# Towards Climate Smart Farming--A Reference Architecture for Integrated Farming Systems
## 2 Related Work
### Data Aggregation Systems  
Improving mixed farm productivity is essential for increasing farm profitability and meeting the rapidly growing demand for food that is fueled by rapid population growth across the world and decreasing demand for energy. Mixed-farm productivity can be increased by understanding and forecasting crop and livestock performance in a variety of environmental, social and ecological conditions. Both crop and livestock recommendations are currently based on data collected in field-based agricultural studies that capture performance under a variety of conditions (e.g., soil quality, diseases, environmental conditions, etc.). The quality of manually collected performance data is very low, because it does not take into account earlier conditions that have not been observed by the human operators, but it is essential to filter out collected data that will lead to invalid conclusions. Furthermore, due to MFS, we must increase the volume of data, the number of the sources and the energy demands for efficient communication, consequently increasing the complexity of the data gathering process exponentially. To this end, the utilization of emerging technologies such as IoT devices, ground sensors, livestock sensors (collars/ear-tags), multispectral cameras and UAVs for data gathering purposes, is quite mature nowadays, and they can be used to collate vast amounts of environmental and crop-livestock performance data, ranging from time series data from sensors, to spatial data from cameras, to human observations collected and recorded via smart applications [45]. More specifically, their benefits are extended in the whole spectrum of the agricultural ecosystem. Their deployment decreases the farmer's labor tasks, facilitating his daily routine and making resource allocation more efficient. Moreover, the quality and quantity of data are improving and being enriched respectively, providing insights that enhance productivity while sustainable and eco-friendly processes are identified and fostered. Precision agriculture and data analytics are reshaping industry and market boundaries and interactions, with precision agriculture technologies (data holding, data specialization, data strategy) being a combination of: Global Positioning System (GPS), sensors mounted on farm machines or standalone, geo-mapping, automatics steering systems, electronic communications and variable rate technologies [46]. Increasing UAVs' obligations results in a higher energy consumption, a fact that could disrupt the sustainability and eventually the viability of the system [47]. Intelligent devices are deployed to collect specialized data and provide valuable information. Such devices are photonics which are utilized for monitoring and predicting the ripening process and quality of produce, ear-tags that gather information about animals' nutrition and multi-spectral cameras that collect images in different spectrums. The incorporation of such devices intensifies data diversity, raising concerns about interoperability between the physical components, data processing and information integration capabilities [48]. Obviously, these new trends on the one hand manage to handle some of the MFS concerns, but on the other hand reveal new challenges that need to be addressed.