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Systematic Review

Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020

by
Vasileios P. Georgopoulos
,
Dimitris C. Gkikas
and
John A. Theodorou
*
Department of Fisheries and Aquaculture, School of Agricultural Sciences, University of Patras, 30200 Messolonghi, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2023, 15(23), 16385; https://doi.org/10.3390/su152316385
Submission received: 11 October 2023 / Revised: 19 November 2023 / Accepted: 27 November 2023 / Published: 28 November 2023
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Abstract

:
Food production faces significant challenges, mainly due to the increase in the Earth’s population, combined with climate change. This will create extreme pressure on food industries, which will have to respond to the demand while protecting the environment and ensuring high food quality. It is, therefore, imperative to adopt innovative technologies, such as Artificial Intelligence, in order to aid in this cause. To do this, we first need to understand the adoption process that enables the deployment of those technologies. Therefore, this research attempts to identify the factors that encourage and discourage the adoption of Artificial Intelligence technologies by professionals working in the fields of agriculture, livestock farming and aquaculture, by examining the available literature on the subject. This is a systematic literature review that follows the PRISMA 2020 guidelines. The research was conducted on 38 articles selected from a pool of 225 relevant articles, and led to the identification of 20 factors that encourage and 21 factors that discourage the adoption of Artificial Intelligence. The factors that appeared most were of economic nature regarding discouragement (31.5%) and product-related regarding encouragement (28.1%). This research does not aim to quantify the importance of each factor—since more original research becoming available is needed for that—but mainly to construct a list of factors, using spreadsheets, which could then be used to guide further future research towards understanding the adoption mechanism.

1. Introduction

Every year the world’s nutritional requirements increase, mainly due to the increase in the Earth’s population. The population of the planet today is about 8 billion, with an annual increase of about 0.88%, and in 2050 it is estimated to reach about 10 billion [1]. This increase, combined with climate change, will create extreme pressures on food industries, which will have to respond to this need while protecting the environment and ensuring high food quality [2].
This is additionally supported by eleven of the seventeen goals for sustainable development set by the United Nations in 2015, which are directly or indirectly linked to food systems, with the most prominent goal being zero hunger by 2030 [3].
The journey of agriculture and livestock farming begins almost from the beginning of human civilization when agricultural practices were based on the power of animals. It continued with the industrial revolution, where animal power was replaced by combustion engines [4], accompanied by synthetic fertilizers and pesticides. This was followed by the integration of various modern technologies such as sensors, geolocation systems, computer programs, etc., which aimed to calculate ideal quantities, ideal time frames and ideal locations regarding the application of agricultural and livestock products and practices, and continues today with the integration of state-of-the-art technologies, such as the Internet of Things, Big Data, Cloud Computing, Artificial Intelligence and so on [5], with the ultimate goal being to increase productivity in a sustainable way. And although there is currently a balance between demand and supply, the increased needs that will arise in the near future, combined with the finite available resources, pose a great challenge for the future [6].
When it comes to the field of aquaculture, this discipline deals with the breeding and production of marine organisms, both animals and plants. Aquaculture production is almost always either fully or partially controlled. The contribution to fish production reached an impressive 49% in 2020 in terms of total fish volume [7]. Problems within the field include water pollution, reproduction quality (broodstock and fingerlings), disease outbreaks and inefficient management [8,9].
In order to be able to increase food production to meet future demand, while maintaining—or even potentially needing to increase—food quality, many difficulties and challenges must be addressed. These include—but are not limited to—more efficient food yield planning, more efficient prognosis, diagnosis and treatment of ailments in crops, fish, livestock etc., more efficient management and so on. Innovative technologies can assist greatly in this cause. One of these technologies is Artificial Intelligence (A.I.). A.I. is a branch of computer science that aims to empower computers with the ability to learn and make assessments based on the past, imitating living organisms. The term “Artificial Intelligence” can be used both to refer to a specific branch of technology in this field, or more broadly, to refer to all the various technologies in this field, such as Machine Learning, Deep Learning, etc., all of which, in many cases, exhibit a dramatically higher accuracy compared to conventional methods [10]. For the purpose of this paper, the term “A.I.” will be used in the latter manner, to mean the entirety of technologies within this field. Regarding the food production industries, A.I. is growing rapidly in agriculture and livestock farming, but not enough in the aquaculture field [10,11].
In order to efficiently deploy the aforementioned technologies, we first need to understand the factors that affect adoption. Therefore, this study, by examining the available literature, aims to provide insight about the factors that encourage and discourage the adoption of A.I. by professionals working in the fields of agriculture, livestock farming and aquaculture. This information is critical and unfortunately lacking in the available literature. The research does not aim to quantify the importance of each factor—since more original research is needed for that—but to construct a list of factors that could be used to guide further future research.

