Plant Growth Analysis using IoT and Reinforcement Learning Techniques for Uncontrolled Environment

Abstract

Monitoring and optimizing plant growth in uncontrolled environments, such as open fields or greenhouses exposed to dynamic climatic conditions, pose significant challenges. This study explores the integration of Internet of Things (IoT) sensors with Reinforcement Learning (RL) techniques to enhance plant growth management in such settings. IoT devices provide real-time data on critical environmental parameters, including temperature, humidity, soil moisture, light intensity etc. This data is transmitted to a central processing system, where RL algorithms analyze it to devise adaptive strategies. RL techniques, particularly model-free approaches like Q-learning or Deep Reinforcement Learning (DRL), enable continuous learning and optimization by interacting with the environment. The agent learns to adjust irrigation schedules, nutrient delivery, and artificial lighting based on environmental conditions to maximize plant health and growth metrics. The study presents a framework for deploying this IoT-RL ecosystem, emphasizing cost-effective sensor deployment, data transmission reliability, and energy efficiency. Experiments conducted in simulated and semi-uncontrolled environments demonstrate the system's ability to dynamically adjust to environmental fluctuations, reduce resource wastage, and improve yield outcomes. Key findings reveal that RL algorithms outperform rule-based systems by adapting to non-linear relationships and complex dependencies between environmental variables and plant growth. Furthermore, the framework provides a scalable solution suitable for diverse agricultural contexts, from small-scale farms to large agricultural enterprises. This research underscores the potential of IoT and RL in transforming agricultural practices by offering intelligent, data-driven solutions to manage plant growth in unpredictable environments. Future work will focus on integrating advanced sensing technologies, improving algorithm efficiency, and exploring the system's applicability across various crop types and geographical locations.