EXPLORING BENEFITS, OVERCOMING CHALLENGES, AND SHAPING FUTURE TRENDS OF ARTIFICIAL INTELLIGENCE APPLICATION IN AGRICULTURAL INDUSTRY | The American Journal of Agriculture and Biomedical Engineering

EXPLORING BENEFITS, OVERCOMING CHALLENGES, AND SHAPING FUTURE TRENDS OF ARTIFICIAL INTELLIGENCE APPLICATION IN AGRICULTURAL INDUSTRY

HAC
inLibrary
Google Scholar
doi
 
CC BY f
11-27
13
To share
Sanchita Saha, ., Ashok Ghimire, ., Mia Md Tofayel Gonee Manik, ., Anamika Tiwari, ., & Md Ahsan Ullah Imran, . (2024). EXPLORING BENEFITS, OVERCOMING CHALLENGES, AND SHAPING FUTURE TRENDS OF ARTIFICIAL INTELLIGENCE APPLICATION IN AGRICULTURAL INDUSTRY . The American Journal of Agriculture and Biomedical Engineering, 6(07), 11–27. https://doi.org/10.37547/tajabe/Volume06Issue07-03
0
Citations
Crossref
Сrossref
Scopus
Scopus

Abstract

The global population, now at 8 billion and projected to reach 9.7 billion by 2050, necessitates a significant increase in food production. This escalating demand underscores the importance of artificial intelligence (AI) technologies in agriculture, which enhance resource optimization and productivity amid supply chain pressures and more frequent extreme weather events. A systematic literature review (SLR), conducted using the PRISMA methodology, examined AI applications in agriculture, encompassing 906 relevant studies from five electronic databases. From these, 176 studies were selected for bibliometric analysis, with a quality appraisal further refining the selection to 17 key studies. The review highlighted a notable rise in publications over the past five years, identifying over 20 AI techniques, including machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision, as predominant. The research emphasized significant contributions from India, China, and the USA, focusing on sectors like crop management, prediction, and disease and pest management. The study concluded with an analysis of current challenges and future trends, pointing to promising directions for AI in agriculture to meet global food production demands.


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

11

https://www.theamericanjournals.com/index.php/tajabe

PUBLISHED DATE: - 27-07-2024
DOI: -

https://doi.org/10.37547/tajabe/Volume06Issue07-03

PAGE NO.: - 11-28

EXPLORING BENEFITS, OVERCOMING
CHALLENGES, AND SHAPING FUTURE
TRENDS OF ARTIFICIAL INTELLIGENCE
APPLICATION IN AGRICULTURAL INDUSTRY


Sanchita Saha

Department Of Business Administration, Westcliff University, 17877 Von Karman Ave 4th

Floor, Irvine, CA 92614, United States

Ashok Ghimire

Department Of Business Administration, Westcliff University, 17877 Von Karman Ave 4th

Floor, Irvine, CA 92614, United States

Mia Md Tofayel Gonee Manik

Department Of Business Administration, Westcliff University, 17877 Von Karman Ave 4th

Floor, Irvine, CA 92614, United States

Anamika Tiwari

Department Of Business Administration, Westcliff University, 17877 Von Karman Ave 4th
Floor, Irvine, CA 92614, United States

Md Ahsan Ullah Imran

Department Of Business Administration, Westcliff University, 17877 Von Karman Ave 4th
Floor, Irvine, CA 92614, United States

RESEARCH ARTICLE

Open Access


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

12

https://www.theamericanjournals.com/index.php/tajabe

INTRODUCTION

Geopolitical events and climatic disruptions are

increasingly straining supply chains and

undermining the resilience of food systems,

presenting significant challenges to global efforts
to end hunger and food insecurity [1]. The COVID-

19 pandemic has exacerbated these vulnerabilities,
exposing critical weaknesses in agri-food systems

and deepening societal inequalities [2]. As the
global population is projected to reach around 10

billion by 2050, the Food and Agriculture
Organization (FAO) estimates that food demand

will surge by 70% [3]. This dramatic rise
necessitates innovative solutions to enhance food

production and distribution. Artificial intelligence
(AI) offers a promising avenue for addressing these

challenges by optimizing agricultural processes. AI
techniques can improve crop yields, reduce waste,

and streamline supply chains, thereby enhancing

the overall resilience of food systems. By
leveraging AI, we can develop more efficient and

sustainable agricultural practices that are better
equipped to meet the growing food demands of the

future while mitigating the impacts of geopolitical
and climatic disruptions.
AI, a key discipline within computer science,

focuses on developing algorithms that mimic the
cognitive,

physiological,

or

evolutionary

phenomena observed in nature and human beings

[4]. Unlike traditional models that require explicit

knowledge of problem-solving paths, AI relies on
data, examples, and relationships to facilitate

diverse problem resolutions. This approach allows
AI to exhibit intelligent behavior akin to human

experts in specific tasks. Presently, AI is
predominantly directed towards solving problems

involving large, dynamic data sets that often

contain

inaccuracies

and

contradictions.

