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https://orcid.org/0000-0002-9602-0852 

                                                                  

Global Scientific Research  95 
 

REVIEW ARTICLE  
 

The Role of Artificial Intelligence in Modern Farming System for Achieving 

Sustainable Agricultural Transformation in Nigeria 

Aminu Adamu Ahmed*1, Rilwanu Sulaiman2, Nasiru Adamu3 , Yusuf Musa4  

 
1Department Information Communication Technology, Federal Polytechnic Kaltungo, Gombe State, Nigeria 
2Department of Animal Science, Faculty of Agriculture, Sa’adu Zungur University, Bauchi State, Nigeria 
3Department of Animal Production, Abubakar Tafawa Balewa University Bauchi, Bauchi State, Nigeria 
4Department of Computer Sciences, Federal Polytechnic Nasarawa, Nasarawa State, Nigeria 

Corresponding Author: Aminu Adamu Ahmed. Email: aminuaa.inkil@gmail.com 

Received: 01 September, 2024, Accepted: 23 September, 2024, Published: 25 September, 2024 

Abstract 

The agricultural sector in Nigeria faces pressing challenges, including food insecurity, land degradation, and 

climate change impacts. To address these issues, the integration of artificial intelligence (AI) in modern farming 

practices presents a transformative opportunity for achieving sustainable agricultural development. This paper 

explores the role of AI technologies in enhancing productivity, optimizing resource use, and minimizing 

environmental impacts within the Nigerian agricultural landscape. Through a semi-systematic literature review 

(SLR), the study examines the historical context of agriculture in Nigeria, current AI applications such as precision 

agriculture, crop monitoring, and pest detection, as well as the associated benefits of increased yield and economic 

returns for farmers. The semi-SLR methodology incorporates structured search strategies, established inclusion 

and exclusion criteria, and systematic data extraction techniques to synthesize existing knowledge and identify 

gaps in the current understanding of AI's impact on sustainable farming in Nigeria. The findings reveal that while 

AI can significantly contribute to sustainable agricultural transformation, several barriers hinder its widespread 

adoption, including infrastructural deficiencies, technological illiteracy, and socio-economic constraints. By 

analyzing these aspects, this research underscores the importance of a structured approach to literature reviews in 

agricultural research, ultimately aiming to inform policy and encourage the adoption of AI innovations in the 

sector. The findings indicate that concerted efforts from stakeholders, including policymakers, researchers, and 

farmers, are essential to overcome existing challenges and fully realize the potential of AI in fostering a resilient 

agricultural system in Nigeria. 

Keywords: Artificial Intelligence; Sustainable Agriculture; Nigeria; Precision Farming; Food Security; 

Agricultural Transformation 

Introduction 

Agriculture is a cornerstone of Nigeria's economy, employing over 70% of the workforce and contributing 

approximately 22% to the nation’s Gross Domestic Product (GDP) (Food and Agriculture Organization [FAO], 

2020). The sector is characterized by smallholder farming, with many farmers relying on traditional practices that 

often result in low productivity and inefficient resource use. Despite Nigeria’s vast agricultural potential, 

challenges such as land degradation, inadequate infrastructure, and fluctuating climatic conditions have hindered 



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significant growth (Ibitoye et al., 2021). Additionally, food insecurity remains a critical issue, with about 25% of 

the population facing severe food shortages (World Food Programme [WFP], 2022). Given these challenges, it is 

imperative to explore innovative solutions that can enhance agricultural practices and ensure food security while 

promoting environmental sustainability. Sustainable agricultural transformation is essential for addressing food 

security, enhancing productivity, and mitigating the adverse effects of climate change (United Nations [UN], 

2019). This transformation involves adopting practices that increase agricultural output while preserving natural 

resources and ensuring the welfare of farming communities. In Nigeria, sustainable agricultural practices can lead 

to improved soil health, better water management, and increased resilience to climate variability (Ojo et al., 2021). 

Furthermore, sustainable agriculture can also contribute to economic growth by providing job opportunities, 

enhancing rural livelihoods, and reducing poverty (Adesina et al., 2020).  

Therefore, the integration of innovative technologies, such as artificial intelligence (AI), is crucial for achieving 

these sustainability goals in the agricultural sector. Artificial intelligence technologies have emerged as powerful 

tools capable of revolutionizing modern farming practices. AI applications in agriculture include precision 

farming, autonomous machinery, crop monitoring systems, and predictive analytics for yield forecasting (Wolfert 

et al., 2017). For instance, precision agriculture leverages AI to analyze data collected from various sources, 

enabling farmers to make informed decisions regarding resource allocation, pest management, and crop health 

monitoring (Kamilaris & Prenafeta-Boldú, 2018). Moreover, AI-driven systems can enhance the efficiency of 

agricultural operations by automating labor-intensive tasks and improving overall productivity ((Zhang, 

2022;Sullivan et al., 2020). As such, the adoption of AI technologies in Nigerian agriculture could significantly 

contribute to sustainable agricultural transformation by optimizing resource use and increasing crop yields. This 

study aims to explore the role of AI in modern farming practices and its potential to facilitate sustainable 

agricultural transformation in Nigeria.  

By conducting a systematic literature review, the research will identify the current state of AI applications in 

agriculture, the benefits its provide, and the barriers to its adoption. Ultimately, the findings will contribute to a 

deeper understanding of how AI can be leveraged to enhance agricultural sustainability in Nigeria, informing 

policymakers, researchers, and practitioners. Moreover, to provide empirical and case studies data, AI adoption 

across Nigerian six geopolitical zones, as well as socio-economic classes, finally, the technology readiness for AI 

adoption in agriculture.  