Research Questions

Based on the above framework, the purpose of this research is to attempt to answer the following two research questions:
  • RQ1: Which factors appear to encourage the adoption of A.I. technologies by professionals in the fields of agriculture, livestock farming and aquaculture?
  • RQ2: Which factors appear to discourage the adoption of A.I. technologies by professionals in the fields of agriculture, livestock farming and aquaculture?

2. Materials and Methods

In order to seek answers to the aforementioned research questions, the systematic literature review method was used. To conduct the survey, the PRISMA 2020 guidelines were followed (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) [12] in order to ensure transparency, repeatability and scientific rigor. Thus, specific protocols were created for the search strategy, the selection criteria, the data extraction and, finally, the data analysis.

2.1. Information Sources and Search Strategy

The systematic literature review consists of articles drawn from two online databases, Scopus and the Web of Science (WoS) Core Collection. The selection of these databases was made due to their international recognition and their extensive and remarkable collection of articles concerning many different scientific subjects and fields.
The search was performed using the “advanced search” feature of these databases, using keywords that are directly relevant to the context in which we are searching for answers and using logical operators (AND, OR) to generate appropriate search strings. No specific date frame was set for the age of the articles. After experimentation and study of the search syntax rules, the logical search strings presented in Table 1 were created and used (the asterisk symbol, *, denotes a wildcard character/word).
In order to ensure the reliability and validity of the research, only peer-reviewed papers were considered, either original or secondary research (literature reviews, systematic reviews and meta-analyses). Furthermore, only papers written in English were considered, while no distinction was made between the different journals (i.e., they were all taken into account). The article search was performed in June of 2023.

2.2. Inclusion and Exclusion Criteria

In order to select and include only helpful papers for our research questions, the following screening criteria were set and applied.

2.2.1. Inclusion Criteria

IC1: The article must be written in English.
IC2: The article must be a journal article.
IC3: The article must be peer-reviewed.
IC4: The article must not be a duplicate (exist in another database).
IC5: The article must be the product of original or secondary research.
IC6: The article’s subject must lie within the context we are interested in, namely agriculture, livestock farming and aquaculture.
IC7: The article’s subject must be related to A.I., either autonomously or as a key component of a digital technology package (e.g., technologies regarding digital agriculture).

2.2.2. Exclusion Criteria

EC1: The article’s subject is not directly related to the context we are interested in (e.g., it studies a more general area, like “Industry 4.0”).
EC2: The article’s subject is not directly related to A.I (e.g., it studies digital technologies in a more general context, like “smart city” digital technology).
EC3: The article does not contain any information regarding the adoption of A.I.

2.2.3. Data Collection and Analysis

The systematic literature review was conducted in four stages, following the PRISMA 2020 guidelines [12]. The first stage consisted of the initial search of the literature included in the WoS (n = 180) and Scopus (n = 154) electronic databases. Based on the inclusion (IC1, IC2, IC3) criteria, a total of 11 papers (filtered by the search engine) were deemed ineligible due to type of paper, language and peer-review criteria. Regarding duplicates (IC4), 98 papers were excluded, using a Python script. In the second stage, the inclusion (IC5, IC6, IC7) criteria were applied by reviewing the title, the abstract and by browsing the entire paper, resulting in the exclusion of 166 papers. In the third stage, a total of 59 were carefully screened for eligibility based on the exclusion (EC1, EC2, EC3) criteria, resulting in the exclusion of 21 papers. In the fourth and last stage, the remaining 38 articles were reviewed thoroughly to confirm their relevance to our criteria and research questions. The above process is illustrated in a PRISMA flow diagram (Figure 1), while the corresponding table listing all included papers in the systematic literature review (Works, from W1 to W38), can be found in Appendix A (Table A1).
This research does not attempt to quantify the importance of each identified factor so it does not deploy any quantification methods. Rather, it attempts to detect and construct a list of factors affecting adoption. All the eligible articles were studied thoroughly and all references regarding A.I. adoption were extracted, collected and placed in spreadsheets. Those references were then analyzed in order to detect factors that potentially affect adoption. After all the potential factors were detected, they were placed in groups based on relevance in an attempt to distinguish main trends, while using frequency tables to present emergence frequency.

3. Results

A total of 38 articles met all of the criteria set. All articles were written from 2017 onwards, with the majority being published in 2022, as shown in Figure 2.
Of the 38 articles, the majority were literature reviews (55%), followed by original research articles (32%), systematic literature reviews (11%) and one special report (3%) (Table 2).

