## 2 Related Work  
Over the last years, a large majority of the research dealing with agricultural crop monitoring has focused on the development of small-scale testbeds and specialized applications built on wireless networks formed by fixed sensors (WSN) [3; 9; 10]. [PERSON] et al. [11] presented an example of such works that rely on infrastructure, where all the devices--a meteorological station, a sprinkler system and humidity sensors deployed a the field; a base station installed near the field and connected to the network infrastructure, which plays the role of gateway between the network of sensors; and the infrastructure--are in radio range. Some works consider the issues of distances and of intermittent connections--mainly induced by the duty cycles of sensors for energy saving purposes--between the devices, and solves them by adding redundant relays and gateways to build a connected network. A few works dealing with precision and sustainable agriculture propose to exploit the mobility of devices carried by people or embedded in agricultural vehicles (e.g., farm tractors and agribots) in order to collect data generated by sensors or to deliver data to actuators (e.g., sprinkler system and agribots). Such a collection process, which leverages the contact opportunities between devices, is referred as being opportunistic sensing. Opportunistic sensing and communication techniques, which have an indubitable interest in the agricultural domain, have also been considered in a few systems that propose to exploit mobile devices (carried by people or embedded in vehicles) to collectively measure social or environmental data in large-scale pervasive environments. These systems rely on opportunistic and people-centric sensing techniques. They differ from first-generation sensor networks, not only in their goal to support concurrent people-centric and opportunistic sensing applications in urban environments, but also in their hardware and software heterogeneity, high volatility, and very large scale. CarTel [12], MetroSense [13], MobiScopes [14], SenseWeb [15], Urbanet [16] and Urban Atmospheres [17] are examples of such systems. To report information from a given region using these systems, applications run sensing tasks on the mobile devices that are located in this region and that have chosen to participate to the sensing process. Mobile devices report sensor data through opportunistic network connections, such as those they establish with third-party access points existing in their radio range. For instance in CarTel, a network node is a mobile device coupled to a set of sensors. Each node gathers and processes sensor data locally before delivering them to a central portal on the Internet, where the data are stored in a database for further analysis and visualization. Mobile devices opportunistically communicate, using their wireless interface (e.g., Wi-Fi and Bluetooth), with other CarTel mobile devices and with Internet access points found in urban areas. The portal and the mobile nodes use a delay-tolerant network stack, called CaNet, to communicate. CarTel applications run on the portal, and submit SQL queries to a delay-tolerant continuous query processor called ICEDB. This query processor maintains a list of queries submitted by the applications, and pushes them to remote nodes using CaNet. It receives results from remote nodes, and puts them into a relational database. MetroSense adopts a similar approach, and envisions a future Internet in which a large part of the data traffic is generated by sensors carried by people during their daily life. BikeNet [18] and CenceMe [19] are examples of applications based on the MetroSense system. In BikeNet, bicycles equipped with multiple sensors communicated with neighboring bikes as well as the network infrastructure, and deliver sensing data to a Web portal that promotes social networking among cyclists. Similarly, in CenceMe, mobile phones collect data about a user's activity and share it with buddies via a social network.