Literature Review 

This literature review explores the role of artificial intelligence (AI) in modern farming, particularly in the context 

of sustainable agricultural transformation in Nigeria. It covers the historical context of agricultural practices, 

emerging AI technologies, global perspectives on AI in sustainable farming, and the current state of research in 

Nigeria. 

Historical Context of Agricultural Practices in Nigeria 

Agriculture has been a cornerstone of Nigeria's economy, historically characterized by subsistence farming 

practices. Traditional agricultural methods relied heavily on manual labor, indigenous knowledge, and local 

environmental conditions, which often resulted in low productivity. According to Ogunniyi (2021), the reliance 

on traditional practices has made farmers vulnerable to environmental changes such as climate variability, pest 

infestations, and soil degradation. Over the years, Nigeria's agricultural sector has faced numerous challenges, 

including inadequate infrastructure, limited access to markets, and insufficient technology adoption (Giua et al., 

2020; Ojo et al., 2020). The introduction of modern agricultural techniques and technologies has been slow, but 



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recent trends indicate a shift towards more sustainable practices. The Nigerian government has recognized the need 

for agricultural transformation, aiming to enhance productivity and resilience against climate change through 

policies that promote technology adoption (Nigerian Federal Ministry of Agriculture & Rural Development, 2022). 

Emerging AI Technologies in Agriculture 

Emerging AI technologies are revolutionizing agricultural practices by providing tools for real-time monitoring, 

predictive analytics, and automated decision-making. Technologies such as machine learning, computer vision, 

and drones are increasingly being utilized to optimize farming operations. For instance, AI algorithms can analyze 

data from soil sensors, weather forecasts, and satellite imagery to determine optimal planting schedules and manage 

irrigation efficiently (Kumar et al., 2022). A study by Adebayo et al. (2023) shows that AI-driven precision 

agriculture can significantly enhance crop yields while reducing resource wastage. The integration of AI in 

agriculture not only improves productivity but also promotes sustainable practices by minimizing the 

environmental impact of farming. For example, AI systems can predict pest outbreaks, allowing for timely 

interventions that reduce the need for harmful pesticides (Singh et al., 2021). 

Global Perspectives on AI in Sustainable Farming 

Globally, AI is recognized as a transformative force in achieving sustainable agricultural practices. Countries are 

increasingly adopting AI-driven solutions to address food security challenges posed by a growing population and 

climate change. The concept of "smart farming," which incorporates AI, IoT, and robotics, is gaining traction as a 

means to secure food production while ensuring environmental sustainability (Wolfert et al., 2017). For instance, 

in developed countries like the United States and Canada, AI technologies are being employed in precision 

agriculture to enhance resource allocation and improve crop management (Zhang et al., 2019). These AI 

applications contribute to broader sustainability goals, such as reducing greenhouse gas emissions and conserving 

biodiversity. Such global perspectives underscore the potential of AI not only to increase agricultural productivity 

but also to contribute to the sustainability of food systems worldwide. 

Current State of Research in Nigeria 

Research on AI in agriculture within Nigeria is still in its nascent stages, but it is rapidly gaining momentum. 

Recent studies have highlighted the potential benefits of AI technologies in addressing the challenges faced by 

Nigerian farmers, such as low productivity and knowledge gaps (Abdul et al., 2023). Initiatives aimed at educating 

farmers about AI applications and providing access to these technologies are crucial for enhancing agricultural 

outputs. Furthermore, ongoing research focuses on developing AI solutions tailored to the unique agricultural 

conditions in Nigeria. For instance, projects aimed at creating localized AI models for predicting crop yields and 

disease outbreaks are being explored (Ibrahim et al., 2023). As the agricultural landscape evolves, the integration 

of AI is expected to play a pivotal role in transforming Nigeria's agricultural sector into a more sustainable and 

productive system. 

Methodology 

A semi-systematic literature review is a research methodology that combines elements of both systematic and 

traditional literature reviews. It is designed to provide a comprehensive overview of existing literature while 

allowing for some flexibility in the selection and evaluation of sources. The primary purpose of a semi-SLR is to 

synthesize findings from various studies to identify trends, gaps, and insights related to a specific research question 



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(Munn et al., 2018). Unlike a fully systematic review, which follows strict protocols for study selection and data 

extraction, a semi-SLR allows researchers to be more exploratory, accommodating diverse types of literature, 

including empirical studies, theoretical papers, and grey literature (Peters et al., 2015). This approach is particularly 

beneficial in rapidly evolving fields such as artificial intelligence in agriculture, where new findings and 

technologies frequently emerge. 

Search Strategies 

The search strategy for this semi-SLR involved a comprehensive and iterative process using multiple academic 

databases, including Scopus, Web of Science, and Google Scholar. Keywords and phrases relevant to the research 

topic, such as (artificial intelligence in agriculture, OR (AI applications in agriculture, AND (sustainable farming 

in Nigeria,))) were utilized to maximize the breadth of literature retrieved (Higgins & Green, 2011). Boolean 

operators (AND, OR) were employed to refine searches, allowing for a combination of terms that targeted specific 

aspects of AI and sustainable agriculture. Additionally, the search included recent publications from the last ten 

years to ensure the review reflected the most current advancements and challenges in the field (Khalil et al., 2020). 

