Real-time Environmental Monitoring in Smart Agriculture using TinyML and Machine Learning

The necessity of this work stems from the ever-growing demand for sustainable agricultural practices in a world faced with environmental challenges and the imperative to optimize resource utilization. In the area of Smart Agriculture, where precision and efficiency are paramount, real-time environmental monitoring assumes a pivotal role for different use cases. Existing methods, while commendable, exhibit notable limitations. They often fall short in delivering the precision and speed required for monitoring and responding to dynamic environmental conditions. Moreover, their ability to adapt to contextual nuances is often constrained, resulting in suboptimal performance levels. To address these challenges, we propose a novel approach in the form of a Graph Neural Network (GNN) optimized with Q Learning. This model leverages the power of deep learning and reinforcement learning to enhance real-time environmental monitoring in Smart Agriculture sets. The incorporation of GNN enables the extraction of complex spatial dependencies within the agricultural ecosystem, while Q Learning optimizes decision-making, making it more context-aware for different scenarios. The advantages of our proposed model are multi-fold. First, it exhibits superior precision, accuracy, and recall, with a remarkable 5.9%, 5.5%, and 4.9% improvement, respectively, compared to existing methods. Additionally, it boasts an 8.3% higher Area Under the Curve (AUC), signifying its enhanced ability to discriminate between various environmental states. Furthermore, our model showcases a 4.5% increase in speed,