Ubiquitous Computing in Precision Agriculture: A Systematic Review

The applications of ubiquitous computing have increased in recent years, especially due to the development of technologies such as mobile computing and its integration with the real world. One of the challenges in this area is the use of context sensitivity. In agriculture, this can be considered as the context related to the environment, such as the chemical and physical aspects that characterize the different soil types. This scenario periodically changes due to factors such as climate, type of cultivar and soil management technique used, among other aspects. This article presents a systematic review on the research works that explore ubiquitous computing in precision agriculture, including which technologies are being currently applied and which gap scan still be researched. Nine scientific repositories were explored to find articles about precision agriculture and ubiquitous computing. As a result of this search and filtering process, 32 works were reviewed, analyzed and categorized between the years of 2009 and 2019. In general, the reviewed articles concentrate on problems arising from the communication between sensors and the management of context-sensitive data.


Introduction
Precision agriculture is a suitable strategy to increase productivity, which al-lows the rational use of inputs and reduces the environmental impacts caused by agricultural practices. Currently, the inputs are used in a variable way to meet the specific needs of each location, thus optimizing the production process. However, it is necessary to characterize the soil spatial variability to check chemical and physical attributes through several representative sampling (Costa,  Generally, computing research has aimed to develop techniques to integrate information technology into people's daily lives, so that they are proactively assisted by technology while they execute their daily activities (Weiser, 1999). Ubiquitous computing seeks new forms of communication and interaction that are distributed in the environment, either in a perceptible or imperceptible way. Furthermore, through the use of sensors, computers can detect and extract data from the environment, which helps users to perform their tasks (Satyanarayanan, 2001).
Context-aware applications are necessary for this vision to become real-ity (Dey et al., 2001). Context means any information that allows the characterization of an entity situation that is relevant to the interaction between a user and an application, which includes information about the situation, identity and lo-cation of people, groups and physical or computational objects. Through the knowledge of contextual data, an application can adjust its own functioning or even act proactively, such as by alerting users ' to a specific scenario or aiding them to develop activities more efficiently. The generated information will enable the construction of a historical database for posterior decision making (Hong et al., 2009;Ciaramella et al., 2010).
This article uses the systematic review methodology developed by Petersen et al. (2008) to conduct a review of the use of ubiquitous computing in agriculture. Guided by the search and two-phase processing, this article concentrates on discovering the main authors in this area. This article also looks for the most relevant works, in addition to the possible research gaps and their challenges.

Materials and methods
This article uses a systematic review methodology to" identify, analyze and interpret all the available evidence related to a specific research question" (Kitchenham & Charters, 2007), which in this case are relevant to the application of ubiquitous computing imprecision agriculture. This type of methodology not only discusses the conclusion but also examines all of the activities related to the discovery. Hence, a systematic study collects data when the activity occurs and the media in which it was published, and then maps this connection (Cooper, 2016). The methodology consists in the execution of the following steps: a) establish research questions; b) design the process of the research and c) define criteria for filtering results.

Research questions
The research questions led this study to discover works related to the theme. The goal of these questions is to understand how ubiquitous technologies are being used to help in precision agriculture. It is also desirable to find how specific technologies are applied between agriculture and context awareness. Finally, clusters of commonly used terms within selected articles were generated to support the identification of academic research interest trends. Therefore, five questions were established and presented in Table 1.

RQ1
Which technologies support precision agriculture?

RQ2
Where are these technologies being applied in precision agriculture?

RQ3
How is ubiquitous computing being used to support precision agriculture?

RQ4
Which are the main clusters of research that express the terms ubiquitous computing and precision agriculture?

RQ5
What is the number of publications per database and per year?
Source: own processing  Petersen et al. (2008) defined three stages of a research process: specify the search string, choose the databases to apply them and then get the results. The first stage starts by identifying the keywords and their related terms. In this study, we chose the keywords "Ubiquitous" and "Agriculture" and also other related terms, as indicated in Table 2.  These terms generated the following search string to be used in the search databases:((ubiquitous OR context-aware OR context-sensitive OR context awareness OR pervasive OR internet-of-things OR iot) AND (agriculture OR agronomy OR soil)). The term "soil" was inserted in the search string because the term "agriculture" covers other sub-areas of application, such as aquaponic, permaculture, indoors agriculture, organic, subsistence, among others. This enabled this study to filter only the works related to intensive agriculture applied to the soil; that is, that have high productivity, large extensions of land and the use of modern techniques and mechanizations.

