# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 3 The Sensor Layer  
The Sensor Layer is in charge of acquiring the data of the different climatic and soil variables involved in the growth and production of the crops. Data can be acquired either by ground sensors, located above or inside the soil, or by UAV sensors involving embedded cameras, location and other sensors. The Sensor Layer constitutes the cradle of modern EO approaches towards optimizing decision support in remote sensing monitoring systems. EO monitoring of cultivation areas is highly enhanced using autonomous UAVs. Popular EO techniques include satellite and radar-based technologies [13] towards estimating basic biophysical parameters in the fields, such as Leaf Area Index (LAI), crop height and water requirements [14; 15]. According to [16] radar-based technologies are used to enable the estimation of soil moisture spatial variability and can efficiently estimate LAI. However, EO can achieve study higher spatial resolutions only with calibration against accurate ground truth instruments that measure at within field scale resolutions. UAVs deploy EO via using photogrammetry techniques enabling the generation of three-dimensional digital surface models of the fields. In particular, autonomous, manual or GPS-based image acquisition, ortho-image generation and image triangulation and geo-referencing based on navigation sensors are employed. More information regarding these methods is provided in Sections 5 and 8.2. In the Sensor Layer each sensor sends the acquired data in the cloud through a WSN. The WSN is made up of sensor nodes that operate under a mesh or point-to-point topology, a coordinator node and a gateway. Each node in such a network is connected to one or more sensors [17]. In precision agriculture the most important types of sensors for measuring the different types of corps attributes are:  
* Optical Sensors/UAV Sensors: Optical sensors are usually embedded in UAVs and use light reflection information to measure the varying properties of soil and vegetation. In that case, the sensors acquire image data, which are further analyzed with photogrammetry techniques. Object detectors and pattern recognition form the basic building block for extracting information from the images. Such information may involve the vegetation and soil color, the moisture content and temperature of soil and vegetation, the position, height, size and shape of vegetation along with the level of chlorophyll. In this category we find visible light sensors, multispectral sensors, hyperspectral sensors and thermal sensors. * Electrochemical Sensors/Ground Sensors: These types of sensors acquire data regarding the nutrient contents of soil and its associated pH. Electrodes in these sensors work by detecting specific ions in the soil. Different families of electrochemical sensors can be recognized depending on the electrical magnitude used for transduction of the recognition event: potentiometric, which indicates change of membrane potential; conductometric, which indicates change of conductance; impedimetric, which indicates change of impedance; and voltammetric or amperometric, which indicates change of current for an electrochemical reaction with the applied voltage in the first case, or with time at a fixed applied potential in the latter. * Location Sensors/UAV Sensors: Location sensors are usually embedded in UAVs and provide spatial information regarding the positioning of an element. These types of sensors use signals from GPS satellites to determine latitude, longitude, and altitude to within feet. Three satellites minimum are required to triangulate a position. Precise positioning is the cornerstone of precision agriculture. GPS integrated circuits such as the NJR NJG1157 PCD-TE1 are a good example of location sensors. * Weather Stations/Ground Sensors: Weather stations are free-standing units situated at different locations throughout the cultivating fields.