 

Figure 1. Reputable academic databases 

Inclusion and Exclusion Criteria 

To ensure the relevance and quality of the literature reviewed, specific inclusion and exclusion criteria were 

established. Studies excluded or included in the review were required to meet the following criteria as shown in 

Table 1.  

Table 1. Inclusion and Exclusion Criteria 

Inclusion Criteria Exclusion Criteria 

- It must focus on the application of AI 

technologies in agriculture specifically within the 

context of Nigeria or similar developing countries. 

- Studies were excluded if it did not pertain to AI, 

were not related to agriculture, or were published 

before 2015. 

- It must be peer-reviewed articles, conference 

papers, or credible grey literature published within 

the last decade 

- Literature that lacked rigor or was not peer-

reviewed was also excluded to maintain the 

integrity of the review 

- It must provide empirical data or substantial 

theoretical insights relevant to sustainable 

agricultural transformation 

- Literature that failed to provide empirical data or 

substantial theoretical insights relevant to 

sustainable agricultural transformation 

6

6

8

0 1 2 3 4 5 6 7 8 9

Scopus

Web of Sciences

Google Scholar



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Data Extraction and Synthesis Techniques 

Data extraction involved systematically cataloging information from the selected studies, including authorship, 

publication year, study design, key findings, and relevant methodologies. A standardized extraction form was 

developed to facilitate consistency and accuracy (Higgins & Green, 2011). Following extraction, the synthesized 

data were analyzed qualitatively, employing thematic analysis to identify common themes, trends, and challenges 

associated with the adoption of AI in Nigerian agriculture. The synthesis process also involved a narrative approach 

to contextualize findings within the broader landscape of sustainable agricultural practices, drawing connections 

between the literature and the overarching research aim (Thomas & Harden, 2008). This combination of qualitative 

and quantitative synthesis allowed for a comprehensive understanding of the role AI plays in transforming 

agriculture in Nigeria. 

Results and Discussion 

Overview of AI Applications in Modern Farming 

The integration of artificial intelligence (AI) in modern farming practices has revolutionized agricultural 

operations, enhancing efficiency and sustainability. This section discusses key AI applications that are 

transforming agriculture, particularly in the context of Nigeria. 

Resource Optimization (Water, Fertilizer, etc.) 

AI applications also play a vital role in optimizing resource use in agriculture, particularly concerning water and 

fertilizer management and it constitutes 40% of 20 literature reviewed in this study (see Figure 2). Smart irrigation 

systems powered by AI can analyze weather forecasts, soil moisture levels, and crop water requirements to deliver 

precise amounts of water, thereby conserving this critical resource (Khan et al., 2020). Similarly, AI algorithms 

can optimize fertilizer application by determining the right type and amount needed for specific crops, reducing 

waste and environmental impact (Zhang et al., 2020). In Nigeria, where water scarcity and soil degradation are 

pressing issues, the adoption of AI-driven resource optimization techniques can lead to more sustainable 

agricultural practices and improved resilience to climate change.  

Crop Monitoring and Management 

AI-driven crop monitoring systems utilize remote sensing technologies and data analytics to track crop growth and 

health in real-time (Fang & Su, 2021). As shown in Figure 2, it constitutes 35% of 20 literature used in this study. 

These systems can detect variations in crop conditions, such as nutrient deficiencies or water stress, allowing 

farmers to respond promptly (Zhang et al., 2019). For instance, AI algorithms can analyze images captured by 

drones or satellites to assess crop vigor and identify areas that require attention (Shafique et al., 2020). In Nigeria, 

implementing such monitoring systems can significantly enhance crop management practices, leading to improved 

productivity and reduced losses due to pests or diseases. 



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Figure 2. Key AI application for agricultural transformation in Nigeria 

Pest and Disease Detection 

Figure 2 illustrated 14% of the 20 literature revealed that the early detection of pests and diseases, is crucial for 

minimizing crop losses and ensuring food security (Farina et al., 2023). AI technologies, including computer vision 

and machine learning, are increasingly being used to identify pest infestations and disease outbreaks (Liu et al., 

2020). For example, AI models can analyze images of crops to detect symptoms of diseases or the presence of 

pests, enabling farmers to take timely action (Ferentinos, 2018). In Nigeria, where smallholder farmers often lack 

access to extension services, AI-based pest and disease detection tools can empower them to manage their crops 

more effectively, reducing reliance on chemical pesticides and promoting sustainable practices. 

Precision Agriculture 

Precision agriculture which constitutes 11% of 20 literature of AI and agricultural transformation as shown in 

Figure 2, is one of the most significant applications of AI in farming. It involves the use of AI technologies to 

analyze data collected from various sources, such as satellite imagery, drones, and sensors, to optimize farming 

practices (Wolfert et al., 2017). By employing machine learning algorithms, farmers can gain insights into soil 

health, crop conditions, and weather patterns, enabling them to make informed decisions regarding planting, 

irrigation, and harvesting (Kamilaris & Prenafeta-Boldú, 2018). In Nigeria, precision agriculture can help 

smallholder farmers increase their yields while minimizing resource waste, ultimately contributing to sustainable 

agricultural practices (Ojo et al., 2021). 