Research process
Once the search string was defined, we constructed the research parameters to be used on the databases. In the second step, we selected eight re-search databases relevant to the area of computing, including the ACM Digital Library, Semantic Scholar (Citeseerx), Google Scholar, IEEE Xplore Digital Library, Scopus, Science Direct, Springer and Wiley Library. Research in the ACM Digital Library required the use of advanced search features, where each of our strings was inserted in the "Edit Query" tool. Similarly, this practice was applied in the Semantic Scholar (CiteSeerX), Google Scholar, IEEE Xplore and Scopus databases.
Only in the Science Direct and Google Scholar base was the string applied in a simple search box that is available on the main page of these sites.

Filters application
To filter the most relevant works, we generated the following Inclusion Criteria (IC) for this selection: [5] Ubiquitous Computing in Precision Agriculture: A Systematic Review -IC1: The study must be published in a conference proceeding or journal.
-IC2: The study must be related to the context of use of ubiquitous com-putting in agriculture.
-IC3: The study must be a full paper.
In turn, the Excluding Criteria (EC) were also defined, as follows: -EC1: Studies published before 2009.
-EC2: Studies that are not written in English.
-EC3: Studies related to theses or dissertations.
-EC4: Studies that are not related to research questions.
The inclusion and exclusion criteria helped the filtering process to obtain the most relevant studies and to eliminate any noise generated during the search. The studies obtained in the search process were filtered, and those that did not fit in the inclusion criteria were removed. The articles were stored in the Mendeley Desktop© program and organized in specific folders to each database. The next step was to analyze the works by title and abstract, so that they could be later combined in the same folder. Six articles were added by heuristic because they are relevant for this study, even though they were not found during the search process.
The next filter was based on the first two passes of the three-pass approach introduced by Srinivasan Keshav (Keshav, 2007). The first pass is a quick sweep, consisting of: 1) reading the title, the abstract and the introduction; 2) reading only the heading of the section and subsection, but ignoring all the rest; 3) looking the mathematical contents (if there is any) to determine the underlaying theoretical fundamentals; and 4) reading the conclusions. The second pass consisted of carefully analyzing the figures, diagrams and any other illustrations in the article, giving special attention to the graphics. Finally, the remaining articles were filtered by the analysis of the full text and the observation of the exclusion criteria EC4. Figure 1 presents the filtering process, with IC and EC applied at each stage, with the Scopus and Science Direct databases bringing together more assertive works related to the search string with 46.4% of the filtered results. The Semantic Scholar and Wiley databases presented many unrelated works. Figure 2 shows the result of this processing before the article combination stage. The filtering process also brought works related to subareas of computation, such as hardware architecture and networks applied to precision agriculture.
Source: Own processing [6] Ubiquitous Computing in Precision Agriculture: A Systematic Review

Threats to validity
As in any work involving a systematic review, this research presents some risks that can affect the obtained results. These risks are directly related to the data filtering process. To reduce the risk margin, this research searched data in nine different databases, selected from their relevance in the areas of ubiquitous computing and agriculture.
The research string was constructed considering the main terms and some related words to ensure the greatest possible coverage of keywords in the search, avoiding an ineffective search.
Source: Own processing

Results and discussion
This section details the survey results obtained by reading and analyzing the 32 mapped studies.
In addition, the research questions were answered and additional discussions and analysis on the studies were presented.

RQ1 -Which technologies support precision agriculture?
The technologies that support precision agriculture were analyzed and categorized as IoT & Sensors Applications, Architecture Model, Semantic & Ontology and, finally, Wireless Network Sensor (WSN). Table 3 maps the works with their categorization.
Based on this question, most of articles focused on WSN. There is a big difference between IoT and WSN, which causes these terms to have been categorized separately. In an IoT system, all of the sensors directly send their information to the Internet, such as soil temperature and moisture. In this case, a direct connection to the Internet will be open immediately or periodically to synchronize data. Already in a WSN, the various sensors connect to some kind of router or central node. A large collection of sensors, as in a mesh network, can be used to individually gather data and send data through a router to the Internet in an IoT system. In other words, WSN is a subset of IoT.
Source: Own processing  37.50% [7] Ubiquitous Computing in Precision Agriculture: A Systematic Review

RQ2 -Where are these technologies being applied in precision agriculture?
Analyzing the results according to Table 4, most technology applied in precision agriculture was about improvements of communication between sensors, in the same direction that RQ1 appointed. network communication followed by the soil analysis and context sensitive applications. In this last case, the control centers collect and process data in real time to help the farmers to make the best decisions related to planting, fertilizing and harvesting.
Source: Own processing

