## 1 Introduction  
Precision Agriculture (PA), a new concept in crop management, has been applied in agriculture for some years now. The objective of PA is to avoid applying the same management practices to a crop regardless of site conditions and to improve field management from several perspectives; for example, it can help to minimize wastage of pesticides required for the effective control of weeds, diseases and pests and to ensure that crops receive adequate nutrients, leading to more efficient and greener agriculture [1, 2]. In other words, PA can be considered as a management strategy that utilizes information technology with the aim of improving production and quality. It therefore differs from conventional farming as it determines variation more precisely and links spatial data to management actions. It thereby allows farmers to tend their farm, crop and practices from an entirely new perspective. The field adoption of PA can be represented as a five-step cyclical process that covers data collection, diagnostic, data analysis, precision field operation, and evaluation [3]. In addition, at least three criteria are required for the implementation of PA: (1) Clear evidence of significant spatial and temporal variability in soil and crop conditions within a field and in fields within a region; (2) Ability to identify and quantify such variability; (3) Ability to reallocate inputs and adjust management practices in order to improve productivity and profitability while minimizing environmental degradation. The satisfaction of the second criterion is crucial for ensuring the success of the adoption of PA practices. Indeed, field data gathering is an essential stage in order to establish the state and the conditions of a crop. Historically, visual inspection--color estimation or mold localization--has been used for determining the state and conditions of a crop. Recently, more advanced tools have been used, such as physical sensors like anemometers or thermometers, and more complex electronic sensors (e.g., pH sensors, gas sensors or hyperspectral analyzers). Nevertheless, data acquisition from these tools was cumbersome and not available in real time. Wired sensors networks have been applied as a solution to this problem, but due to the long distances and natural unstructured fields involved, Wireless Sensor Networks (WSN) technology has proven to be a better option [4,5]. Although the measured parameters have not changed in time, temperature, humidity and solar radiation still remain as the most important ones. The introduction of WSN nodes for farm monitoring has led to the possibility for supervising how parameters evolve in real time and how they are related. It thereby provides a powerful monitoring tool. Therefore it allows quick reactions or even predictive actions according to the circumstances. This is probably the reason why many applications have recently emerged using WSN in precision agriculture. Most WSNs, such as those described in [6-8], use WSN humidity sensors for evaluating the specific irrigation requirements for each specific area. Many other topics where WSN technology has been adopted include pesticide control [9], quality assurance [10], as well as global monitoring systems [11,12]. On the other hand, the use of Unmanned Aerial Vehicles (UAV) in agricultural tasks was pioneered by Japan during the 80s, when Yamaha received a request for developing a new kind of unmanned helicopter for crop spraying purposes [13]. UAVs have been later introduced in agriculture applications in order to overcome the limitation of conventional remote sensing systems, such as satellites or manned aircrafts, which have shown being inefficient to address agriculture requirements. High spatial resolution and image availability at critical crop stages are some main constraints in PA (_i.e._, the time window to obtain the required images is usually very small) [14].