# Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture
## 1 Introduction  
Developing sustainable systems for water management is one of the main research challenges in agricultural engineering [1]. Applying new tools and methodologies is necessary to increase the efficiency in using water by the crop and maintaining production levels [2]. The region of Murcia, Spain, is one of the most productive zones of agricultural products in its context. However, it is a water-deficit area with annual rainfall between 150 mm and 350 mm; availability and efficiency in water use are critical factors in this region. Thus, the development of new methods for water management and estimation of irrigation requirements of the crops is particularly important. Traditionally, irrigation decisions are done by farmers, who estimate the requirements based on historic values of the reference and crop evapotranspiration (_ETo_ and _ETc_, respectively). At most, it is done by estimating the current water consumption of the crop based on information offered by agrometeorological stations near the field. The management of the crop growth is an essential task that is done manually by the farmers. This know-how is tried to be replicated in the automatic monitoring systems using different parameters of the crops. Precision agriculture applies geospatial technology (such as remote sensing, GPS and GIS) to monitor the fields. Currently, high-resolution satellite imagery is very frequently applied to study changes in crop and soil. However, the prohibitive cost and limited availability of these resources encourage the development of alternative or complementary systems for agricultural applications. This way, images captured with drones and, in general, unmanned aerial vehicles (UAVs), have proved to be a feasible solution because of their high temporal and spatial resolution, reduced operational cost, and high flexibility in inflight programming. UAVs ground stations provide user interfaces including flight control, planning and image acquisition [3; 4]. Some authors have defined precision irrigation as \"the accurate and precise application of water to meet the specific requirements of individual management units or plants and minimize the adverse environmental impact\" [5]. To achieve this goal, new technologies have to be developed to create \"an irrigation system that knows what to do, knows how to do it, knows what it has done, and learns from what it has done\" [6]. For this purpose, using images of crop areas has proved to be adequate to estimate parameters such as the crop coefficient, _Kc_, and the evapotranspiration, _ET_[7; 8]. The lack of water resources has led to the development of automatic systems to evaluate and establish criteria to achieve savings in water consumption. The combination of information technology in sensor networks and in field stations [9; 10] is very beneficial for water management, allowing for an optimal control and monitoring in the use of water [11; 12; 13]. One of the specific techniques for managing the water in the crop and in the soil is the use of images [14]. The images of the green cover of the crop or the canopy give an idea of the amount of water the crop is losing by evapotranspiration [15]. The percentage of the green cover of the crop (PGC) is related to the crop evaporation model [16]. In this sense, the top view images of a crop can be processed for determining the PGC, which is then used for computing the crop coefficient, _Kc_, and then the crop evapotranspiration, _ETc_. For example, in the case of lettuce crops, the effective diameter of the plants can be estimated accurately [8], from which the root depth, plant height and _ETc_ are computed. In order to improve the evapotranspiration model of a crop, a 3D model can also be obtained [17]. In this case, the 2D images from several viewing angles can be integrated for obtaining 3D models of the crop.