Techniques such as iterative methods and

interconnected neural network architectures,
commonly referred to as "Machine Learning" and

"Deep Learning," form the backbone of modern AI
[5-7]. These methods are widely applied across

various domains, unified by their ability to analyze
vast and complex data structures influenced by

temporal and uncertain factors.
Agriculture, an intricate sector blending science,

engineering, and economic principles, has
undergone significant advancements through the

integration of AI. Comprehensive reviews by Ruiz-
Real, et al. [8] and Jha, et al. [9] highlight the

transformative impact of expert systems and
decision

support

systems

in

optimizing

agricultural

processes

and

supply

chain

management. These systems simulate agricultural

operations, enhancing resource allocation and
process efficiency. AI's applications in quality

control, as explored by Nair and Mohandas [10],

Abstract


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

13

https://www.theamericanjournals.com/index.php/tajabe

enable precise monitoring through artificial vision
systems. Additionally, AI facilitates policy

formulation, exemplified by Bryceson and
Slaughter [11] analyzed AI as a collaborative tool

among agri-food chain stakeholders. Economic
studies applying neural networks and machine

learning techniques to agri-food product pricing
reveal AI's potential in predicting and stabilizing

market fluctuations. In climate science, researchers

like Siegert, et al. [12] and Kosovic, et al. [13] used
AI to model and predict solar radiation, aiding in

climate management and agricultural planning.
The interest in AI's agricultural applications has

surged due to its robust data analysis capabilities.

The rise of Agriculture 3.0 introduced robotics and
automation, revolutionizing traditional practices

with sophisticated machinery capable of
autonomous planting, spraying, and harvesting

[14-16]. Agriculture 4.0 further enhances efficiency

through intelligent farms and interconnected
systems, focusing on precision agriculture to

optimize water, fertilizer, and phytosanitary
product use [17-19]. This approach, combined with

genetic engineering and big data analytics,
addresses challenges like climate change

adaptation and resource optimization. The
technification of agriculture, driven by Industry 4.0

concepts, has heightened AI's role, enabling agri-
food companies to streamline operations and drive

economic growth [20, 21]. This technological
evolution not only meets the rising demand for

richer diets but also enhances employment and
economic activities in industrial regions,

solidifying AI's crucial role in modern agriculture

and global food security.
Bibliometric studies that connect various

disciplines have become increasingly significant in

understanding the impact and future potential of
interdisciplinary synergies within the research

community. These studies serve as vital indicators
of interest and engagement in specific fields,

offering a comprehensive view of scientific
production and collaboration patterns. For

instance, Gu [22] demonstrated the global

structure of scientific output, highlighting the
intricate relationships between quality, references,

and author synergies. In the realm of artificial
intelligence (AI), Cobo, et al. [23] delved into its

evolution using various bibliometric indicators,
focusing on citations related to knowledge-based

systems. Despite the wealth of research in AI,
similar bibliometric studies in the agri-food

industry or agriculture are less common.
Nevertheless, the growing number of publications

in these fields, as evidenced by trends in Google
Scholar over the past five years, suggests a

burgeoning interest and a promising future for

interdisciplinary studies that explore the interplay
between AI and agriculture.
Literature reviews play a crucial role in

synthesizing the existing knowledge base, offering
insights into the application of AI in agriculture

[24]. Notable reviews include works on crop yield
prediction using machine learning [25], advanced

agricultural

disease

image

recognition

technologies [26], IoT solutions for smart farming,

and big data applications in agriculture [27-29].

These reviews highlight the diverse and innovative
applications of AI aimed at addressing various

agricultural challenges. In our study, we conducted
extensive research on AI applications in

agriculture, identifying seven key domains: crop
management,

water

management,

soil

management, fertilization, crop prediction, crop
classification, and disease and pest management.

From 176
studies selected for descriptive analysis, over 20

different AI techniques were identified. A
subsequent qualitative analysis distilled 17 articles

that provided detailed insights into the application,
challenges, and benefits of AI in agriculture,

underscoring the significant potential and ongoing
advancements in this field.

Research Purpose
SLR, as defined by [11], utilizes systematic methods

to collate and synthesize the findings of studies

addressing a formulated question, reported in
sufficient detail to enable replication of the

review's findings. This SLR aims to identify and
analyze recent studies on AI techniques applied in

agriculture, addressing specific questions and

recognizing emerging trends. The methodological
steps involved identification, screening, eligibility,


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

14

https://www.theamericanjournals.com/index.php/tajabe

and inclusion of relevant studies. Initially, research
questions were defined, followed by the

establishment of criteria for study inclusion and
exclusion. A comprehensive search in scientific

databases was then conducted to extract relevant
studies, which were subsequently analyzed to

answer the research questions. The SLR ensured

clarity and transparency through a four-phase
verification flowchart adapted from [12]. This

work aims to expand current research knowledge
by focusing on AI technologies in agriculture. Table

1 presents the questions formulated for this
review.

Table 1. Questions for study

ID

Research question

Justification

R1

Which nations have had the most impact in the field

of research universities, publications, and
seminal works in AI approaches used in
agriculture?

To provide the necessary background

information regarding AI research in
the agricultural sector.

R2

What are the most common AI methods used for

agricultural work?

Application

of

AI

methods

for

identifying key areas of agriculture.

R3

When it comes to using AI in agriculture, what are

the biggest pros and cons?

Highlights trends and problems while

revealing potential avenues for future
research and improvement.

This research aimed to contextualize the topics of

AI and agriculture by addressing the research
question posed in Q1. To tackle Q2 and Q3, a

framework adapted from existing literature was
employed. Figure 1 illustrates the intersections

between AI technology's potential impacts on
agricultural applications (Q2) and highlights the

associated challenges and benefits (Q3). The
framework concentrates on AI technologies, their

application domains, and the ensuing challenges
and benefits, building on the methodologies

proposed by previous studies.