3.1. Encouraging Factors

The study of the 38 articles revealed 20 factors encouraging the adoption of A.I. technologies in agriculture, livestock farming and aquaculture, which were grouped into seven categories: 1. Security, 2. Economics, 3. Products, 4. Information, 5. Demographics, 6. Approach, 7. Utility. The “products” group was the most important (28.1%), followed by the groups: “demographics” (18.0%), “economics” (16.9%), “information” (16.9%), “utility” (11.2%), “approach” (5.6%) and finally “security” (3.4%) (Table 3).
The most important factors encouraging the adoption decision were whether it is considered profitable (in a cost-benefit analysis sense) (n = 9), whether proper information is available so that stakeholders can learn how these technologies can benefit them in their work (n = 9), and the age of stakeholders (younger ages were more likely to adopt new technologies) (n = 9), followed by whether these technologies work reliably and produce the results promised (n = 8) and whether training and education are available concerning the use of said technologies (n = 7). In addition, the high level of education and the existence of relevant technological knowledge enhanced the likelihood of adoption (n = 7). All factors are presented in detail in Table 4.

3.2. Discouraging Factors

The study of the 38 articles revealed 21 factors discouraging the adoption of A.I. technologies in agriculture, livestock farming and aquaculture, which were grouped into eight categories: 1. Economics, 2. Knowledge, 3. Information, 4. Security, 5. Infrastructure, 6. Interoperability, 7. Products and 8. Approach. The category “economics” was the most important (31.5%), followed by the groups: “knowledge” (21.8%), “information” (13.7%), “security” (12.9%), “infrastructure” (9.7%), “interoperability” (5.6%), “products” (2.4%) and finally “approach” (2.4%) (Table 5).
The most important factor discouraging the adoption decision was the general cost of the investment (which entails initial investment cost, maintenance cost, upgrade cost, training cost, etc.) (n = 22), followed by the knowledge and skills required to use said technologies (n = 18), the risk adoption (when the profit appears to be such that the transition is not worth the investment/the profit of the transition is not evident) (n = 14), the protection of data and infrastructure (from sabotage, leakage of information, dubious use of data by providers, etc.) (n = 13) and the complexity of the technology/information overload that go hand in hand with said technologies (n = 9). All factors are presented in detail in Table 6.

4. Discussion

4.1. Study of Encouraging Factors

Product: The category “products” was the most important group of encouraging factors. Availability of training programs regarding the use of said technologies [13,14], providing user-friendly technology [15] and involving the end user in the development process through suggestions, opinions, remarks, concerns and so on [16,17] were the most mentioned factors encouraging adoption. Offering the opportunity to test the technology before making the decision to invest [18] as well as providing the possibility to customize these technologies to the needs and requirements of each user were also encouraging factors [19].
Demographic: Many studies [20,21,22,23] have repeatedly shown a positive relationship between education and the likelihood of adopting new technologies, where the higher the level of education the more likely it is for the stakeholder to adopt new technologies. For example, research by Alvarez and Peter [24] showed that skills acquired through education enables farmers to integrate computers more easily into their professional activities. The same is true for age, with younger ages being more positive about adopting new technologies [25] and older ages more likely to resist them [26].
Economics: The most important economic factor encouraging adoption is whether the investment is considered profitable or not, meaning whether the benefit of adopting A.I. technologies, in relation to the total cost of the investment, is deemed advantageous compared to current conventional methods [27]. In any case, the existence of high purchasing power enhances the likelihood of adoption [28]. The ability to offer and provide cost-effective solutions—such as mobile phone applications—helps familiarize the public with A.I. technologies while also highlighting their usefulness, factors that are very important for their further adoption [13]. Providing easier access to the technology, for example through leasing mechanisms, reduces the financial burden and enhances the likelihood of adoption [29].
Information: The availability of organized information about how these technologies will benefit the end user is an important factor in encouraging adoption [15]. Equally important is the familiarization of stakeholders with the various available technologies by offering information regarding their capabilities, how they work, where to buy them, which products are available and so on [30].
Utility: Many studies have shown that the expectations of performance of a new technology always serve a very important role in its adoption [31]; that is, people believing that the new technology will help them do their work and achieve their goals more efficiently [32]. The perception that a product is useful significantly enhances the likelihood of adoption [33]. The most important factor, however, is whether these technologies are reliable, operate as intended and deliver the results promised [34].
Approach: The adoption and usage of new technologies by professional colleagues greatly enhances the possibility for a stakeholder to adopt the new technologies as well. For example, farmers who witnessed the operation and end results of new technologies on their colleague’s farms were much more likely to adopt these technologies themselves [29]. The research of Blasch et al. [35] showed that social ties between colleagues are usually so strong that when a member of the group adopts them, it increases the rate of adoption for the rest of the group. As a result, new technologies are shown to be adopted gradually. First, by the most innovative members of the group, and afterwards by the rest due to peer-influence.
Security: Stakeholders place great value on the security associated with these technologies in their adoption decision process. The existence of mechanisms that guarantee that these technologies are protected against the possibility of sabotage or information leakage—which would result in financial loss—greatly enhances adoption rates [14]. The same applies when stakeholders are provided with guarantees and transparency regarding the way providers of A.I. technologies use the data collected during the operation of these technologies [13]. At the same time, a very important encouraging factor is the assurance that stakeholders will maintain control over the operation of these technologies, as well as of the decision-making process, regarding work matters, without the fear of being replaced or undermined by them [36].

