Deep Learning and Riemannian Residual Networks for Energy-Efficient Precision Agriculture Monitoring

Abstract

The integration of deep learning and optimization techniques in precision agriculture has significantly enhanced environmental monitoring systems, particularly with the emergence of energy-efficient wireless sensor networks. This paper presents a comprehensive review of advanced deep learning approaches, focusing on optimized Riemannian Residual Neural Networks (RRNNs) for precision agriculture applications. RRNNs extend traditional residual neural networks to non-Euclidean domains, enabling effective learning on manifold-structured data commonly encountered in agricultural environments such as spatial-temporal sensor data and environmental variables. The incorporation of Low-Power Wide-Area Network (LPWAN) technologies, particularly LoRa-based wireless sensor networks, facilitates long-range communication with minimal energy consumption, making them suitable for large-scale agricultural deployments. These networks enable real-time monitoring of soil moisture, temperature, humidity, and crop health parameters. This review critically analyses recent advancements in deep learning models, optimization strategies, and IoT-enabled architectures for smart farming systems. It also explores the challenges associated with computational complexity, energy efficiency, scalability, and data heterogeneity. Furthermore, comparative analysis highlights the superiority of hybrid models integrating Riemannian optimization and residual learning techniques. The study concludes that optimized RRNN-based frameworks, combined with LoRa-based WSNs, provide a promising solution for sustainable and intelligent precision agriculture systems.