METHODOLOGY

This section outlines the review principles of the

Systematic Literature Review (SLR), the criteria for

selecting studies, and the quality assessment of the

chosen studies. To examine the evolution of AI in

the agricultural industry as reflected in scientific
publications, a comprehensive bibliometric

analysis was conducted. The study employs a
systematic bibliographical approach centered on a

specific topic, adhering to a sequence of methodical
steps. These steps include: (a) defining the search

criteria, keywords, and time frame; (b) selecting
relevant databases; (c) refining the research

criteria; (d) exporting the full set of results; and (e)
analyzing and discussing the findings (see Figure

1). This structured process ensures a rigorous and
thorough exploration of the literature, facilitating a

clear understanding of AI's impact on agriculture
over time.

Figure 1. Stages of bibliometric analysis

DATA COLLECTION

For the systematic exploration of scientific

information, a comprehensive array of literature


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

15

https://www.theamericanjournals.com/index.php/tajabe

databases was meticulously utilized. These
databases, including ScienceDirect, Scopus,

Springer, IEEE Xplore, and MDPI, were selected for
their expansive coverage across various disciplines

and their robust repositories of scholarly works.
Each database was meticulously navigated to

ensure a thorough search for pertinent research
material. Table 2 serves as a crucial guide,

presenting the inclusion indicator for the data
collection phase, thus ensuring a systematic and

transparent approach in the retrieval and selection
of relevant literature. This meticulous selection

process underscores the commitment to rigor and
comprehensiveness in acquiring the necessary

foundation for the subsequent stages of analysis
and synthesis.

Table 2. Data collection source

Indicator

Description

Search interval

2017 to 2022

Databases Screening

ScienceDirect; Scopus; Springer; IEEE

Xplore; MDPI

Document types

Title, abstract, DOI, and year

Language

Review and original article

The proposed solution

English

Screening

Applied on agriculture

Search term

The search string devised for querying databases

on artificial intelligence and its applications in
agriculture incorporates a nuanced blend of

keywords, synonyms, and logical operators to
optimize the retrieval of relevant literature. By

amalgamating terms like "artificial intelligence"
and "agriculture" with synonyms and subarea

descriptors, the search aims to cast a wide net,
capturing varied perspectives and facets within the

intersection of these domains (see Table 3). The

strategic use of logical operators such as OR and

AND enhances the precision of the search, allowing
for the inclusion of diverse terms while

maintaining coherence in the retrieved results.
Employing this comprehensive query string in

advanced search fields of databases ensures a
systematic exploration of pertinent literature,

facilitating a robust understanding of the
advancements,

challenges,

and

potential

applications of artificial intelligence in agricultural

contexts.

Table 3. Search term of this study

Search term

TITLE-ABS-KEY (agriculture*) AND ( TITLE-ABS-KEY ( artificial* AND intelligence* ) OR

TITLE-ABS-KEY ( AI*) AND TITLE-ABS-KEY (machine* AND learning* ) AND TITLE-ABS-KEY
(deep* AND learning* )) AND PUBYEAR > 2012 AND PUBYEAR < 2022 AND ( LIMIT-TO (

DOCTYPE , "review" ) AND (DOCTYPE , "original article")) AND ( LIMIT-TO ( LANGUAGE ,
"English" ) )

Selection criteria

The systematic literature review (SLR) followed a

meticulous process of identification and screening
to ensure the inclusion of pertinent studies, as

depicted in Figure 2 of the PRISMA flow diagram.


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

16

https://www.theamericanjournals.com/index.php/tajabe

This paper's identification phase meticulously
considered

contemporary

knowledge

disseminated through peer-reviewed scientific
journals in English. Notably, exclusion criteria were

applied to filter out sources such as book chapters,
annals, and abstracts, thereby maintaining a focus

on robust, scholarly publications. By adhering to
these rigorous standards, the SLR aimed to

synthesize a comprehensive understanding of the
topic under investigation, rooted in the latest

empirical research findings available in the
academic landscape.

Figure 2. PRISMA flow diagram of this study

Selection criteria

The quality assessment of papers within the SLR

encompassed an in-depth evaluation of various

pertinent aspects, crucial for determining their
inclusion or exclusion during the screening phase.

This qualitative analysis was designed to
meticulously categorize and prioritize the articles

scrutinized within the SLR framework. Five distinct

quality evaluation criteria were employed,
encompassing both quantitative and content-

related dimensions. Among these criteria, three
were quantitative, directly linked to journal

metrics such as Impact Factor, Citescore, and
Citations, serving as indicators of scholarly impact

and relevance. Additionally, qualitative aspects,
notably the integration of AI technologies and

applications within the agriculture domain, were


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

17

https://www.theamericanjournals.com/index.php/tajabe

assessed for their alignment with the study's
objectives. Each criterion was assigned one of three

response options

high, medium, or low

based

on

predefined

thresholds,

ensuring

a

comprehensive and standardized assessment

process (see Table 4).

Table 4. Quality appraisal criteria

Indicator

Description

Impact Factor

Assess the significance of scientific

publications

Citescore

Stands for the typical amount of

references

Citations

The number of times a publication

has been referenced

Artificial intelligence technologies

Taking into account the technique,

citations, usage, and outcomes,
artificial intelligence technology is of

extreme significance.