4.2. Study of Discouraging Factors

Economics: The biggest obstacles in the adoption of A.I. technologies appear to be economical. The total cost of the transition consists of the initial cost of investment and the cost of maintenance—which are the key costs that discourage adoption [37,38]—followed by other costs as well, such as the cost of training to use said technologies, the cost of updates/upgrades—which are often not free [13]—and so on, which are included in the cost-benefit assessment of stakeholders [39]. The latter reason makes many farmers reluctant to adopt systems that require frequent updates [13]. The above is combined with the inherent tendency of all investors to be cautious in their investments and to avoid reckless risks and uncertainties [40]. At the same time, the absence of clear evidence that these technologies will bring economic benefit further complicates their adoption [41]. Many stakeholders, for example, believe that the economic benefit is marginal and therefore not worth the risk of transition [42]. Finally, the necessary time needed to transition from conventional methods to A.I. technologies also pose a hindering role [43].
Knowledge: The second most important factor is the knowledge necessary for the adoption of A.I. technologies, which is not limited exclusively to technical knowledge. Setting up a smart farm requires knowledge regarding which hardware and software is best suited for the requirements of the farm, knowledge regarding the installation of these technologies (in case no installation service is offered by the supplier), knowledge regarding the usage of these technologies and so on. [44]. Lack of knowledge regarding usage, combined with the complexity of new technologies, keeps many stakeholders away from embracing them, even when these technologies are already present, installed and operational. Due to this, many stakeholders insist on using traditional ways to calculate and estimate various factors, such as through conventional spreadsheets [45]. For example, the research of Wathes et al. [46] showed that, although many farmers had adopted technologies to monitor their farms through the use of sensors, the complexity of this technology combined with the lack of appropriate knowledge on how to operate them led to a low-efficient use. In addition, the need for extensive training creates discomfort for stakeholders [47], which is further exacerbated when these technologies are accompanied by an overload of information, which creates an additional intellectual burden on stakeholders [48].
Information: One of the prerequisites for adoption—which is a major deterrent in its absence—is confidence in the reliability and effectiveness of A.I. technologies [49]. It has been observed that, usually, the most reliable results are offered by ecosystems of technologies and services that are designed to work well together [50]. If there is uncertainty about the effectiveness of these technologies, stakeholders interpret it as a risk, which is then taken into account in the cost–benefit assessment of the investment [51] as a discouraging factor. At the same time, a lack of information regarding the available technologies, the possibilities these technologies offer and how one can procure them further confuses stakeholders [29] and constitutes an obstacle to public familiarity with these new technologies, which ultimately hinders their acceptance and adoption [52].
Security: There are fewer personal data involved in the fields of agriculture, livestock farming and aquaculture than other fields, such as health; however, concerns about their possible leakage is a major discouraging factor [53]. In addition, stakeholders do not want to surrender control of their decision-making to these technologies. Rather, their desire is that these technologies act as an assistant, helping them make better decisions, rather than replacing them [54]. Furthermore, the research of Shang et al. [55] highlights stakeholder’s concerns about how manufacturers/suppliers of these technologies will use the data collected during their operation (for example, management data, input data, production yield and so on) and the influence that this concern exerts on the adoption decision. The ownership of said data is also an open and sensitive issue.
Infrastructure: Another discouraging factor is the lack of appropriate infrastructure to support the transition to A.I. technologies. For example, the lack of a sufficient telecommunications network (telephony, internet, etc.), or of a proper energy grid in rural areas—services that are absolutely necessary for the proper functioning of most A.I. technologies—pose major obstacles to their adoption [56,57]. Equally discouraging is unavailable technical support, which is needed to guarantee a smooth operation [58].
Interoperability: Stakeholders—like all consumers—are most often not loyal exclusively to a single technology manufacturer (brand), but instead buy products from different manufacturers depending on the circumstances. Reduced interoperability between the different technologies of different manufacturers discourages adoption and creates anxiety as to whether the adoption of a new technology will work smoothly and in synergy with the existing ones [45]. In addition, the difficulty in achieving collaboration between new and existing (conventional) technologies also discourages adoption [59].
Product: The most discouraging factors associated with the use of products are: not being able to test and see the benefits of these technologies before deciding to adopt them [60], and the lack of sufficient products to cover the full range of potential needs that stakeholders may have [61], as well as the lack of user-friendliness of these technologies [62].
Approach: Finally, the approach and perspective stakeholders have towards A.I. technologies significantly influence their adoption. For example, many farmers do not even go through the process of assessing the benefits they could have by adopting these technologies simply because they prefer to stick to traditional and well-known farming practices [63]. This phenomenon is further intensified if the stakeholder’s needs are satisfied by the performance of current conventional technologies [56]. At the same time, the decision concerning adoption is significantly influenced by the testimonies of colleagues in their social environment. When there is absence of information regarding the benefits of these technologies, the only information input the stakeholders have are the opinions of colleagues of their social environment [27].

