# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
## 8 Use Case Study: The DIAS Architecture
### DIAS Architecture
UAVs' data transmission and communication is enabled by the IEEE 802.11 ah networking standard in cooperation with the IPv6 protocol, to forward the collected images to the network server as well. In the Middleware layer, the DIAS platform will employ the MQTT-SN protocol. MQTT-SN enables the management of all DIAS networking devices through message exchanges coordinated by the FIWARE's context broker. It is essential to maintain the monitoring's system reliability towards achieving meaningful data collection. Based on this protocol, the exchanged messages can be organized by topics and importance regarding device management commands, transmission requests initiated by a higher layer and data acquisition. Sixteen number of message types are supported and transmitted in an asynchronous way of communication by the publish-subscribe model. TCP and UDP will also be used as transport protocols. MQTT is ideal for the DIAS system since it excels in smart sensor device communication on a wide area network, due to the publish/subscribe architecture with the broker in the middle. MQTT is also very useful if the bandwidth is limited to enhance network reliability and availability. In the according Configuration layer, all saffron field collected data will be grouped and formulated to reach the information management unit. This layer facilitates the FIWARE NGSI API so to produce the desired information model of the acquired field data based on efficient context management. The FIWARE's Orion Context Broker has powerful features enhancing the capabilities and performance of the DIAS monitoring system such as enabling asynchronous application notifications. The basic operations of the current layer include enabling the publication,consumption, subscription and processing of all the information relevant to the crops and the cultivation process in the DIAS platform. The Management layer constitutes the basic operation center of the DIAs platform. It stores, maintains, analyzes the collected data and produces predictions for the progress of the crops growth rate and statistics regarding they current state. According to the commands acquired from the user of the platform in the highest architectural layer, the information management logic initiates the according processes. Data processing includes the use of the Random Forest machine learning classification algorithm for direct extraction of conclusions regarding the growth and health of vegetation. This algorithm is directly applied on the data of the images acquired by the UAVs, evaluating the RGB  
Figure 4: The DIAS architecture. colors and the intensity of each pixel. The Random Forest scheme performs very well with high dimensionality and can handle binary features, categorical features, and numerical features. This method is chosen since very little pre-processing is required and the data does not need to be rescaled or transformed. Digital image processing of geospatially corrected aerial images (orthophotographs) focuses on producing the according vegetation indexes for evaluating the state of saffron plants. Two kind of vegetation indexes will be calculated by the PiX4D image processing tool. Vegetation indexes derived from multispectral information, such as NDVI will enable the detection of unhealthy or sparse vegetation that reflects more visible light and less near infrared light, making it easy to monitor the growth and health of saffron crops. On the other hand, regarding the use of RGB-based Vegetation Indices, Excess Greenness Index (ExG) is based on the assumption that saffron plants will display a clear high degree of greenness, while soil being the only background element. Finally, the Normalized Difference Index (NDI) will be also used since it can enable the separation of saffron plants from soil and residue background images, using only green and red channels. Spectral information can help significantly in assessing a lot biological and physical characteristics of the saffron crops.