Agriculture domain applications

Taking into account the technique,

citations, usage, and outcomes,

agriculture application is of extreme
significance.

During the eligibility analysis phase, thorough

scrutiny of the 176 selected studies revealed
distinct ranges of values pertinent to various

criteria. For instance, in assessing the impact
factor, a spectrum from 11.8 to 6.1 delineated high-

quality studies, each garnering 1.0 points in this

criterion, as outlined in Table 5. This quantitative
approach facilitated a nuanced evaluation, wherein

each record could amass points ranging from 0 to 3
across multiple criteria. By meticulously

delineating

such

ranges

and

assigning

corresponding point values, the eligibility analysis

not only ensured a comprehensive assessment but
also facilitated a structured comparison among the

diverse pool of studies under consideration.

Table 5. Quantitative assessment criteria

Criterion

High

Medium

Low

ImpactFactor

[11.8–6.1]

[6.0–3.1]

[3.0–1.58]

Citescore

[18.7–6.5]

[6.4–4.0]

[3.9–2.7]

Citations

[1195–100]

[99–10]

[9–0]

For a quality appraisal, all 176 papers that were chosen during the eligibility stage were reviewed in their

entirety. The papers with the highest scores are shown in Table 7.

Table 7. Quality assessment with a high score

References

AI technology

Application
domain

Citescore Impact

Factor

Citation

Score

[30]

Robotics and
automation; Computer

Crop
management

5.002

8.7

135

5.5


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

18

https://www.theamericanjournals.com/index.php/tajabe

vision; Convolutional
neural network

[31]

Genetic algorithm;
Internet of Things
(IoT)

Fertigation
management

11.072

15.8

25

5.5

[32]

Machine learning

Water
management

6.757

11.8

40

5.5

[33]

Digital twins

Crop
management

6.757

11.8

63

5.5

[34]

Machine learning

Crop prediction

8.171

12

12

5.5

[35]

Machine Learning
(ML); Internet of
Things (IoT)

Crop
management

3.476

7

12

5.0

[27]

Bigdata; Robotics

Crop
management

6.765

9.7

1195

5.0

[36]

Machine earning;
Computer vision

Crop
management

6.757

11.8

70

5.0

[37]

Deep learning;
Computer vision

Crop
Classification

3.889

5.0

19

4.5


RESULTS AND DISCUSSION

Descriptive Analysis(R1)
This section provides a quantitative analysis of the

176 selected studies, encompassing their

publication

and

citation

volumes,

the

methodologies employed in their identification,

and the predominant countries, journals, and

institutions driving their research. Figure 3 visually
represents the distribution of papers and citations

per year, offering insight into the temporal trends
of scholarly activity within the field. Through

meticulous examination, this section illuminates
the landscape of research, shedding light on the

prolific contributors, influential publications, and
the evolving discourse surrounding the subject

matter. By delineating these key metrics, it affords

a comprehensive understanding of the breadth and
impact of the studies under scrutiny, thereby

facilitating informed interpretation and guiding
future inquiry.


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

19

https://www.theamericanjournals.com/index.php/tajabe

Figure 3. Publications and citations per year

Over the past three years, there has been a

remarkable surge in both the number of

publications and citations within the field,
indicating a substantial growth in research activity

and scholarly engagement. Data collected up to
December 2022 revealed a peak of 2440

publications referencing works from 2020. This
surge underscores the dynamic feedback loop

prevalent in this area of research, with subsequent
years building upon and citing earlier findings.

These studies have been systematically classified
into theoretical and empirical categories (see Table

8). Theoretical inquiries encompass reviews and
SLRs, while empirical investigations include

modeling and simulation studies, surveys, and case
studies. This structured classification offers

valuable insights into the diverse methodologies

employed within the field, reflecting its
multidisciplinary nature and the breadth of

approaches undertaken to advance knowledge and
understanding.

Table 8. Identified studies

Studies

Category

Total

%

Theoretical

Reviews

59

34

Systematic reviews

16

9

Total

75

43

Empirical

Modeling and simulations

68

39

Case studies

13

7

Surveys

20

11

Total

101

57

Overall total

176

100

The distribution of studies was balanced, with 43%

classified as theoretical and 57% as empirical. The
theoretical studies predominantly focused on

literature reviews, accounting for 34% of the total

with 59 papers. Among the empirical studies,
modeling and simulation were particularly

prominent, comprising 39% of 68 papers. This
distribution is mirrored in the quality appraisal


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

20

https://www.theamericanjournals.com/index.php/tajabe

steps, where notable articles such as [38] and [39]
exemplify the applied research in the field. These

studies respectively developed innovative systems
for robotic strawberry picking and smart

irrigation, highlighting the practical implications of
both theoretical and empirical research in

advancing technological applications.
When evaluating the significance of a dissertation

Bibliometric analysis sheds light on the quantity
and influence of publications by use of citation

metrics; it assesses the relevance of research

articles by means of a number of variables. The
Netherlands stands out as the most influential

country in this domain, achieving 1629 citations
from only 5 publications. India is a close contender

with 37 publications accumulating 1499 citations.
Greece, although contributing just 3 publications,

has a substantial impact with 1002 citations. China,
with 23 publications, has garnered 899 citations.