5. Conclusions

The present research attempted to identify the factors that encourage and discourage the adoption of A.I. technologies in agriculture, livestock farming and aquaculture, through a systematic literature review using the PRISMA 2020 guidelines [12].
The research identified twenty encouraging factors, grouped into seven categories and twenty-one discouraging factors, grouped into eight categories. The research was based on 38 articles on Artificial Intelligence, either autonomously or as a key component of new technologies introduced in agriculture, livestock farming and aquaculture.
The most important adoption factors were whether the investment is cost-effective and whether there is confidence that these technologies can benefit the stakeholder, along with ensuring that these technologies are reliable, work seamlessly and deliver the results promised. Younger and more educated people are more likely to adopt these technologies. It is necessary to take measures so that those interested are informed and educated about new technologies, become familiar with them, and see their usefulness in practice, while the technology itself should be as user-friendly as possible [17]. Ideally, users should be allowed to be involved in the development process and their concerns and opinions should be taken into account. The researchers Uddin et al. [64] and Joshi et al. [65] consider that the training of stakeholders is relatively easy in developed countries due to the existing adoption and acclimatization of stakeholders to many modern technologies. Research has shown that adoption usually happens gradually; that is, instead of all of it happening at once, stakeholders usually gradually adopt each technology, part by part, after becoming familiar with each, seeing its results in action and being convinced that further adoption is indeed beneficial [66]. The adoption of A.I. technologies requires the stakeholder’s trust in the manufacturers/providers in order to also trust that their data is protected and not being used in dubious ways by the latter, like being resold [27], etc. If possible, adoption should be encouraged through various means to help familiarize and inform stakeholders about the new technologies, such as offering affordable and user-friendly products—like smart phone applications—providing training and information material, as well as financial means, such as offering technology trials, leasing solutions in order to reduce the financial burden and so on. Manufacturer/providers should also guarantee that stakeholders will maintain control of their production facilities, without replacing or undermining their ability to make decisions. All of the above will launch the adoption of new technologies, initially to the young, most educated and most innovative members of the various professional fields, and then—further enhanced with the help of peer influence—the adoption will gradually extend to the rest.
The most important factors discouraging adoption were the financial cost of the investment and the risk of the investment, but also the specific knowledge needed to use these technologies, followed by concerns about data and infrastructure security (from potential sabotage, leaks, usage, etc.), as well as the complexity and information overload that usually go hand in hand with new technologies. Other important factors were the unsuitability of existing infrastructure (internet, energy grid, etc.) as well as reduced interoperability between various technologies, new and old. Finally, the lack of awareness and familiarity of stakeholders with A.I. technologies are major obstacles to their adoption. Treating the above obstacles with appropriate measures will reduce hesitation and help the gradual adoption of said technologies.

Limitations

The research was based on 38 articles, with the vast majority of them focusing on the field of agriculture (although, in a broader sense, the term “agriculture” includes livestock farming and aquaculture as well [67]). And while the findings of the research are generally encountered in any market context, it is necessary to carry out more original research, particularly in the areas of livestock farming and aquaculture, in order to collect data that is missing from the available literature.
Furthermore, due to choosing only two databases (Scopus and Web of Science), it is possible that many related publications that are not hosted in these databases were omitted.
Finally, due to the selection of articles written only in English, it is possible that relevant publications written in other languages were omitted as well.

