Optimized Riemannian Residual Neural Networks for Energy-Efficient Precision Agriculture Using LoRa-Based Wireless Sensor Networks

Abstract

Precision agriculture has emerged as a transformative paradigm aimed at improving crop productivity, optimizing resource utilization, and ensuring environmental sustainability. The integration of advanced deep learning models with Internet of Things (IoT)-based sensing infrastructures has significantly enhanced real-time environmental monitoring. In this context, Riemannian Residual Neural Networks (RRNNs) represent a novel advancement in geometric deep learning, enabling efficient learning over manifold-structured data while overcoming challenges such as vanishing gradients and inefficient feature representation.  Simultaneously, LoRa (Long Range) based Wireless Sensor Networks (WSNs) provide a low-power, long-range communication framework suitable for large-scale agricultural deployments, ensuring energy-efficient data transmission from distributed sensors monitoring parameters such as soil moisture, temperature, and humidity.  This survey presents a comprehensive analysis of methods and architectures combining optimized Riemannian residual learning with LoRa-enabled environmental monitoring systems. It explores architectural innovations, optimization techniques, and hybrid AI-IoT frameworks that enhance energy efficiency and predictive accuracy in smart farming applications. Furthermore, the study evaluates recent advancements, identifies research gaps, and highlights future directions for integrating geometric deep learning with edge-based sensor networks. The findings demonstrate that the synergy between RRNN architectures and LoRa-based WSNs significantly improves scalability, reliability, and sustainability in precision agriculture systems.