Table 9 provides a comprehensive overview of

countries with more than 100 citations,
highlighting the global reach and influence within

this research area.

Table 9. Quality assessment with a high score




Score

Country

Publications Citations

1

Netherlands

5

1629

2

India

37

1499

3

Greece

3

1002

4

China

23

899

5

Spain

5

868

6

USA

10

679

7

Australia

5

649

8

Brazil

9

556

9

France

2

232

10

Egypt

3

185

11

New Zealand

2

168

12

Italy

7

157

13

Pakistan

5

144

14

Malaysia

7

115

15

Portugal

2

107

16

Canada

3

105

17

Chile

4

100

Over the last three years, the publications and

citations of artificial intelligence (AI) techniques

applied to agriculture have increased almost
sixfold, underscoring the growing significance and

relevance of this research area. The most

influential countries in this domain are among the
world's largest food producers, highlighting the

global recognition of AI's potential to revolutionize

agricultural practices. Additionally, numerous
high-impact journals have emerged as key

platforms for disseminating groundbreaking
research in this field, further demonstrating the

scholarly and practical importance of integrating
AI in agriculture.


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

21

https://www.theamericanjournals.com/index.php/tajabe

Artificial Intelligence in Agriculture (R2)
Agriculture domain

Agriculture, defined as the science of cultivating

land and raising livestock, is essential for
producing the food and resources necessary for

human survival. Central to this practice is the
physical environment, which serves as the

foundational resource base, and the cultivated crop
plant, which is the primary unit of production [40].

The success of agricultural endeavors hinges on the
effective management of the physical environment

to meet the biological demands of these crops. Key
factors influencing crop yield include soil

productivity, water availability, climate conditions,

and the control of pests and diseases [41].
Mastering these variables is crucial for optimizing

agricultural output and ensuring the sustainability
and resilience of food systems.
Artificial intelligence is revolutionizing the

agricultural sector by optimizing processes and
resources. This review identified seven key

applications:

crop

management,

water

management,

soil

management,

chemical

application, fertigation, crop prediction, and crop
classification, as summarized in Table 10. The

primary goal of crop management is to rationalize
resource use [42]. Water management focuses on

optimizing irrigation and water use on farms, while

soil management is crucial for the success of site-
specific cropping systems. Proper chemical

application is vital for environmental and economic
sustainability [43]. Fertigation, the use of irrigation

systems for fertilizer application, has been shown
to enhance fertilizer effectiveness. Crop prediction

and classification, utilizing image processing and
deep learning [44], are essential for sustainable

resource utilization. Effective management of
diseases and pests is critical for improving crop

yield, quality, and overall food security.

Table 10. Uses in the agricultural sector

References Domain

Details

[45]

Crop management

Included are planting, tending, harvesting, storing,
and distributing seeds

[46]

Water
management

Streamlining irrigation methods and processes to
maximize utilization of water

[47]

Soil management

Make sure plants get enough nutrients.

[48]

Fertirrigation

Technology with the goal of providing data for a
different application sector and a worldwide
perspective on crop distribution

[34]

Crop prediction

The logistical management of farmers relies heavily
on crop productivity projection

[49]

Crop classification The purpose of crop categorization is to provide a

comprehensive picture of crop dispersion and
related data for a different field of usage

[50]

Disease and pest
management

Impair crop yields and quality while decreasing the
efficiency of resource utilization. Protecting
agricultural crops from the many different kinds of
weeds,

animals,

and

microbes

necessitates

technological solutions.


Artificial Intelligence Technologies

The integration of AI into agriculture, aimed at

enhancing food production while mitigating the

effects of climate change, poses significant

challenges rooted in the analysis of AI technologies.

Originating from the conceptualization of cognitive
processes and neurobiology in the 1950s, AI's

evolution has delineated four distinct categories of
intelligent systems: those that emulate human


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

22

https://www.theamericanjournals.com/index.php/tajabe

thought, behavior, rational thinking, and rational
action. Success in these categories is gauged by

their fidelity to human performance or rationality
[8]. AI systems are proficient in data storage,

manipulation,

knowledge

acquisition,

representation, and deduction of new knowledge

from existing information. Within the realm of
agriculture,

AI

technologies are diverse,

encompassing cognitive science, robotics, and

natural interface applications. These technologies,
as identified across numerous studies, are

bolstered by auxiliary technologies like IoT, big
data, and cloud computing, enabling the

implementation of specific AI techniques such as
computer vision, robotics, machine learning,

augmented reality, and virtual reality.
The analysis of 17 selected articles in the quality

assessment stage revealed a spectrum of AI
technologies and their applications in agriculture,

as summarized in Table 11. Notably, the focus
encompassed innovations such as irrigation,

disease, and pest management systems, which
emerged

prominently

in

the

literature.

Additionally, a comprehensive review of these
advancements, as presented in [24], provided

valuable insights into the field's progression.

Furthermore, the development of a smart
irrigation system, detailed in [39], showcased

practical implementations of AI in enhancing
agricultural practices. Moreover, the exploration of

emerging technologies like agricultural digital
twins, highlighted in [33], underscored the ongoing

efforts to capture the intricate interactions
between living systems and their environment.