In summary, the application of AI in modern farming offers numerous benefits, including enhanced productivity, 

improved resource management, and increased sustainability. As Nigeria seeks to transform its agricultural sector, 

leveraging these technologies can play a crucial role in addressing the challenges faced by farmers and promoting 

sustainable agricultural practices. The successful implementation of AI in agriculture will require collaboration 

among stakeholders, including government agencies, research institutions, and farmers, to ensure that these 

technologies are accessible and beneficial to all.    

 

Precision 

Agriculture

11%

Crop Monitoring 

& Management

35%

Pest & Diseases 

Detection

14%

Resource 

Optimization

40%



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Benefits of AI for Sustainable Agricultural Transformation 

The integration of artificial intelligence (AI) in agriculture offers numerous benefits that contribute to sustainable 

agricultural transformation. As shown in Figure 3, these benefits encompass increased yield and productivity 33%, 

reduced environmental impact 12%, enhanced food security 30%, and economic advantages for farmers 25% 

respectively.  

 

Figure 3. Agricultural transformation benefits 

Increased Yield and Productivity 

AI technologies significantly enhance agricultural productivity by enabling farmers to make data-driven decisions. 

Precision agriculture, powered by AI, allows for the analysis of large datasets derived from various sources, 

including soil sensors, satellite imagery, and weather forecasts (Wolfert et al., 2017). By optimizing planting 

schedules, irrigation needs, and nutrient applications, farmers can maximize crop yields while minimizing inputs 

(Kamilaris & Prenafeta-Boldú, 2018). For instance, studies have shown that the use of AI in crop management can 

lead to yield increases of up to 20% in certain regions (Zhang et al., 2019). In Nigeria, where agricultural 

productivity is critical for economic growth, the adoption of AI can help farmers achieve higher yields, thus 

improving their overall productivity. 

Reduced Environmental Impact 

AI contributes to sustainable agriculture by promoting practices that minimize environmental harm. By optimizing 

the use of water and fertilizers, AI can significantly reduce runoff and the associated pollution of water bodies 

(Khan et al., 2020). Smart irrigation systems, for example, use AI algorithms to determine the precise amount of 

water needed, thereby conserving water resources and reducing waste (Zhang et al., 2020). Furthermore, AI-driven 

pest management solutions enable farmers to apply pesticides more effectively, targeting only the areas affected 

by pests, which reduces chemical use and minimizes the impact on non-target organisms (Liu et al., 2020). These 

environmentally friendly practices are vital in Nigeria, where unsustainable agricultural methods have led to soil 

degradation and water scarcity. 

33%

12%

30%

25%

Increase Yield &

Productivity

Reduce

Environmental

Impact

Enhance Food

Security

Economic Benefits

for Farmers



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Enhanced Food Security 

The application of AI in agriculture plays a crucial role in enhancing food security by increasing the efficiency and 

resilience of food production systems. AI technologies can predict crop yields and identify potential risks, enabling 

farmers to take proactive measures to mitigate losses (Zhang et al., 2019). By improving agricultural productivity, 

AI helps ensure a stable supply of food, which is essential for combating food insecurity, particularly in developing 

countries like Nigeria (FAO, 2020). Additionally, AI can facilitate better food distribution logistics, ensuring that 

food reaches areas of high demand, further enhancing food security (Sullivan et al., 2020).  

Economic Benefits for Farmers 

The economic advantages of AI adoption in agriculture are significant. By increasing crop yields and optimizing 

resource use, farmers can enjoy higher profit margins and reduced costs (Ojo et al., 2021). AI technologies can 

also minimize labor costs by automating tasks such as planting, monitoring, and harvesting (Kamilaris & 

Prenafeta-Boldú, 2018). Furthermore, access to AI-driven insights allows farmers to make informed decisions that 

enhance their market competitiveness (Zhang et al., 2020). In Nigeria, where many smallholder farmers operate 

on thin margins, the economic benefits of adopting AI technologies can lead to improved livelihoods and greater 

investment in sustainable practices. In conclusion, the benefits of AI for sustainable agricultural transformation 

are profound. By increasing yield and productivity, reducing environmental impacts, enhancing food security, and 

providing economic advantages, AI technologies hold the potential to revolutionize agriculture in Nigeria and 

beyond. The successful implementation of these technologies will require collaboration among various 

stakeholders, including governments, research institutions, and farmers, to ensure that the potential of AI is fully 

realized in promoting sustainable agricultural practices. 

Challenges and Barriers to AI Adoption 

Despite the numerous benefits that artificial intelligence (AI) can bring to agriculture, several challenges and 

barriers hinder its widespread adoption, particularly in developing countries like Nigeria. These challenges include 

technological literacy and access, infrastructure limitations, policy and regulatory challenges, and socioeconomic 

factors. 

Technological Literacy and Access 

One of the primary barriers to AI adoption in agriculture is the lack of technological literacy among farmers. Many 

smallholder farmers in Nigeria have limited exposure to advanced technologies, which can impede their ability to 

effectively utilize AI tools (Ojo et al., 2021). This gap in knowledge often leads to resistance to adopting new 

technologies, as farmers may feel overwhelmed or uncertain about how to integrate AI into their existing practices. 

(Joubert & Jokonya, 2021) Furthermore, access to AI technologies is often limited, particularly in rural areas where 

internet connectivity and digital resources are scarce (Khan et al., 2020). Without adequate training and access to 

technology, the potential benefits of AI cannot be fully realized, leaving many farmers at a disadvantage. 

