## 1 Introduction  
The growing scarcity of arable land due to various human and climatic factors, coupled with the growing global demand for food, underscores the need for sustainable and productive agricultural management [1, 2]. To address these challenges, the Internet of Things (IoT) is recognized as a powerful tool, owing to its potential to make agriculture more data-driven, leading to timely, economical, and efficient farming systems [3]. Precision agriculture solutions, which are becoming increasingly prevalent, involve management practices based on spatial measurements that utilize IoT technologies [4, 5]. For example, fertilizers can be applied only where necessary, and water management can be automated to minimize waste while adapting to the specific needs of crops [6, 7]. This is in addition to monitoring the parameters of the crops themselves [8, 9]. Smart agriculture is evolving beyond these applications, enhancing the use of spatial data in real-time events. Farmers can respond quickly to significant changes in weather, humidity, and air quality, as well as the health of crops and soil [10]. This is facilitated by systems that integrate sensors, intelligent agricultural vehicles, drones, and autonomous robots [11]. In these applications, the IoT supports the documentation and supervision of various activities, along with the traceability of products through systems for data analysis, visualization, and management. However, one of the main challenges in the design of IoT systems is addressing the need for interoperability between devices. It is estimated that interoperability could increase the potential market value of IoT by 40% [12]. Many complex IoT systems, such as smart agriculture, require the coordination of different IoT devices that often use different data formats [13]. The goal is to achieve _semantic interoperability_, defined as the ability to represent data in a form whose meaning is independent of the application that generates or uses it [14, 15]. In digital agriculture systems, most of the data exchanged between machines and heterogeneous services consist of readings from sensors and commands for actuators [16]. These data are generally numerical and specific to the type of device and the measurement domain, for example, a temperature reading, sometimes accompanied by additional information such as the nature of the measurements and the units of measure [17]. Alternatively, they may be control strings used to modify parameters or activate actuators [18]. Receiving-side applications must be able to understand and interpret the received data in order to process them and act accordingly [15]. In other words, the two IoT endpoints involved in communication must ensure _semantic interoperability_ in order to interact correctly. This implies that the data must be formatted in such a way that they can be interpreted identically by both ends involved in the message exchange. Indeed, in the field of precision agriculture, there is currently no dedicated and universally shared standard. There are various IoT standards that could meet the needs of this field by modelling data exchange between different sensors [19]. However, at present, the market offers a series of heterogeneous proprietary devices, each with its own specific mode of communication. The contribution of this research is, in the first phase, to analyse the available standards and evaluate the pros and cons of each, in order to choose the one that best meets the requirements in the field of precision agriculture. In the second phase, a new interoperable architecture is proposed to overcome the challenges associated with the standards and protocols present in the smart agriculture sector. The remaining sections of this article are organized as follows: Section 2 provides the background and highlights the contributions of this paper. Section 3 reviews the IoT standards and protocols used in precision agriculture. Section 4 introduces an interoperable IoT architecture designed for precision agriculture applications.