Table 10. AI technologies that were mentioned in the chosen papers

References Domain

Details

[30]

Robotics and
automation

The utilization of machinery, software, and other
technology to carry out activities that replace or
mimic human movements

[51]

Drones and
unmanned aerial
vehicles (UAVs)

Unmanned aerial vehicles that are capable of being
commanded remotely

[32]

Machine learning
(ML)

This system has the ability to adapt its behavior on
its own using various algorithms that can be used
to evaluate performance and make accurate
predictions.

[52]

Artificial neural
networks(ANNs)

Computer systems that mimic human intelligence,
which is able to acquire new knowledge and adjust
to novel and ever-changing circumstances.

[37]

Deep learning:
convolutional
neural network
CNN)

A collection of algorithms connected to machine
learning forms its basis. CNNs use picture patterns
to identify objects, classifications, and categories.

[30]

Genetic algorithm
(GA)

Machine learning algorithms that simulate evolving
processes and resolve issues by studying biological
evolution.

[31]

Computer vision

Automatic picture acquisition, analysis, and
comprehension are all part of computer vision,
which includes issues like object detection, motion
tracking, and action recognition.


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

23

https://www.theamericanjournals.com/index.php/tajabe

[53]

Digital twins

The goal of this virtual representation is to
maximize efficiency and production

[36]

Internet of Things
(IoT)

Establishes connections between various intelligent
devices, making crop management easier

[54]

Cloud computing

Assets like storage for information and computer
power are made available on request

[55]

Big data

Information is gathered, processed, and analyzed

In our comprehensive review of the literature, we

have discerned seven primary applications that
underscore the diverse utility of artificial

intelligence (AI) in agricultural contexts. These
applications encompass crop management, water

management, soil management, fertigation, crop

prediction,

crop

classification,

and

the

identification and mitigation of diseases and pests.

Furthermore, our analysis has unveiled a rich
tapestry of AI techniques, numbering twenty-four

distinct methodologies. Notably, prevalent among
these techniques are big data analysis, Internet of

Things (IoT) integration, and cloud computation.
Of these applications, crop management, water

management, and disease and pest control emerge
as the most prevalent areas of focus, indicating

their significance in addressing contemporary
agricultural challenges. Within the realm of AI

techniques, machine learning, robotics, deep
learning, and IoT technologies stand out as the

most frequently employed tools, underscoring

their pivotal role in advancing agricultural
innovation and sustainability.
Benefits, Challenges and Trends (RQ3)
The integration of machine learning, deep learning,

and computer vision techniques within agriculture

has heralded a transformative era known as
Agriculture 4.0 or Digital Agriculture. This

paradigm shift leverages advanced technologies
like precision agriculture, IoT, and cloud

computing to revolutionize farming practices.
Precision

agriculture

optimizes

resource

utilization and minimizes environmental impact
through real-time monitoring aided by remote

sensing technologies. Agriculture 4.0, akin to
Industry 4.0, harnesses big data and novel

technologies across the supply chain, aiming to
enhance productivity while reducing waste.

Moreover, the emergence of next-generation
agriculture, denoted as 5.0 and 6.0, emphasizes

deep learning and robotic advancements to
balance

production

and

environmental

sustainability. Underpinning these advancements
is the pervasive use of artificial intelligence,

facilitating efficient crop management, irrigation,
and disease detection by processing vast datasets.

This synergy of technologies underscores a pivotal
shift towards intelligent farming systems, where

data-driven insights empower farmers to make

informed

decisions,

fostering

agricultural

sustainability and productivity.
UAVs have revolutionized data collection in

agriculture, offering vast and intricate datasets.
Leveraging big data analytics tools and cloud

computing can markedly enhance data processing
efficiency, bolster data security, ensure scalability,

and reduce operational costs. Furthermore, the
integration of ML, ANN-based, and deep learning

techniques shows promise, particularly in crop

prediction, owing to the abundance of data from
diverse sources. Amidst these advancements,

emerging technologies like DT are reshaping the
agricultural landscape. While PA remains

prominent, newer terms such as Agriculture 4.0
and smart farming are gaining traction in scholarly

discourse. Notably, there's a call for tailored
research endeavors, considering regional climate

and crop specifics, particularly in countries like
Brazil, which are yet to fully harness their scientific

potential

in

this

domain.

Additionally,

understanding the intricacies of crop production

chains is crucial for effectively applying AI
technologies and optimizing production (see

Figure 4). These technologies, with their common

reliance on digitized data, lie at the heart of the
digital revolution in agriculture. As we chart future

research trajectories, it's imperative to focus on


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

24

https://www.theamericanjournals.com/index.php/tajabe

enabling technologies that facilitate the practical
application and dissemination of research

outcomes.

Figure 4. The top 10 technologies and phrases that emerged from the examination of 175

publications


CONCLUSIONS

This systematic literature review, conducted using

the PRISMA methodology, comprehensively

examines the application of artificial intelligence
(AI) technologies in agriculture. Analyzing 176

papers through bibliometric analysis and assessing
17 papers for quality, the review identifies seven

primary agricultural applications: crop, water, soil
management, fertigation, prediction, classification,

and disease/pest management. Notably, twenty-
four AI techniques, prominently including machine

learning, deep learning with convolutional neural
networks, robotics, and the Internet of Things, are

identified as pivotal in advancing agricultural
practices. These technologies offer substantial

benefits such as optimizing management systems,
irrigation, and disease identification. However,

challenges persist, particularly in digitizing

production processes and addressing the
associated costs, which remain prohibitive for

many farmers. Labor qualification and the need for
supportive public policies in food-producing

nations are also highlighted. Moreover, the

integration of computer vision with robotics and
UAVs for crop classification and disease detection,

alongside emerging technologies like digital twins,
signifies promising avenues for agricultural

optimization. As precision agriculture transitions
towards smart farming frameworks, incorporating

telecommunications and data infrastructure, the
advent of agriculture 4.0 and 5.0 further

underscores the evolving landscape driven by AI
and UAV technologies. Despite these insights, the

review acknowledges limitations such as language
bias and database selection, suggesting avenues for

future research to enhance the comprehensiveness

and validity of findings.

