## 7 Conclusions  
Precision agriculture needs CASs. A CAS that contains a WSN and a DSS can reduce the work of farmers but also improve precision in farming activities. This paper presents our experience in developing a smart irrigation CAS. This system has three advantages: (1) it automates a manual  
Figure 22: DSS experiment during the period from 21/08/2013 to 03/09/2013. irrigation method based on the support of specialists and a real experimental dataset, (2) it uses two new ontologies called CASO and IRRIG that extend other well-known ontologies such as SSN and SAREF, and (3) it provides several rules that can work for other similar systems. This paper first describes the methodology of CAS development based on ontologies and details each step of the proposed methodology. Second, this paper also shares an example of development. Finally, an evaluation of the DSS in this system is also published. A smart irrigation CAS is a complex system. This system requires different roles to participate and contribute to development, such as system developers, ontologists, farmers and agronomists. In developing the DSS, we encountered several challenges. First, because of the participation of different roles from different domains, sharing the same vocabulary and understanding the agronomist model correctly is complicated. Thanks to the SSN ontology and its precise modeling pattern, the design activity produced a clear understanding of the output of each aggregation process. Second, the development of a DSS requires the development of ontologies. There are many methodologies used to develop an information system and other methodologies used to develop an ontology. To the best of our knowledge, this work is the first to combine the two types of methodologies. In the case of CASO and IRRIG, reusing standard and well-known ontologies was a decision made to increase interoperability with other systems and datasets. However, in practice, this activity was actually time-consuming. When reusing ontologies, one truly needs to understand the semantics and consequences behind every axiom and definition. It becomes even more complicated when several ontologies need to be combined, such as SSN/SOSA and SAREF. Based on this experience, it can be stated that building an ad hoc model from scratch would require less time and consume fewer resources; therefore, the decision of reusing ontologies is based completely on whether interoperability is a requirement. In other words, should the sensor streams be reused by several DSSs? Third, the dataset provided by Arvalis is limited. Thus, to test the correctness and robustness of the DSS, more datasets should be used. As soon as the sensor network can be deployed on some maize plots, our DSS will be tested on this new dataset. We plan to perform more experiments in 2021. Finally, the IRRINOV(r) method itself cannot cover all situations; for example, it does not define the action when there are consecutive multiple rainy days. However, the automatic irrigation system needs a mechanism to address this issue. One solution is to use machine learning to train the system over data of different scenarios. If the training is sufficient, the system can learn the action based on the scale of the IRRINOV(r) method. The DSS is a part of a smart irrigation CAS that will be deployed at the experimental INRAE farm (The experimental farm is a part of the AgroetchnoPole, an ecosystem for agricultural machines and digital information systems developed by an alliance of several organizations and institutes in Europe. Two members of the alliance dedicated to develop AgroetchnoPole are INRAE and LIMOS.) located at Montoldre in France. In the future, the DSS system will be combined with other components, such as networks, sensors and actuators, to form a complete smart irrigation system. Conceptualization, Q.-D.N., C.R. and M.P.-V.; Project administration, J.-P.C. ; Software, Q.-D.N. ; Validation, Q.-D.N., C.R. and M.P.-V.; Writing--original draft, Q.-D.N., C.R. and M.P.-V.; Writing--review & editing, C.d.V. and J.-P.C.