Edge computing for real-time precision agriculture applications

Precision agriculture increasingly depends on real-time (RT) processing of data to improve efficiency and sustainability, particularly in rural or resource-limited settings, where conventional cloud-based strategies are limited by network latency, bandwidth, and system robustness. This chapter presents an edge computing-centric architecture involving (IoT) sensors, edge devices, and lightweight machine learning models to support RT sensing, decision-making, and actuation in agriculture processes, enhancing soil moisture monitoring, pest identification, and irrigation scheduling. Utilizing a hybrid approach involving prototype creation, experimental deployment within a testbed of controlled agriculture, and comparative performance assessment, the system is tested on the criteria of latency, accuracy, energy efficiency, and fault tolerance. Comparative evaluation with existing cloud-based approaches shows enhanced processing time and resistance to network and power constraints. The architecture uses decentralized edge computing to reduce reliance on centralized cloud infrastructure, taking advantage of effective communication protocols such as LoRa, ZigBee, and MQTT. Findings indicate that the edge-based solution reduces decision latency and improves RT responsiveness considerably, providing a scalable, cost-efficient, and reproducible model for smart farming and advancing sustainable agricultural automation.