Design of an IoT based Monitoring System Utilizing Deep Reinforcement Learning and Ensemble Learning for Precision Agriculture

This research work proposes a novel IoT -based framework for smart farming in precision agriculture, incorporating state-of-the-art technologies such as Deep Reinforcement Learning, IoT -enabled nano-sensors with edge computing, and ensemble learning with multimodal data fusion. Despite the advancements in smart farming, current systems face several challenges, including inefficient resource allocation, delayed real-time monitoring, inaccurate predictive analytics, and data integration issues. Recent techniques, such as traditional machine learning models and basic sensor networks, have attempted to address these challenges but fall short in adapting to dynamic agricultural environments and handling heterogeneous data sources effectively. The primary challenges include high latency in data processing, poor accuracy in resource prediction, inability to provide timely alerts, and difficulties in integrating diverse data types from various sensors and imaging technologies. To overcome these limitations, our strategy uses DRL to learn from the dynamic agricultural environment and optimize a resource allocation strategy. The inputs include sensor data on soil moisture, temperature, humidity, NPK levels, historical yield data, and weather forecasts. A DRL model starts with naive policies and uses a state-action paradigm to update policies using algorithms such as Deep Q-Networks and Proximal Policy Optimization. This has resulted in an increase in yield by 12%, the efficiency of resource allocation by 15%, and a reduction in waste by 10%. For real-time, high-resolution monitoring, we deploy IoT-enabled nano-sensors with edge computing capabilities. These sensors collect detailed soil and environmental data, processed locally to reduce latency and bandwidth usage, achieving a 20% reduction in data latency, 25% bandwidth savings, and 18% improvement in real-time alert accuracy. Multimodal data fusion with ensemble learning enhances the accuracy and robustness of the prediction. This approach fuses heterogeneous data, including sensor data, satellite and drone imagery, for which different machine learning models-like Random Forest, Gradient Boosting, and Neural Networks-are trained with different subsets of data, and their outputs are fused using various techniques like Voting and Stacking. This enhanced the accuracy of prediction by 9%, resource optimization efficiency by 14%, and reduced anomalies by 8%.