.jpg)
The integration of Internet of Things (IoT) and Edge Artificial Intelligence (AI) has revolutionized precision agriculture by providing real-time, data-driven insights for optimizing farming practices. As IoT devices proliferate in agricultural environments, the need for robust and scalable systems that can seamlessly handle vast amounts of data becomes critical. This chapter explores the challenges and opportunities presented by large-scale IoT deployments in agriculture, focusing on the role of Edge AI in ensuring real-time processing and decision-making. Key issues such as device heterogeneity, scalability, interoperability, and data security are examined, alongside strategies for effective system integration and validation. The chapter delves into case studies that illustrate the successful deployment of IoT and Edge AI technologies in diverse agricultural settings, highlighting lessons learned and best practices. Furthermore, it outlines future directions for enhancing system efficiency, ensuring secure data transmission, and fostering widespread adoption of these technologies in precision farming. By addressing these challenges, the chapter provides a comprehensive framework for developing and deploying scalable IoT and Edge AI systems that can drive sustainable agricultural practices worldwide.
The agricultural sector, essential for global food production, is undergoing a significant transformation driven by advancements in digital technologies [1]. Among these innovations, the integration of Internet of Things (IoT) and Edge Artificial Intelligence (AI) stands out as a game-changer in precision farming [2]. IoT devices, such as sensors, drones, and automated systems, are increasingly being deployed to collect vast amounts of real-time data from farm environments [3]. This data, ranging from soil moisture levels to crop health indicators, has the potential to significantly enhance farm management practices, optimizing resource use, and improving overall agricultural productivity [4]. However, harnessing the full potential of these technologies requires overcoming substantial challenges related to scalability, data interoperability, and system security, particularly in large-scale agricultural operations [5].
The deployment of IoT devices in precision farming has created a network of interconnected systems that continuously collect and transmit data [6]. However, as the number of devices in these networks grows, ensuring effective communication between them becomes increasingly difficult [7]. IoT devices often come from different manufacturers, use varying communication protocols, and operate under different conditions, which makes interoperability a significant concern [8]. This lack of standardization can lead to issues with data consistency, device compatibility, and communication inefficiencies, hindering the seamless integration of IoT systems into farming operations [9]. Edge AI plays a crucial role in addressing these challenges by enabling localized data processing, reducing the reliance on centralized cloud computing and improving system efficiency [10].
One of the most significant advantages of integrating Edge AI into IoT systems for agriculture is its ability to process data in real time, close to the source [11]. This local processing is particularly valuable in agricultural environments, where decisions such as irrigation, pest control, and nutrient management need to be made quickly and efficiently [12]. Without Edge AI, the vast amounts of data generated by IoT devices would need to be transmitted to a central server, which could introduce delays due to bandwidth limitations and latency [13]. By performing data analysis at the edge, in proximity to the IoT devices [14], Edge AI reduces these delays, enabling faster decision-making and allowing for more responsive interventions in farm management [15].
