Design of a Data Management Reference Architecture for Sustainable Agriculture

Görkem Giray,Cagatay Catal

doi:10.3390/su13137309

Abstract

Effective and efficient data management is crucial for smart farming and precision agriculture. To realize operational efficiency, full automation, and high productivity in agricultural systems, different kinds of data are collected from operational systems using different sensors, stored in different systems, and processed using advanced techniques, such as machine learning and deep learning. Due to the complexity of data management operations, a data management reference architecture is required. While there are different initiatives to design data management reference architectures, a data management reference architecture for sustainable agriculture is missing. In this study, we follow domain scoping, domain modeling, and reference architecture design stages to design the reference architecture for sustainable agriculture. Four case studies were performed to demonstrate the applicability of the reference architecture. This study shows that the proposed data management reference architecture is practical and effective for sustainable agriculture.

Highlights

The increasing food demand and its large ecological footprint call for action in agricultural production [1]
To the best of our knowledge, this is the first study that focuses on sustainability within the context of data management reference architecture
The design science research (DSR) method was applied while designing the reference architecture

Summary

Introduction

The increasing food demand and its large ecological footprint call for action in agricultural production [1]. Inputs and assets should be optimized; long-term ecological impacts should be assessed for sustainable agriculture. Decision-making processes on optimization and assessment need data on several inputs, outputs, and external factors. To this end, various systems have been developed for data acquisition and management to enable precision agriculture [1]. Precision agriculture refers to the application of technologies and principles for improving crop performance and environmental sustainability [2]. Smart farming extends precision agriculture and enhances decision-making capabilities by using recent technologies for smart sensing, monitoring, analysis, planning, and control [1]. Real time sensors are utilized to collect various data and real time actuators are used to fine-tune production parameters instantly

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Discussion

Conclusion