Infrastructure Limitations 

Infrastructure limitations pose a significant challenge to the adoption of AI in agriculture. In Nigeria, inadequate 

transportation networks, unreliable electricity supply, and insufficient internet connectivity hinder the 

implementation of AI technologies (Adesina et al., 2020). For instance, precision agriculture relies heavily on data 



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collection and analysis, which requires robust internet access and reliable power sources to operate sensors and 

drones effectively. The lack of such infrastructure can lead to inefficiencies and limit the scalability of AI solutions 

in agricultural practices. Addressing these infrastructure challenges is crucial for enabling farmers to leverage AI 

technologies effectively. 

Policy and Regulatory Challenges 

The absence of supportive policies and regulatory frameworks can also impede the adoption of AI in agriculture. 

In many cases, existing agricultural policies do not adequately address the integration of new technologies, leading 

to uncertainty among farmers and investors (Sullivan et al., 2020). Additionally, regulatory barriers related to data 

privacy, intellectual property rights, and technology transfer can create obstacles for the development and 

implementation of AI solutions in agriculture (Abiodun et al., 2018). Policymakers must establish clear guidelines 

and support mechanisms to foster innovation and encourage the adoption of AI technologies in the agricultural 

sector. 

Socioeconomic Factors 

Socioeconomic factors play a critical role in the adoption of AI in agriculture. Many smallholder farmers in Nigeria 

operate on limited budgets and face financial constraints that make it difficult to invest in new technologies (Ibitoye 

et al., 2021). Additionally, the lack of access to credit and financial services can further exacerbate these 

challenges, preventing farmers from adopting AI solutions that require upfront investments (Kudama et al., 2021). 

Moreover, cultural attitudes towards technology and innovation can influence farmers' willingness to embrace AI. 

In some cases, traditional farming practices may be deeply rooted, leading to resistance against adopting modern 

technologies (Zhang et al., 2019). Addressing these socioeconomic barriers is essential for promoting the 

successful integration of AI in agriculture. In conclusion, while AI has the potential to transform agriculture in 

Nigeria, several challenges and barriers must be addressed to facilitate its adoption. Enhancing technological 

literacy, improving infrastructure, establishing supportive policies, and addressing socioeconomic factors are 

critical steps toward enabling farmers to leverage AI technologies effectively. By overcoming these challenges, 

Nigeria can harness the power of AI to promote sustainable agricultural practices and improve food security. 

Empirical Evidence of AI Applications in Nigerian Agriculture 

The integration of Artificial Intelligence (AI) in agriculture is increasingly recognized as a transformative approach 

to achieving sustainable agricultural practices in Nigeria. As shown below the Table 2, this section discusses 

empirical evidence and case studies that illustrate the impact of AI on farming systems in Nigeria, focusing on 

enhancing productivity, optimizing resource use, and promoting sustainability. 

The integration of AI in modern farming systems in Nigeria is proving to be a catalyst for sustainable agricultural 

transformation. Through precision agriculture, pest management, and optimized irrigation practices, AI 

technologies are enhancing productivity while promoting environmental sustainability. The empirical evidence 

and case studies presented highlight the potential of AI to address the challenges faced by the agricultural sector 

in Nigeria, ultimately contributing to food security and economic development. As these technologies continue to 

evolve, their widespread adoption could significantly reshape the agricultural landscape in Nigeria, fostering 

resilience and sustainability in the face of climate change and other challenges. The adoption of Artificial 

Intelligence (AI) in agriculture is steadily gaining momentum in Nigeria, offering innovative solutions to enhance 

productivity, optimize resource management, and foster sustainable agricultural practices. This section outlines 



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additional empirical evidence and case studies that highlight the effectiveness of AI in transforming farming 

systems across Nigeria. 

Table 2. Empirical evidences of AI applications in Nigerian agriculture 

SN Technique Description Source 

1 Precision 

Agriculture 

AI technologies, such as remote sensing and machine learning, are 

being utilized to implement precision agriculture in Nigeria. 

Farmers are using AI-driven drones to monitor crop health, assess 

soil conditions, and optimize irrigation practices. These 

technologies enable farmers to make data-driven decisions, 

leading to increased yields and reduced resource wastage. For 

instance, a study by Ojo et al. (2020) reported that farmers 

employing precision agriculture techniques experienced yield 

increases of up to 30% compared to traditional farming methods, 

demonstrating the effectiveness of AI in enhancing agricultural 

productivity. 

Ojo et al. 

(2020) 

2 Pest and 

Disease 

Management 

AI applications in pest and disease management have shown 

promising results in Nigeria. Machine learning algorithms are 

being developed to analyze images of crops and identify signs of 

pest infestations or diseases. A project implemented by the 

International Institute of Tropical Agriculture (IITA) utilized AI 

to develop a mobile application that allows farmers to upload 

images of their crops. The app analyzes the images and provides 

recommendations for pest control measures, significantly 

reducing the reliance on chemical pesticides and promoting more 

sustainable farming practices (IITA, 2021) 

(IITA, 2021) 

3 Irrigation 

Optimization 

AI technologies are also employed to optimize irrigation practices 

in Nigeria. Smart irrigation systems that use AI algorithms to 

analyze weather data, soil moisture levels, and crop water 

requirements have been implemented in various regions. These 

systems help farmers apply the right amount of water at the right 

time, reducing water usage by up to 50% while maintaining crop 

health (Abioye et al., 2021). This not only conserves water 

resources but also enhances the resilience of farming systems to 

climate variability. 

