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.

Research method

The design science research (DSR) method proposed by Hevner et al. [20] was followed in this study. DSR is a problem-solving paradigm and seeks to create artifacts through which information systems can be effectively and efficiently engineered. [20] These artifacts are designed to interact with a problem context to improve something in that context. [21]

The activities and the artifacts span two significant dimensions, i.e., problem-solution and theory-practice dimensions. [22] Figure 1 shows the research method used in this study. The first step was the identification of some problem instances occurring in practice and sharing similar aspects. These problem instances were analyzed, and a problem statement was formed using theoretical concepts from the literature. A conceptual solution, i.e., an artifact or artifacts, was designed by following a systematic approach. Domain analysis was used to derive and represent domain knowledge to be used for solution design. Domain analysis involved domain scoping and domain modeling activities. [23] Domain scoping refers to the identification of relevant knowledge sources to derive the key concepts of the solution. [24] To this end, several searches were conducted on the Scopus database using different search strings. Domain modeling aims at unifying and representing the domain knowledge obtained from relevant sources. The feature model was used to represent the output of domain modeling. [25] A reference architecture was designed as a conceptual solution.

Fig. 1 The research method used in this study, which involves the main steps of design science research (DSR), i.e., problem definition, solution design, and validation. The iterative nature of the research method was neglected for the sake of simplicity.

To evaluate the reference architecture, requirements were specified using recent literature on sustainable agriculture. [26,27,28,29,30] Based on these requirements, a concrete application architecture was derived using the reference architecture.

In accordance with DSR and the research method described here, the following sections describe problem definition, design of a solution, and the evaluation of the solution.

Problem definition

This study was motivated by three use cases involving different data management requirements to support sustainable agriculture. The following three use cases were used for understanding and conceptualizing the problem:

• Case 1: Satellite images (e.g., Sentinel-2 data) can be obtained from a data provider. These images can be processed to derive plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content during the growing season. [31] Afterward, the current growth status and development of cultivated crops at each location in the field can be deduced. [32] This information can be used for site-specific plant protection and fertilization measures [33], which support sustainable agriculture.

• Case 2: Harvested crop volume can be quantified and recorded in real time using numerous sensors. [34] Various parameters such as "quantity per hectare" and "flow" can be calculated, and crop productivity maps can be built. [34] Farmers can use these maps to optimize inputs such as fertilizers, pesticides, and seeding rates, resulting in an increase in yields. [35]

• Case 3: Machinery process data such as speed, angle, pressure, and flow rate can be obtained through sensors in tractors and equipment. [4] Machine, worker, field, and time slot data can be stored, and basic statistics like minimum, maximum, and standard deviation can be computed. [4] As a result, automated documentation of the production process and site-specific work can be attained. [4]

Table 1 summarizes the above-mentioned cases from a data management perspective. Similar to many cases in various domains, at a high level, digital data are produced and fed to a software system to be processed and stored. Such a system can be designated a data management platform and produce outputs that can lead to better business outcomes. As per the first case, satellite images can be processed via computer vision algorithms to drive plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content, which can in turn be used to track the current growth status of cultivated crops and support decision-making activities.

Table 1. A summary of the three cases presented above from a data management perspective Data input Data processing Data output Outcome Satellite images Derive plant parameters via computer vision algorithms Plant parameters such as Leaf Area Index (LAI), biomass, and chlorophyll content Track current growth status and development of cultivated crop at each location Harvested crops volume via sensors Build crop productivity maps Various parameters such as "quantity per hectare" and "flow" on the map Use such maps to optimize inputs such as fertilizers, pesticides, and seeding rates in order to increase yields Machinery process data via sensors Compute statistics Machine, worker, field, and time slot data, along with basic statistics such as minimum, maximum, and standard deviation Attain automated documentation of the production process and site-specific work

Solution design and artifact description

This section starts with a summary of related reference architecture studies and then presents the three steps of the solution design phase, namely domain scoping, domain modeling, and reference architecture design.

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.