A Comprehensive Review of Optimized Riemannian Residual Neural Networks: An Advanced Energy-Efficient Environmental Monitoring in Precision Agriculture Using LoRa-Based Wireless Sensor Networks

Abstract

Precision agriculture has emerged as a critical solution for improving crop productivity, resource efficiency, and environmental sustainability. Wireless Sensor Networks (WSNs) integrated with Long Range (LoRa) communication technology have enabled large-scale monitoring of environmental parameters such as soil moisture, temperature, and humidity. LoRa-based systems provide long-range, low-power communication, making them highly suitable for energy-constrained agricultural environments. However, the massive volume of sensor data generated requires advanced analytical models for accurate decision-making. Recent advancements in deep learning, particularly Riemannian Residual Neural Networks (RRNNs), have shown promise in handling complex, non-Euclidean data structures and improving feature representation. These models extend traditional residual networks to Riemannian manifolds, enabling improved learning dynamics and higher accuracy in structured data analysis. Additionally, optimization techniques in WSN routing and energy prediction have significantly enhanced network lifetime and reduced energy consumption in agricultural monitoring systems. This review presents a comprehensive analysis of optimized RRNN architectures combined with LoRa-based WSN systems for energy-efficient environmental monitoring. It highlights recent methodologies, optimization strategies, system architectures, and challenges, providing insights into future research directions for intelligent and sustainable precision agriculture systems.