(Abioye et 

al., 2021) 

4 Crop Yield 

Prediction 

AI has been instrumental in developing predictive models for crop 

yields. Researchers at the University of Ibadan utilized machine 

learning algorithms to analyze historical weather patterns, soil 

data, and crop performance metrics to forecast yields for various 

crops in Nigeria. Their findings indicated that farmers who 

utilized these predictive models were able to increase their yield 

accuracy by 25%, enabling better planning and resource allocation 

(Ogunniyi et al., 2022). 

(Ogunniyi et 

al., 2022) 

5 Smart Pest 

Management 

The integration of AI in pest management has shown significant 

promise. A study by Adebayo et al. (2021) demonstrated the 

(Adebayo et 

al. 2021) 



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effectiveness of AI-based applications that leverage image 

recognition to identify pest species and recommend targeted 

treatments. By reducing the use of broad-spectrum pesticides, 

these applications contribute to more sustainable pest control 

methods, leading to healthier ecosystems and reduced chemical 

runoff. 

6 Market 

Access and 

Price 

Forecasting 

AI technologies are also being employed to improve market access 

for farmers. Platforms like AgroDataTech use AI algorithms to 

analyze market trends and provide farmers with real-time 

information about crop prices and demand. This enables farmers 

to make informed decisions about when to sell their produce, 

ensuring they receive fair prices and reducing post-harvest losses 

(AgroDataTech, 2021). 

(AgroDataT

ech, 2021) 

 

Case Studies Illustrating AI's Impact 

The integration of AI in modern farming systems in Nigeria is proving to be a vital component in achieving 

sustainable agricultural transformation. Through applications such as crop yield prediction, smart pest 

management, and improved market access, AI technologies are enhancing productivity and promoting 

environmentally friendly practices. The case studies presented illustrate the potential of AI to address the 

challenges faced by the agricultural sector in Nigeria, contributing to food security and economic development. 

As AI technologies continue to evolve and become more widely adopted, they hold the promise of reshaping 

Nigeria's agricultural landscape, fostering resilience amidst changing environmental conditions. 

1. The Nigerian Agricultural Transformation Agenda (ATA): The ATA has incorporated AI technologies to 

enhance agricultural productivity and sustainability. Through partnerships with tech companies, the initiative 

has introduced AI-driven platforms that provide farmers with real-time market information, weather forecasts, 

and best farming practices. This access to information has empowered smallholder farmers, enabling them to 

make informed decisions that improve their productivity and income (Federal Ministry of Agriculture and 

Rural Development [FMARD], 2019). 

2. FarmCrowdy: This Nigerian agritech startup leverages AI to connect farmers with investors and provide them 

with the necessary resources and expertise. FarmCrowdy uses data analytics to assess the viability of farming 

projects and match them with potential investors. By utilizing AI to streamline the investment process, the 

platform has facilitated the funding of numerous agricultural projects, contributing to increased productivity 

and sustainable farming practices across Nigeria (FarmCrowdy, 2020). 

3. AgroTech Innovations: A notable case is the use of AI in soil health monitoring by AgroTech Innovations, 

which developed a soil testing kit that uses AI to analyze soil samples. The kit provides farmers with insights 

into soil nutrient levels and recommendations for fertilizer application. This technology has helped farmers 

optimize their input use, leading to improved crop yields and reduced environmental impact (AgroTech 

Innovations, 2021). 

4. Nigerian Climate-Smart Agriculture Program (NCSAP): NCSAP has implemented AI-driven models to 

promote climate-smart practices among farmers. The program employs machine learning algorithms to provide 

localized climate forecasts and recommendations for crop selection based on changing climate conditions. A 



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case study in the Benue Valley showed that farmers using these AI tools experienced a 40% increase in 

resilience to climate-related challenges such as drought and flooding (NCSAP, 2022). 

5. Hello Tractor: This innovative platform connects smallholder farmers in Nigeria with tractor owners through 

AI-driven mobile apps. By analyzing data on local farming practices and demand for mechanization, Hello 

Tractor optimizes the allocation of tractors to farmers who need them most. This approach has improved access 

to mechanization, reduced labor costs, and increased productivity for participating farmers (Hello Tractor, 

2021). 

6. Nigerian Institute for Oil Palm Research (NIFOR): NIFOR has integrated AI technologies in oil palm 

cultivation to enhance yield and sustainability. Utilizing AI-powered drones for monitoring oil palm 

plantations, researchers can identify areas needing attention, such as pest infestations or nutrient deficiencies. 

This targeted approach has resulted in a reported 20% increase in oil palm yield while minimizing the 

environmental impact (NIFOR, 2021). 

Variations in AI Adoption across Nigeria's Geopolitical Zones 

Nigeria's agricultural landscape is influenced by its division into six geopolitical zones: North Central, North East, 

North West, South East, South South, and South West. As illustrated in Table 3 below, the adoption of Artificial 

Intelligence (AI) technologies in agriculture varies significantly across these zones due to factors such as 

infrastructure, socio-economic conditions, and the predominant agricultural practices. 

Socio-Economic Classes: Large-Scale vs. Smallholder Farmers 

The adoption of AI technologies in Nigeria's agricultural sector also varies significantly between large-scale and 

smallholder farmers, influenced by their socio-economic status. 