REFERENCES

1.

D. Aminetzah et al., "A reflection on global food

security challenges amid the war in Ukraine and

the early impact of climate change," McKinsey’s

Agriculture Practice, 2022.

2.

S. Shiratori, Y. Tobita, and E. M. Sawadogo-

Compaoré, "Food Security, Nutritional Supply,

and Nutrient Sources in Rural Burkina Faso,"


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

25

https://www.theamericanjournals.com/index.php/tajabe

Nutrients, vol. 15, no. 10, p. 2285, 2023, doi:
10.3390/nu15102285

3.

N. Alexandratos and J. Bruinsma, "World

agriculture towards 2030/2050: the 2012

revision," 2012, doi: 10.22004/ag.econ.288998.

4.

B. A. King, T. Hammond, and J. Harrington,

"Disruptive technology: Economic consequences

of artificial intelligence and the robotics
revolution," Journal of Strategic Innovation and

Sustainability, vol. 12, no. 2, pp. 53-67, 2017.

5.

F. S. Aditto et al., "Fresh, mechanical and

microstructural behaviour of high-strength self-
compacting concrete using supplementary

cementitious materials," Case Studies in
Construction Materials, vol. 19, p. e02395, 2023.

6.

J. A. Jabin, M. T. H. Khondoker, M. H. R. Sobuz, and

F. S. Aditto, "High-temperature effect on the
mechanical behavior of recycled fiber-

reinforced concrete containing volcanic pumice

powder: An experimental assessment combined
with machine learning (ML)-based prediction,"

Construction and Building Materials, vol. 418, p.
135362,

2024/03/08/

2024,

doi:

https://doi.org/10.1016/j.conbuildmat.2024.1
35362.

7.

M. H. R. Sobuz et al., "Optimization of recycled

rubber self-compacting concrete: Experimental
findings

and

machine

learning-based

evaluation," Heliyon, vol. 10, no. 6, 2024, doi:

https://doi.org/10.1016/j.heliyon.2024.e2779
3.

8.

J. L. Ruiz-Real, J. Uribe-Toril, J. A. Torres Arriaza,

and J. de Pablo Valenciano, "A look at the past,
present and future research trends of artificial

intelligence in agriculture," Agronomy, vol. 10,
no.

11,

p.

1839,

2020,

doi:

10.3390/agronomy10111839

9.

K. Jha, A. Doshi, P. Patel, and M. Shah, "A

comprehensive review on automation in
agriculture

using

artificial

intelligence,"

Artificial Intelligence in Agriculture, vol. 2, pp. 1-
12,

2019/06/01/

2019,

doi:

https://doi.org/10.1016/j.aiia.2019.05.004.

10.

B. B. Nair and V. Mohandas, "Artificial

intelligence

applications

in

financial

forecasting

a survey and some empirical

results," Intelligent Decision Technologies, vol.

9, no. 2, pp. 99-140, 2015, doi: 10.3233/IDT-
140211.

11.

K. Bryceson and G. Slaughter, "Integrated

Autonomy A Modeling-Based Investigation of

Agrifood Supply Chain Performance," in 2009
11th International Conference on Computer

Modelling and Simulation, 2009: IEEE, pp. 334-
339, doi: 10.1109/UKSIM.2009.42.

12.

C. Siegert, D. Leathers, and D. Levia, "Synoptic

typing: interdisciplinary application methods
with three practical hydroclimatological

examples," Theoretical and Applied Climatology,

vol.

128,

pp.

603-621,

2017,

doi:

https://doi.org/10.1007/s00704-015-1700-y.

13.

I. N. Kosovic, T. Mastelic, and D. Ivankovic, "Using

Artificial Intelligence on environmental data
from Internet of Things for estimating solar

radiation: Comprehensive analysis," Journal of
Cleaner Production, vol. 266, p. 121489,

2020/09/01/

2020,

doi:

https://doi.org/10.1016/j.jclepro.2020.121489

.

14.

G. Ren, T. Lin, Y. Ying, G. Chowdhary, and K. C.

Ting, "Agricultural robotics research applicable
to poultry production: A review," Computers

and Electronics in Agriculture, vol. 169, p.
105216,

2020/02/01/

2020,

doi:

https://doi.org/10.1016/j.compag.2020.10521
6.

15.

S. Fountas, N. Mylonas, I. Malounas, E. Rodias, C.

Hellmann Santos, and E. Pekkeriet, "Agricultural

robotics for field operations," Sensors, vol. 20,
no. 9, p. 2672, 2020, doi: 10.3390/s20092672

16.

J. Lowenberg-DeBoer, I. Y. Huang, V. Grigoriadis,

and S. Blackmore, "Economics of robots and
automation in field crop production," Precision

Agriculture, vol. 21, no. 2, pp. 278-299, 2020, doi:

https://doi.org/10.1007/s11119-019-09667-5.

