# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 5 The Management Layer  
In a smart farming monitoring system, the basic component of intelligence is considered to be the study and filtering of the collected data. The use of EO techniques enable the advance of cultivation procedures and increase productivity by providing the base layer for spatial information analysis and monitoring of agricultural activities. A large percentage of smart agriculture applications are based on simulators, commercial programs and specific programming languages for implementing and controlling the data system. The Management layer uses modern software tools to efficiently satisfy multiple tasks, presented in Table 5.  
Information management is deployed so the farmer can consult, record and modify the information collected by the sensors in tables, statistical graphs and interactive maps. In addition, it can download daily, monthly and annual reports of historical data. However, the farmer can mainly see the current data of the monitored variables of one or all the WSN nodes and consult the history. The interaction with the network and services layer is achieved using an intermediate layer of management logic [36]. WSN data will be stored in an online database [37]. The system also enables Big Data analytics in agriculture monitoring by using tools such as the Apache Hadoop software and various Big Data hardware platforms [38] as possible resources. Big Data refers to information assets characterized by such a high volume, velocity and variety able to require specific technology and analytical methods for its transformation into value [39]. In precision agriculture smart applications, the collected data are recorded in a specific format, to discover patterns, correct errors, eliminate duplicate and inconsistencies and to solve noise problems. Big Data technologies are playing an essential role in modern farming systems, since predictive insights are provided regarding optimizing the quality of the crop, minimizing environmental impact, reducing costs, increasing profit and generally optimizing production efficiency. In addition, Big Data enable the management of real-time operational decisions and the redesign of business processes for advancing the food supply chain. Furthermore, the proposed architectural scheme promotes various data processing techniques that upgrade the impact of EO technologies in agriculture. EO by satellite leads to the acquisition of regular or spatially continuous data regarding large areas. It provides essential information on the functioningof fields and on the causes of environmental change. UAVs enable EO by using photogrammetry techniques to extract three-dimensional digital surface models of the field, as well as orthophotographs. One popular algorithm for this purpose is Structure from Motion (SfM) [40]. An orthophotograph can be used to create a realistic map of the fields by measuring true distances and providing to the farmer a clearer helpful view from the sky. Based on photographs, digital image processing tools, such as Pix4 Dmapper can calculate various vegetation indices that can lead to conclusions, either on each photograph individually or after the production of orthophotos regarding the crops' state. Vegetation indices are mathematical quantitative combinations of the absorption and scattering of plant in different bands of the electromagnetic spectrum [41]. Calculating vegetation indices will help to identify useful crop characteristics concerning important biological and physical parameters of the vegetation. The best-known vegetation index is the Normalized Difference Vegetation Index (NDVI), which is the evolution of the vegetation index ratio and is calculated by the visible and near infrared light reflected from the vegetation. Last but not least, the smart agriculture monitoring system also performs data mining processes based on tools such as the Apache Mahout Framework so as to identify and discover hidden patterns in the collected data, once they are processed, in the form of reviews.