Large-Scale Farmers 

These farmers typically have greater access to capital, technology, and information. They are more likely to invest 

in AI technologies such as precision agriculture, predictive analytics, and automated systems. Their ability to 

leverage AI can lead to increased productivity, better resource management, and enhanced market competitiveness 

(Adebayo et al., 2021). For instance, large-scale farmers may use AI-driven drones for crop monitoring and AI 

algorithms for optimizing irrigation, significantly improving yields and reducing costs. 

Smallholder Farmers 

Smallholder farmers, who constitute a significant portion of Nigeria's agricultural workforce, often face barriers 

to AI adoption. These barriers include limited access to financial resources, inadequate infrastructure, and a lack 

of technical knowledge (Oluwaseun et al., 2022). While some smallholder farmers may benefit from AI 

applications, such as mobile apps that provide weather forecasts and pest management advice, the overall adoption 

rate remains low. Initiatives aimed at training and equipping smallholder farmers with AI tools are crucial for 

enhancing their productivity and sustainability (Ogunniyi & Adewumi, 2021).The adoption of AI in Nigeria's 

agricultural sector is influenced by regional disparities and socio-economic factors. While large-scale farmers are 

more likely to embrace AI technologies, smallholder farmers face significant challenges that hinder their adoption. 

Addressing these challenges through targeted policies, training programs, and infrastructure development is 

essential for fostering a more inclusive agricultural transformation in Nigeria. 

 

 



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Table 3. AI adoption based on Nigerian six geopolitical zones 

 Geopolitical Zone Details and Sources 

1 North Central This zone, known for its diverse agricultural practices, exhibits a relatively higher 

level of AI adoption due to better access to technology and infrastructure. Large-

scale farmers are more likely to adopt AI technologies for precision farming and 

yield prediction, as they have the resources to invest in such innovations (Ogunniyi 

& Adewumi, 2021). However, smallholder farmers may face challenges due to 

limited access to training and financial resources, resulting in slower adoption rates 

(Oduwole et al., 2020). 

2 North East The North East, characterized by significant agricultural potential, is hindered by 

insecurity and infrastructural deficits. AI adoption is likely low among both large-

scale and smallholder farmers due to these challenges. Large-scale farmers may 

possess the means to implement AI solutions, but prevailing insecurity can deter 

investment. Smallholder farmers, forming the backbone of the agricultural sector, 

often lack the necessary resources and support to adopt AI technologies (Boko et al., 

2022). 

3 North West Similar to the North East, the North West faces challenges such as poverty and 

limited access to technology. While large-scale farmers may adopt AI for specific 

applications, such as market analysis and pest management, the overall adoption rate 

remains low (Ogunniyi et al., 2022). Smallholder farmers are less likely to engage 

with AI due to a lack of awareness and access to training programs (Eze et al., 2021). 

4 South East The South East is known for its vibrant agricultural practices, particularly in cassava 

and yam production. AI adoption is gradually increasing, especially among large-

scale farmers who leverage AI for market access and supply chain optimization 

(Okeowo & Ndubuisi, 2021). However, smallholder farmers may struggle with 

adoption due to limited access to technology and financial constraints (Igbokwe et 

al., 2022). 

5 South South This zone, rich in natural resources, has witnessed some adoption of AI technologies, 

particularly in aquaculture and oil palm cultivation. Large-scale farmers are more 

inclined to adopt AI for resource management and environmental monitoring 

(Olagunju et al., 2021). In contrast, smallholder farmers often lack the necessary 

infrastructure and training, which hampers their ability to utilize AI effectively 

(Ogunniyi et al., 2022). 

6 South West The South West, with relatively better infrastructure and access to technology, has a 

higher rate of AI adoption among both large-scale and smallholder farmers. Large-

scale farmers increasingly use AI for precision agriculture, while smallholder 

farmers are beginning to engage with AI through mobile applications that provide 

agricultural advice and market information (Adedayo et al., 2021). 

 

Technology Readiness for AI Adoption in Agriculture 

The successful adoption of Artificial Intelligence (AI) in agriculture is significantly influenced by several factors, 

including internet connectivity, smartphone access, and farmers' willingness to embrace these technologies. 



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Understanding these elements is crucial for fostering an environment conducive to AI integration in farming 

practices. 

Internet Connectivity 

Internet connectivity is a foundational requirement for the effective implementation of AI technologies in 

agriculture. Reliable internet access enables farmers to utilize cloud-based AI applications, access real-time data, 

and communicate with agricultural experts. In Nigeria, the disparity in internet connectivity across different 

regions poses a challenge. Urban areas tend to have better internet infrastructure compared to rural regions, where 

many smallholder farmers operate. This digital divide can hinder the adoption of AI technologies, as farmers in 

less connected areas may struggle to access the necessary tools and information to leverage AI effectively 

(Ogunniyi et al., 2022). Enhancing internet connectivity in rural areas is essential for enabling farmers to benefit 

from AI-driven solutions, such as precision agriculture and market analysis tools. 

Smartphone Access 

Smartphones play a critical role in facilitating AI adoption among farmers, particularly smallholders. With the 

proliferation of mobile technology, smartphones have become accessible to many farmers, providing them with a 

platform to access agricultural applications that utilize AI. These applications can offer valuable services, such as 

weather forecasts, pest management advice, and market price information. However, the extent of smartphone 

penetration varies across different socio-economic classes and regions. Large-scale farmers are more likely to have 

access to advanced smartphones and the internet, allowing them to utilize sophisticated AI applications. In contrast, 

smallholder farmers may have limited access to smartphones, which can restrict their ability to engage with AI 

technologies (Adebayo et al., 2021). Bridging this gap through targeted initiatives, such as providing affordable 

smartphones and training on their use, can enhance AI adoption among smallholder farmers. 