17.

D. C. Rose, R. Wheeler, M. Winter, M. Lobley, and

C.-A. Chivers, "Agriculture 4.0: Making it work

for people, production, and the planet," Land Use
Policy, vol. 100, p. 104933, 2021/01/01/ 2021,

doi:
https://doi.org/10.1016/j.landusepol.2020.104


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)

VOLUME 06 ISSUE07

26

https://www.theamericanjournals.com/index.php/tajabe

933.

18.

Z. Zhai, J. F. Martínez, V. Beltran, and N. L.

Martínez, "Decision support systems for

agriculture 4.0: Survey and challenges,"

Computers and Electronics in Agriculture, vol.
170, p. 105256, 2020/03/01/ 2020, doi:

https://doi.org/10.1016/j.compag.2020.10525
6.

19.

S. K. Roy and D. De, "Genetic Algorithm based

Internet of Precision Agricultural Things (IopaT)
for Agriculture 4.0," Internet of Things, vol. 18, p.

100201,

2022/05/01/

2022,

doi:

https://doi.org/10.1016/j.iot.2020.100201.

20.

M. Ryan, "Agricultural big data analytics and the

ethics of power," Journal of Agricultural and

Environmental Ethics, vol. 33, pp. 49-69, 2020,
doi:

https://doi.org/10.1007/s10806-019-

09812-0.

21.

M. Mokarram and M. R. Khosravi, "A cloud

computing

framework

for

analysis

of

agricultural big data based on Dempster

Shafer

theory," The Journal of Supercomputing, vol. 77,
pp.

2545-2565,

2021,

doi:

https://doi.org/10.1007/s11227-020-03366-z.

22.

Y. Gu, "Global knowledge management research:

A bibliometric analysis," Scientometrics, vol. 61,

pp.

171-190,

2004,

doi:

https://doi.org/10.1023/B:SCIE.0000041647.0

1086.f4.

23.

M. J. Cobo, M. A. Martínez, M. Gutiérrez-Salcedo,

H. Fujita, and E. Herrera-Viedma, "25years at
Knowledge-Based Systems: A bibliometric

analysis," Knowledge-Based Systems, vol. 80, pp.
3-13,

2015/05/01/

2015,

doi:

https://doi.org/10.1016/j.knosys.2014.12.035.

24.

T. van Klompenburg, A. Kassahun, and C. Catal,

"Crop yield prediction using machine learning: A
systematic literature review," Computers and

Electronics in Agriculture, vol. 177, p. 105709,
2020/10/01/

2020,

doi:

https://doi.org/10.1016/j.compag.2020.10570
9.

25.

Y. Yuan, L. Chen, H. Wu, and L. Li, "Advanced

agricultural

disease

image

recognition

technologies: A review," Information Processing

in Agriculture, vol. 9, no. 1, pp. 48-59,
2022/03/01/

2022,

doi:

https://doi.org/10.1016/j.inpa.2021.01.003.

26.

M. S. Farooq, S. Riaz, A. Abid, T. Umer, and Y. B.

Zikria, "Role of IoT technology in agriculture: A
systematic literature review," Electronics, vol. 9,

no.

2,

p.

319,

2020,

doi:

10.3390/electronics9020319

27.

S. Wolfert, L. Ge, C. Verdouw, and M.-J. Bogaardt,

"Big Data in Smart Farming

A review,"

Agricultural Systems, vol. 153, pp. 69-80,

2017/05/01/

2017,

doi:

https://doi.org/10.1016/j.agsy.2017.01.023.

28.

F. Maffezzoli, M. Ardolino, A. Bacchetti, M.

Perona, and F. Renga, "Agriculture 4.0: A

systematic literature review on the paradigm,
technologies and benefits," Futures, vol. 142, p.

102998,

2022/09/01/

2022,

doi:

https://doi.org/10.1016/j.futures.2022.102998

.

29.

S. Araújo, R. Peres, J. Barata, F. Lidon, and J.

Ramalho, "Characterising the Agriculture 4.0
Landscape

Emerging Trends, Challenges and

Opportunities. Agronomy 2021, 11, 667," ed,
2022.

30.

H. A. M. Williams et al., "Robotic kiwifruit

harvesting using machine vision, convolutional
neural networks, and robotic arms," Biosystems

Engineering,

vol.

181,

pp.

140-156,

2019/05/01/

2019,

doi:

https://doi.org/10.1016/j.biosystemseng.2019.

03.007.

31.

N. Lin, X. Wang, Y. Zhang, X. Hu, and J. Ruan,

"Fertigation management for sustainable

precision agriculture based on Internet of
Things," Journal of Cleaner Production, vol. 277,

p.

124119,

2020/12/20/

2020,

doi:

https://doi.org/10.1016/j.jclepro.2020.124119

.

32.

M. M. Reis, A. J. da Silva, J. Zullo Junior, L. D. Tuffi

Santos, A. M. Azevedo, and É. M. G. Lopes,
"Empirical and learning machine approaches to

estimating reference evapotranspiration based
on temperature data," Computers and

Electronics in Agriculture, vol. 165, p. 104937,
2019/10/01/

2019,

doi:


background image

THE USA JOURNALS

THE AMERICAN JOURNAL OF AGRICULTURE AND BIOMEDICAL ENGINEERING (ISSN

2689-1018)