RQ3 -How is ubiquitous computing being used to support precision agriculture?
According with Table 4, there is currently a big gap (56.25% of the articles) in improving communication between the distribution of the sensors and the base. Technology such as WSN has boosted agricultural research because there is no longer a need for cables to receive the information but instead solutions are available to avoid the loss of data, to enable synchronization and to improve sensor power efficiency.
A total of 14 articles were identified that used the contexts as defined by Dey et al. (2001). The main goal of these researchers was to improve the use of technology in favor of increased production in the planted area. The major challenge of ubiquitous computing is related to the need of the applications be context-sensitive so that, when appropriate, they respond through decision making. All of these studies of context awareness that were selected in the mapping are presented in Table 5. Figure 3 presents the result of a bibliometric mapping tool known as VOSViewer, which was used to identify relevant works by publication year (Van Eck & Waltman, 2009). It is also possible to verify the common terms within publications and interest relating to ubiquitous computing and precision agriculture. All of the selected studies focus on four terms, in order of relevance: sensor network, context, sensor node and soil. The WSN is one of the most promising technologies for the agricultural sector. WSN enables advancements in ubiquitous computing due to their availability, small size and low price, resulting in an easy and cost-effective implementation. Figure 4 presents the publications grouped according to the year of publication. However, it should be noted that the year of creation of this article (2019) is still on going and other articles could compose this statistic. In the last six years, the number of publications on agriculture and ubiquitous computing increased when compared with the rest of the beginning period, except 2015. This growth shows the interest of researchers in improving agriculture, as well as in improving the quality of products through the better monitoring of production. The number on the top of peaks indicates the total of publications, excluding duplicates.

Discussion
Only 10 studies or 31.25% of the articles used IoT and Sensors to support precision agriculture (Table 3). Among these, the most popular technology was WSN with 12 articles. [8]

Ubiquitous Computing in Precision Agriculture: A Systematic Review
Source: Own processing This work proposed and illustrated the implementation of a new method for delineation of management areas using satellite images to understand spatial variation within a field and to optimize the use of agricultural inputs (seeds, agrochemicals and soil amendments). (Phillips, Newlands et al., 2014) This work discussed a series of key recommendations for monitoring the soil moisture dynamics at a field scale to integrate with remote sensing and decision support models. It also concluded that for integrated sensing to be utilized for long-term operational monitoring and decision support web services, soft-ware tools and analysis tools need to be developed. Thus, data from multiple sources could be analyzed and integrated with various models for crop production, food security and environ-mental change. This work recommends that disease treatments for farmers plants must be considered from a set of related observations. Thus, it developed a theoretical framework for systems to man-age a farmer's observation data. It introduced the representation of observation data, called warn cons, based on the user's context information aiming to create a representation of the advice data  By analyzing the entire filtering process presented in Figure 1 and the Research Question 1 (RQ 1), which presents in which databases the articles were published, it is possible to verify that the most accurate databases that processed the query string were Google Scholar and Science Direct because they had a small number of articles in the initial survey but have a relatively high number when compared to the total articles used in the mapping. The least accurate database was Semantic Scholarof the 81 papers in the initial search, only one study was used after the last filtering.

Conclusion
This systematic review has presented the state-of-the-art in the application of ubiquitous computing in precision agriculture. Furthermore, it also presented different applications Source: Own processing [10] Ubiquitous Computing in Precision Agriculture: A Systematic Review of technologies associated with computing for better results in agricultural production.
Although many of the selected papers aim to solve WSN problem, one of the gaps that was found during the evaluation of this research corresponds to the application of historical data in precision agriculture. In particular, none of the articles mentioned the use of a historical database and how this collected data could effectively improve production with the support of mobile technology.
Contextual data could lead to a three-dimensional spatial variability of soil conditions, such as fertility, moisture, pH, macro and micronutrients, and other soil attributes. This type of visualization was approached by Stojanovic using yield data (Stojanovic et al., 2017).
The history of contextual data could support decision-making on the farm. In this sense, the formalization of a context (Dey et al., 2001) applied to precision agriculture would allow the generation of context histories (Rosa et al., 2015) related to a whole plantation or areas of it. This data could be analyzed to generate context pre-dictions (da Rosa et al., 2016) of soil conditions. Therefore, the right moment for agricultural inputs distribution, such as pesticides and fertilizers, could be determined, in addition to uniformity in productivity.