Farmers' Willingness to Adopt AI 

The willingness of farmers to adopt AI technologies is influenced by several factors, including perceived benefits, 

trust in technology, and previous experiences with agricultural innovations. Farmers who recognize the potential 

advantages of AI, such as increased productivity and reduced labor costs, are more likely to embrace these 

technologies. However, skepticism about the reliability and effectiveness of AI can hinder adoption, particularly 

among smallholder farmers who may have limited exposure to technology (Oluwaseun et al., 2022). Education 

and awareness campaigns that demonstrate the practical benefits of AI in agriculture can help build trust and 

encourage adoption. Additionally, involving farmers in the development and testing of AI solutions can foster a 

sense of ownership and increase their willingness to adopt these technologies (Eze et al., 2021). The readiness for 

AI adoption in agriculture is significantly influenced by internet connectivity, smartphone access, and farmers' 

willingness to embrace new technologies. Addressing the challenges associated with these factors is essential for 

promoting the effective integration of AI in Nigeria's agricultural sector. By improving internet infrastructure, 

enhancing smartphone accessibility, and fostering a positive attitude towards AI, stakeholders can create an 

environment that supports the widespread adoption of AI technologies, ultimately leading to increased agricultural 

productivity and sustainability. 

Conclusion 

This study has explored the role of artificial intelligence (AI) in transforming agriculture, particularly in the context 

of Nigeria. The findings indicate that AI applications, including precision agriculture, crop monitoring, pest and 

disease detection, and resource optimization, have the potential to significantly enhance agricultural productivity 



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and sustainability. Notably, the adoption of AI can lead to increased yields, reduced environmental impacts, 

improved food security, and economic benefits for farmers. However, the study also identified several challenges 

and barriers to AI adoption, including technological literacy, infrastructure limitations, policy and regulatory 

challenges, and socioeconomic factors. These findings underscore the need for a multifaceted approach to facilitate 

the integration of AI technologies in agriculture. The implications of this study for policy and practice are 

substantial. Policymakers must recognize the transformative potential of AI in agriculture and create an enabling 

environment that supports technology adoption. This includes investing in infrastructure development, such as 

reliable internet connectivity and electricity supply, which are critical for the effective implementation of AI 

solutions. Moreover, policies should promote agricultural innovation by providing incentives for research and 

development in AI technologies tailored to local conditions and challenges. 

Additionally, enhancing technological literacy among farmers is essential. Training programs that focus on the 

practical applications of AI in agriculture should be developed and implemented, particularly in rural areas. These 

programs should aim to empower farmers with the knowledge and skills necessary to leverage AI tools effectively, 

thus increasing their confidence and willingness to adopt new technologies. Furthermore, establishing clear 

regulatory frameworks that address data privacy, technology transfer, and intellectual property rights is crucial to 

fostering innovation and collaboration between stakeholders in the agricultural sector. Future research should focus 

on several key areas to further advance the understanding of AI in agriculture. Firstly, longitudinal studies that 

assess the long-term impacts of AI adoption on agricultural productivity and sustainability are necessary. These 

studies can provide valuable insights into the effectiveness of AI technologies over time and the factors that 

contribute to successful implementation. Secondly, research should explore case studies of successful AI adoption 

in different agricultural contexts, particularly in developing countries. Understanding the specific conditions and 

strategies that facilitated these successes can inform best practices and guide future initiatives. Additionally, 

investigating the socio-cultural factors that influence farmers' attitudes towards technology adoption is vital. Future 

studies should examine how cultural beliefs and practices interact with technology use, as this can provide insights 

into overcoming resistance to new innovations. Lastly, interdisciplinary research that integrates perspectives from 

agriculture, technology, economics, and social sciences can lead to a more comprehensive understanding of the 

challenges and opportunities associated with AI in agriculture. Such research can support the development of 

holistic solutions that address the complex issues faced by farmers in Nigeria and similar contexts. In a nutshell, 

while the potential of AI in transforming agriculture is immense, realizing this potential requires concerted efforts 

from policymakers, researchers, and practitioners. By addressing the identified challenges and facilitating the 

adoption of AI technologies, Nigeria can move towards a more sustainable and productive agricultural future. 

 

Declaration  

 

Acknowledgment: We sincerely acknowledged the contribution of the group of Journal of Global Sustainability 

Research for removing the knowledge boundaries. 

Funding: The authors did not receive any fund from anybody with regards to this study. 

Conflict of interest: Authors did not declare any conflict of interest.  

Ethics approval/declaration: All the sources of data used have been carefully acknowledged. 

Consent to participate: All the authors are aware of the submission and the status of the paper. 

Consent for publication: The authors are glad to hear about the paper acceptance by the Global Sustainability 

Research. 



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Data availability: The authors agree to publish their paper openly for public use without any obstacles.   

Authors’ contribution: All authors contributed to the paper’s compilation and preparation, Aminu Adamu 

Ahmed*1, Yusuf Musa were contributed with technological aspects while Rilwanu Sulaiman2, and Nasiru 

Adamu3, Yusuf Musa4 help with agricultural aspects of the research. 

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