricultural production and minimizing environmental footprint. Conventional centralized systems suffer from limitations in scalability, energy use, and adaption to a changing environment. This paper presents a decentralized multi-agent reinforcement learning (MARL) architecture combined with an Automatic Water Sprinkle and Monitoring System utilizing Internet of Things (IoT) technology, real-time analytics, and precision agriculture techniques. In the MARL framework, agents are dispatched across field zones, allowing them to autonomously control the use of resources (e.g., irrigation, fertilizer, and pest control) and to learn the most effective strategy to improve the crop yield and health. IoT-based sensors measure soil water content, temperature, relative humidity and nutrient status in co-operation with weather forecasts to enable adaptive allocation of resources. The decentralized architecture of the system has the capability of real time, closed-loop, decision level, scalability as well as decoupling from hierarchical, centralized control. Communicating between agents enables effective coordination, resource use optimization and lower operational costs. One of the system's important Ness is the solar powered system, this allows the system to be sustainable in rural and resource-scarce regions. IoT-based automation facilitates in-line data analysis and precision irrigation scheduling and control of resources. Experimental evidence reveals an average 28% reduction in water consumption, 18% reduction in fertilizer application, and 16% crop yield improvement, with a smaller footprint on the environment. The agent de-centralized architecture improves scalability, robustness against agent crashes, and flexibility for mobile agriculture. Through the integration of distributed Reinforcement Learning (RL), IoT, and renewable energy, this system provides an environmentally friendly and sustainably as well efficient solution for overcoming the problems of current agricultural practices and environmental management.