Author Contributions

Conceptualization, J.A.T.; methodology, J.A.T., V.P.G. and D.C.G.; resources, J.A.T., D.C.G. and V.P.G.; data curation, V.P.G. and D.C.G.; writing―original draft preparation, V.P.G.; writing―review and editing, V.P.G., D.C.G. and J.A.T.; supervision, D.C.G. and J.A.T.; project administration, J.A.T.; funding acquisition, J.A.T. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by the action “Improving Competitiveness of the Greek Fish Farming Through Development of Intelligent Systems for Disease Diagnosis and Treatment Proposal and Relevant Risk Management Supporting Actions” (MIS 5067321). EU-Greece Operational Program of Fisheries EPAL 2014-2020.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data supporting reported results can be found in the Open Science Framework database: https://doi.org/10.17605/OSF.IO/M4H7E.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. Papers included in the systematic literature review.
Table A1. Papers included in the systematic literature review.
CodeTitleWritersMagazineYear
W1 [14]A.I. APPLICATIONS OF DATA SHARING IN AGRICULTURE 4.0: A FRAMEWORK FOR ROLE-BASED DATA ACCESS CONTROLSpanaki, K., Karafili, E., and Despoudi, S.International Journal of Information Management2021
W2 [13]AN INTERDISCIPLINARY APPROACH TO ARTIFICIAL INTELLIGENCE IN AGRICULTURERyan, M., Isakhanyan, G., and Tekinerdogan, B.NJAS: Impact in Agricultural and Life Sciences2023
W3 [68]AN INTERPRETABLE ARTIFICIAL INTELLIGENCE BASED SMART AGRICULTURE SYSTEMSabrina, F., Sohail, S., Farid, F., Jahan, S., Ahamed, F., and Gordon, S.Computers, Materials & Continua2022
W4 [44]ARTIFICIAL INTELLIGENCE TECHNOLOGY IN THE AGRICULTURAL SECTOR: A SYSTEMATIC LITERATURE REVIEWElbaşı, E., Mostafa, N., AlArnaout, Z., Zreikat, A., Cina, E., Varghese, G., Shdefat, A., Topcu, A., Abdelbaki, W., and Zaki, C.IEEE Access2023
W5 [69]EXPLORING THE ROLE OF GREEN AND INDUSTRY 4.0 TECHNOLOGIES IN ACHIEVING SUSTAINABLE DEVELOPMENT GOALS IN FOOD SECTORSHassoun, A., Prieto, M.A., Carpena, M., Bouzembrak, Y., Marvin, H.J.P., Pallarés, N., Barba, F.J., Bangar, S.P., Chaudhary, V., Ibrahim, S., and Bono, G.Food Research International2022
W6 [57]SCIENTIFIC DEVELOPMENT OF SMART FARMING TECHNOLOGIES AND THEIR APPLICATION IN BRAZILPivoto, D., Waquil, P.D., Talamini, E., Pauletto, C., Dalla, F., and de Vargas Mores, G.Information Processing in Agriculture2018
W7 [61]SMART SUSTAINABLE AGRICULTURE (SSA) SOLUTION UNDERPINNED BY INTERNET OF THINGS (IOT) AND ARTIFICIAL INTELLIGENCE (A.I.)Alreshidi, E.International Journal of Advanced Computer Science and Applications2019
W8 [43]ADOPTION OF COMPUTER-BASED TECHNOLOGY (CBT) IN AGRICULTURE IN KENTUCKY, USA: OPPORTUNITIES AND BARRIERSGyawali, B.R., Paudel, K.P., Jean, R., and Banerjee, S. “Ban”.Technology in Society2023
W9 [29]ADOPTION OF SMART FARMING TECHNOLOGY AMONG RICE FARMERSZaman, N.B.K., Raof, W.N.A.A., Saili, A.R., Aziz, N.N., Fatah, F.A., and Vaiappuri, S.K.N.Journal of Advanced Research in Applied Sciences and Engineering Technology2023
W10 [70]ADVANCES IN SITE-SPECIFIC WEED MANAGEMENT IN AGRICULTURE—A REVIEWGerhards, R., Sanchez, A., Hamouz, P., Peteinatos, G.G., Christensen, S., and Fernandez-Quintanilla, C.Weed Research2022
W11 [52]AGRICULTURE 4.0: IS SUB-SAHARAN AFRICA READY?Jellason, N.P., Robinson, E.J.Z., and Ogbaga, C.C.Applied Sciences2021
W12 [60]APPLICATION, ADOPTION AND OPPORTUNITIES FOR IMPROVING DECISION SUPPORT SYSTEMS IN IRRIGATED AGRICULTURE: A REVIEWAra, I., Turner, L., Matthew, T.H., Monjardino, M., deVoil, P., and Rodriguez, D.Agricultural Water Management2021
W13 [71]APPLICATIONS OF DATA MINING AND MACHINE LEARNING FRAMEWORK IN AQUACULTURE AND FISHERIES: A REVIEWGladju, J., Kamalam, B.S., and Kanagaraj, A.Smart Agricultural Technology2022
W14 [72]APPLICATIONS OF REMOTE SENSING IN PRECISION AGRICULTURE: A REVIEWSishodia, R.P., Ray, R.L., and Singh, S.K.Remote Sensing2020
W15 [73]ARTIFICIAL INTELLIGENCE IN AGRICULTURAL VALUE CHAIN: REVIEW AND FUTURE DIRECTIONSChandirasekaran, G., Jena, S., Sivakumar, A., and Nambirajan, T.Journal of Agribusiness in Developing and Emerging Economies2021
W16 [74]ARTIFICIAL INTELLIGENCE RESEARCH IN AGRICULTURE: A REVIEWSood, A., Sharma, R.K., and Bhardwaj, A.K.Online Information Review2020
