Journal:Design of a data management reference architecture for sustainable agriculture

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.

Keywords: sustainability, agriculture, sustainable agriculture, data management, reference architecture, design science research

Introduction

The increase in food demand and its associated large ecological footprint call for action in agricultural production. [1] Inputs and assets should be optimized, and 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] Data to be acquired are enhanced by context, situation, and location awareness. [1] Real-time sensors are utilized to collect various data, and real-time actuators are used to fine-tune production parameters instantly.

In the late 2000s, Murakami et al. [3] and Steinberger et al. [4] pointed out a need for data storage and a processing platform for agricultural production. They utilized web services to send and receive data from a central web application. That web application received, stored, and processed data, and it provided the required outputs to its users or any other system. Similarly, Sørensen et al. [5] listed several data processing use cases to assist farmers’ decision-making processes. More recently, technologies such as the internet of things (IoT) make digital data acquisition, and hence smart farming, possible. [6] In recent years, many studies have been performed in the fields of smart farming and precision agriculture. [7,8,9,10,11,12,13] At the heart of many of those studies is Industry 4.0, which acts as a transformative force on smart farming processes. Industry 4.0-related technologies—namely IoT, big data, edge computing, 3D printing, augmented reality, collaborative robotics, data science, cloud computing, cyber-physical systems, digital twins, cybersecurity, and real-time optimization—are increasingly integrated into different parts of modern agricultural systems. [14]

To realize operational efficiency, full automation, and high productivity in these systems, different types of data are collected from operational systems using different sensors, stored in big data systems, and processed using machine learning and deep learning approaches. Traditional data management techniques and systems are not sufficient to deal with this scale of data, and as such, big data infrastructures and systems have been designed and implemented. To manage the complexity of this big data, many different aspects of data must be considered during the design of these systems. Different data management reference architectures have been designed to date. [15,16,17] To the best of our knowledge, none of these studies have focused on sustainable agriculture. There exist several practices for sustainable agriculture that can protect the environment, improve soil fertility, and increase natural resources. It is known that agriculture can affect soil erosion, water quality, human health, and pollination services. [18] As such, sustainable agriculture is crucial to minimize the negative effects of agricultural production. Sustainable agriculture requires an iterative process because each actor in the system has a different responsibility, and the success of this process is highly dependent on the success of each actor.

The goal of this study is to present a data management reference architecture for supporting smart farming, sustainable agriculture, and other domains. The study builds on the recent developments in data management and processing, i.e., big data, machine learning, and data lakes. We designed a data management reference architecture for sustainable agriculture and evaluated it using several case studies. Domain scoping, domain modeling, and reference architecture design stages were followed to create the reference architecture. Based on the reference architecture, we can design different application architectures. During the validation stage of this study, using different case studies obtained from the literature, we have shown the applicability of our reference architecture as a novel data management reference architecture for sustainable agriculture.

The structure of this paper follows the outline proposed by Gregor & Hevner [19] for design science research. The next section summarizes the research method adopted in this study, followed by the definition and structuring of the problem by analyzing the existing literature. We then present the related reference architecture studies and explain the solution design process and the reference architecture obtained. That is followed by the evaluation of the reference architecture by deriving application architectures from it based on some requirements from the sustainable agriculture domain. The penultimate section discusses the results, and the final section provides conclusions and plans of future work.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, grammar, and punctuation. In some cases important information was missing from the references, and that information was added.