Towards smart farming: Systems, frameworks and exploitation of multiple sources

Agriculture is by its nature a complicated scientific field, related to a wide range of expertise, skills, methods and processes which can be effectively supported by computerized systems. There have been many efforts towards the establishment of an automated agriculture framework, capable to control both the incoming data and the corresponding processes. The recent advances in the Information and Communication Technologies (ICT) domain can collect, process and analyze data from different sources while materializing the concept of agriculture intelligence. The thriving environment for the implementation of different agriculture systems is justified by a series of technologies that offer the prospect of improving agricultural productivity through the intensive use of data. The concept of big data in agriculture is not exclusively related to big volume, but also the variety and velocity of the collected data. Big data is a key concept for the future development of agriculture as it offers unprecedented capabilities and it enables various tools and services capable to change its current status.

The concept of agriculture includes a series of different scientific fields, where some of them are directly connected to land cultivation (water control, crop growing, harvesting, etc.), while some others are the natural expansion of the agriculture model (engineering, economics, management, etc.). Advances in different areas of the Information and Communication Technologies (ICT) domain in combination with the need for improvement of agriculture productivity, both for food security issues and environmental impact, have created the field of smart agriculture.

Precision agriculture (or smart farming) can significantly boost agriculture production both in terms of productivity and sustainability. Although productivity seems to be the driving force of every technological advance in agriculture, the importance of sustainability should not be neglected. Sustainability emerges as a major issue throughout the spectrum of human activity, thus one of the goals of smart agriculture is the minimization of the environmental impact of agriculture activities.

The field that is considered the predecessor of smart farming is precision agriculture. Although the two terms seem identical, they have differences, as the concept of smart farming goes beyond in-field management tasks and expands to a wider ecosystem considering the constant integration of new technologies (cloud computing, Internet of Things (IoT), Geographic Information System (GIS), etc.) to the existing infrastructure and the exploitation of data from multiple sources (descriptive, vector and remote sensing).

To address the challenges of constant integration of new technologies in the area of smart farming, complex systems have to be built where concepts of scalability and interoperability are their foundations. Novel approaches have to be followed in the upcoming agriculture systems to fully exploit the emerging digital technologies, able to collect, store and model huge amounts of data coming from various heterogeneous sources. This heterogeneity in data poses the greatest challenge towards the improvement of agriculture productivity through the extensive exploitation of the generated data. The challenge is the constant extraction of knowledge from raw data, thus the agriculture systems should incorporate new methods and techniques such as data mining, applied statistics and machine learning that would enable the potential of the collected data.

A prerequisite for better understating the concept of big data in agri- culture is the exploration of small data. The remarkable growth in producers ability to collect data from multiple on-site sources and their combination with data collected from Global Navigation Satellite Systems (GNSS) or national authorities have formed complex data warehouses with heterogeneous data and eventually the combination of those fragments of information (small data) have created the area of big data in agriculture. Considering the spectrum of applications of big data in various areas and domains it is difficult to define to cover every possible scenario, but in the agriculture sector, big data is mostly related to the variety of data, rather with volume or velocity.

The post is part of the paper Towards smart farming: Systems, frameworks and exploitation of multiple sources, from Anastasios Lytos, Thomas Lagkas, Panagiotis Sarigiannidis, Michalis Zervakis, George Livanos. You can download the paper:



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