# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 3 MiFarm-CSA Architecture
### Integrated Data Collection Network
Furthermore, based on technological advances such as edge computing, the ASSM can incorporate on-board, real-time video analyzers which enable the autonomous and dynamic flight control of UAV according to the results of video surveillance output. As a result, the time needed for scanning an area will be highly reduced, allowing MiFarm-CSA to expand the scalability of the monitored landscape. WCCF can further extend the information provided to the MFS by including satellite, meteorological and climate-related information. Satellite-derived measurements are essential inputs to monitor water management and agricultural production for improving regional food security [84]. Observations coming from near real-time satellites can be used to mitigate the adverse impacts of extreme events and promote climate resilience. MiFarm-CSA architecture can benefit from energy harvesting techniques in the operation of SST to reduce the environmental impact of farming. The optimal \"energy management\" policy can be defined based on existing machine learning (ML) models and AI algorithms [85]. Considering data storage and transfer, middleware software and the necessary hardware gateways and actuators are needed for storing and forwarding the captured data. The SMG component provides the necessary middleware software and gateway infrastructure to support IDCON by gathering all kinds of information from different sources. The middleware should able to abstract the heterogeneity and complexity of the underlying hardware devices, appliances or unmanned vehicles (data transmission formats, manufacturer particular features, operating systems, etc.) and to provide inter-operability among sensors, robots and smart devices. Reliability in communications, real-time data distribution, automatic discovery of entities and quality of service (QoS) monitoring is guaranteed by the middleware itself. The CEDA component provides the data collection and processing system that is carried through into distributed computing systems and clusters for real-time processing. To this end, robust data analytics, high performance computing, efficient data network management and cloud computing (CC) techniques are used [86]. More specifically, the main pillars are (i) the integration of data that originate from different sources via a common communication point, and (ii) the provision of suitable adaptive algorithms for effective, dynamic and autonomous decision-making. Taking into account the massive and heterogeneous characteristics of data, acquiring and processing all of it is inefficient in terms of communication cost, computing complexity and the utilization of data storage resources. For this reason, dimensionality reduction should provide a reduced version (sketch) of original data taking into account the specific characteristics of the data generated. The Panoptis platform consists of hardware add-ons (collars and ear-tags) and software modules that provide tools to livestock farmers so as to locate and monitor livestock. The platform allows one to generate many types of enclosures, sub-areas and points of interest. This allows one to have records and monitoring of animal behavior in these enclosures. The challenge in this component lies in the integration of information when received from different sources: (a) IoT devices mounted on animals; (b) satellite information about pastures; (c) weather forecast; (d) soil sensors, in a DSS to improve grazing management. The interconnection between the data collecting and the data storing can be achieved through low power wide area networks (LPWAN) such as LoRA and Sigfox telecommunication networks. LoRa and Sigfox protocols are widely used for supporting smart applications and monitoring systems [87].