W17 [75]ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: LIMITATIONS AND POTENTIAL NEXT STEPS FOR MODELING AND MODELERS IN THE ANIMAL SCIENCESJacobs, M., Remus, A., Gaillard, C., H.M., III, Tedeschi, L.O., Neethirajan, S., and Ellis, J.L.Journal of Animal Science2022
W18 [34]AUTOMATION AND DIGITIZATION OF AGRICULTURE USING ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGSSubeesh, A., and Mehta, C.R.Artificial Intelligence in Agriculture2021
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W21 [36]COMMUNICATING AGRICULTURE A.I. TECHNOLOGIES: HOW AMERICAN AGRICULTURAL PRODUCERS» PERCEPTION OF TRUSTWORTHINESS, RISK PERCEPTION, AND EMOTION AFFECT THEIR LIKELIHOOD OF ADOPTING ARTIFICIAL INTELLIGENCE IN FOOD SYSTEMSChen, K., Cate, A., and Cheren, H.Environmental Communication2023
W22 [77]DESIGN THINKING AND COMPLIANCE AS DRIVERS FOR DECISION SUPPORT SYSTEM ADOPTION IN AGRICULTUREBaumont De Oliveira, F.J., Fernandez, A., Hernández, J.E., and del Pino, M.International Journal of Decision Support System Technology (IJDSST)2022
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W24 [27]DIGITAL TRANSFORMATION FOR A SUSTAINABLE AGRICULTURE IN THE UNITED STATES: OPPORTUNITIES AND CHALLENGESKhanna, M., Atallah, S.S., Kar, S., Sharma, B., Wu, L., Yu, C., Chowdhary, G., Soman, C., and Guan, K.Agricultural Economics2022
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Sustainability 15 16385 g001
Figure 1. PRISMA 2020 flow diagram.
Figure 1. PRISMA 2020 flow diagram.
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Figure 2. Selected articles’ publications by year during the period January 2017–June 2023.
Figure 2. Selected articles’ publications by year during the period January 2017–June 2023.
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Table 1. Search strings by database.
Table 1. Search strings by database.
DatabaseSearch Suggestion
WoS Core Collection(TI=(“ARTIFICIAL INTELLIGENCE”) OR AB=(“ARTIFICIAL INTELLIGENCE”) OR AK=(“ARTIFICIAL INTELLIGENCE”)) AND (TI=(ADOPT OR ADOPTION) OR AB=(ADOPT OR ADOPTION) OR AK=(ADOPT OR ADOPTION)) AND (TI=(HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE OR *CULTURE) OR AB=(HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE) OR AK=(HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE))
Scopus(TITLE (“ARTIFICIAL INTELLIGENCE”) OR ABS (“ARTIFICIAL INTELLIGENCE”) OR KEY (“ARTIFICIAL INTELLIGENCE”)) AND (TITLE (ADOPT OR ADOPTION) OR ABS (ADOPT OR ADOPTION) OR KEY (ADOPT OR ADOPTION)) AND (TITLE (HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE OR *CULTURE) OR ABS (HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE) OR KEY (HUSBANDRY OR LIVESTOCK OR AGRICULTURE OR AQUACULTURE OR MARICULTURE OR PISCICULTURE OR MARINE))
Table 2. Types and frequencies of selected articles.
Table 2. Types and frequencies of selected articles.
TypeArticlesFrequencies
Literature ReviewW1, W5, W10, W11, W12, W13, W14, W15, W17, W18, W19, W20, W24, W25, W27, W29, W31, W32, W34, W36, W3755%
Original ResearchW2, W3, W6, W7, W8, W9, W21, W22, W23, W26, W28, W3032%
Systematic ReviewW4, W16, W33, W3811%
Special ReportW353%
Table 3. Frequencies of categories of encouragement.
Table 3. Frequencies of categories of encouragement.
GroupsFrequencies
Product28.1%
Demographic18.0%
Economics16.9%
Information16.9%
Utility11.2%
Approach5.6%
Security3.4%
Table 4. Factors encouraging adoption.
Table 4. Factors encouraging adoption.
Encouraging FactorsArticlesnn
Security 3
Ensuring data protection (sabotage, leakage, how data is used by the provider, etc.)W1, W22
Ensure that the stakeholders maintain control over the technology—not being dependent on A.I. technology providersW211
Economics 15
Familiarity with A.I. technologies through initial affordable solutions (e.g., mobile applications)W21
Whether it is possible to lease the equipment (e.g., through a subscription fee) in order to reduce the financial burdenW91
Whether it is considered beneficial (cost-benefit analysis)W21, W24, W26, W27, W29, W30, W35, W36, W389
Having sufficient capital to make the transition from conventional to sustainable A.I. W8, W16, W34, W384
Product 25
When the end user is involved in the development process with suggestions/opinions/concerns, and so on.W1, W2, W22, W23, W26, W376
When the product can be tailored to the needs and requirements of the end userW1, W362
Whether the technology is user-friendlyW2, W14, W16, W21, W34, W386
