## 1 Introduction  
In the agricultural domain, farmers need to observe natural phenomena to engage in appropriate activities on their fields. For example, in traditional irrigation, farmers go to their fields to examine the crop development stage and measure the soil moisture provided by probes in the soil. Then, they use practical experience or follow an irrigation method to estimate manually the water needs of their crops. Based on their estimations, the farmers decide whether to irrigate the fields. This conventional approach has two significant drawbacks. First, irrigation requires daily observations, often made by farmers. Manual observations are influenced by other factors such as the weather or the situation of farmers. For example, a daily observation could be skipped if the farmer is sick. Second, the resource shortage problem demands that farmers use water sparingly. Context-aware systems (CASs) can overcome the above-mentioned situations. A CAS uses a wireless sensor network (WSN) to monitor environmental phenomena and uses those measurements for further processes. In a CAS, context refers to \"any information that can characterize the situation of an entity. An entity could be a person, a place or an object that is considered relevant to the interaction between a user and an application, including the user and the application themselves\" [1]. A CAS has two contexts: a low-level context and a high-level context [2]. The low-level context contains quantitative data. The high-level context contains qualitative data that synthesize a situation and ease the decision. For example, the statement \"soil moisture is 160 centibar (cbar)\" presents a low-level context; however, the statement \"soil is dry\" presents a high-level context. The CAS has some peculiar characteristics. First, sensors in the system are heterogeneous. Each type of phenomenon demands a different type of sensor. For example, in agriculture, pluviometers measure rain quantity, and tensiometers measure soil moisture. Thus, the CAS should address some data interoperability issues. Second, the system should have the capability to process the raw measurement data and apply reason on them. To address the above requirements, ontologies are adequate candidates. Some sensor ontologies are provided by several institutes of standardization. Their main goal is to propose a shared data schema that precisely describes measurements to ease their interpretations. These descriptions are reusable by any machine and understandable by humans. They propose a data modeling design pattern that is precise enough to answer any informational need. Thus, any sensor measurements described by these ontologies may be reusable by several decision support systems (DSSs). The sensor network becomes a data provider for several applications. For example, rain quantity measurements can be used by irrigation decision systems and crop disease management systems. Reusing existing ontologies enables the harmonization of sensor measurement descriptions. Moreover, those descriptions share the same data model. Thus, they are easy to integrate in a global data schema. Furthermore, some ontologies enable reasoning over data because they propose logical descriptions. In short, ontologies used in context modeling can solve the heterogeneity issues of various sensor measurements and infer the high-level context and decisions by applying rules. To the best of our knowledge, the proposed work is the first to reuse two well-known ontologies: the new version of the Semantic Sensor Network (SSN) [3] and Smart Appliances REFerence (SAREF) [4]. This paper presents the development method of a CAS that combines two engineering methodologies: ontology and information systems. This method was used to build a smart irrigation CAS that automates a manual irrigation method. This irrigation method, called IRRINOV(r), was developed by Arvalis (Arvalis is an applied agricultural research organization dedicated to arable crops. It engages in many activities at 27 different local sites in France.)