Whether training programs are available regarding the use of said technologiesW1, W2, W7, W16, W26, W28, W367
Whether it is possible to test the technology before making a decision (available trial)W12, W24, W383
Whether the software is open sourceW151
Information 15
Demonstration on how this technology will benefit the end userW1, W2, W7, W8, W9, W12, W17, W22, W269
Familiarization with products through information (regarding features, operation, usage, supply, etc.)W3, W9, W16, W21, W30, W386
Demographic 16
Higher level of education/knowledge related to technologyW8, W11, W21, W25, W28, W30, W387
Young ageW8, W11, W21, W23, W25, W26, W28, W30, W389
Approach 5
Taking into account whether colleagues have adopted the new technologiesW8, W9, W24, W384
Whether there is trust in the A.I. technology providerW211
Utility 10
Whether products are considered usefulW21, W382
Whether the equipment is considered reliable/works as intended/produces the results promisedW16, W18, W21, W22, W26, W29, W36, W388
Table 5. Frequencies of categories of discouragement.
Table 5. Frequencies of categories of discouragement.
GroupsFrequencies
Economics31.5%
Knowledge21.8%
Information13.7%
Security12.9%
Infrastructure9.7%
Interoperability5.6%
Product2.4%
Approach2.4%
Table 6. Factors discouraging adoption.
Table 6. Factors discouraging adoption.
Discouraging FactorsArticlesnn
Security 16
Concern about data/infrastructure security (sabotage, info leakage, way data is used by the provider etc.)W1, W2, W5, W13, W18, W21, W24, W26, W29, W33, W34, W36, W3713
No desire to depend/hand over control to A.I. technology providersW2, W10, W213
Economics 39
Cost (initial investment cost, maintenance, upgrades, training costs and so on)W2, W4, W5, W7, W8, W9, W11, W13, W15, W16, W18, W19, W20, W21, W24, W25, W26, W28, W32, W33, W36, W3822
Adoption/investment risk (small profit that is not worth the transition, profit is not evident and so on)W8, W9, W10, W11, W12, W13, W14, W21, W23, W24, W25, W26, W35, W3714
Lack of government funding/subsidy for transitionW9, W262
Time required for the adoption of A.I. technologiesW81
Knowledge 27
Technological/technical knowledge and skills required (education/training required)W2, W4, W5, W6, W9, W10, W11, W14, W16, W18, W19, W20, W23, W24, W28, W32, W33, W3618
Technology complexity/information overloadW7, W8, W12, W23, W24, W31, W33, W36, W389
Information 17
Lack of understanding of how A.I. technologies can benefit the stakeholdersW4, W8, W23, W244
Lack of awareness/familiarity with A.I. technologiesW8, W11, W21, W23, W29, W31, W33, W388
Lack of acceptance of A.I. technologies due to misinformationW111
Lack of practical knowledge on whether these technologies work/are reliable/do what they promiseW7, W24, W31, W334
Infrastructure 12
There is no digital infrastructure (ecosystem) in which they can joinW51
There is no suitable infrastructure (e.g., proper energy grid, telecommunications etc.)W6, W18, W20, W23, W24, W26, W27, W29, W389
Lack of support of any kind (e.g., technical, information, etc.)W28, W382
Interoperability 7
Lack of interoperability between technologies (either among A.I. or between A.I. and conventional ones)W6, W7, W18, W24, W25, W27, W297
Product 3
Lack of testing (available trial) before making a decisionW121
Lack of sufficient A.I. products that can meet all needsW71
Technology is not user-friendlyW271
Approach 3
Taking into account whether the technology is not adopted by colleaguesW321
Satisfaction with the performance/productivity of current technologiesW8, W202
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Georgopoulos, V.P.; Gkikas, D.C.; Theodorou, J.A. Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020. Sustainability 2023, 15, 16385. https://doi.org/10.3390/su152316385

AMA Style

Georgopoulos VP, Gkikas DC, Theodorou JA. Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020. Sustainability. 2023; 15(23):16385. https://doi.org/10.3390/su152316385

Chicago/Turabian Style

Georgopoulos, Vasileios P., Dimitris C. Gkikas, and John A. Theodorou. 2023. "Factors Influencing the Adoption of Artificial Intelligence Technologies in Agriculture, Livestock Farming and Aquaculture: A Systematic Literature Review Using PRISMA 2020" Sustainability 15, no. 23: 16385. https://doi.org/10.3390/su152316385

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