































   Advancements in Agricultural Development 
  Volume 6, Issue 4, 2025 
  agdevresearch.org 

 

1. Ataharul Chowdhury, Associate Professor, School of Environmental Design and Rural Development, University of Guelph, 
50 Stone Road East, Guelph, ON. N1G 2W1, Canada, ataharul.chowdhury@uoguelph.ca,  

 https://orcid.org/0000-0003-2432-0933  
2. Uduak Ita Edet, PhD Candidate, School of Environmental Design and Rural Development, University of Guelph, 50 Stone 

Road East, Guelph, ON. N1G 2W1, Canada, uedet@uoguelph.ca,   https://orcid.org/0000-0002-1675-0442  
 

1 

 

Enabling Responsible AI-Driven Agri-Food Innovation in 
Ontario: A Framework for Analysis of Adoption Challenges 

and Opportunities 
 

        A.Chowdhury1  U. I. Edet2      
 

Article History 
Received: May 15, 2025 
Accepted: October 17, 2025 
Published: November 4, 2025 
 
 
Keywords 
structured literature review; AI 
adoption framework; AI ethics; 
inclusive innovation; interconnected 
systems; technology adoption; 
adoption barriers; SDG 9: Industry, 
Innovation, and Infrastructure   

Abstract 
AI adoption in the agri-food sector offers significant gains in productivity 
and competitiveness, but responsible implementation is essential to 
avoid stakeholder resistance and ethical concerns. This study examines 
the adoption of artificial intelligence (AI) technologies in Ontario’s 
horticultural and livestock sectors. Applying a systems perspective and 
responsible innovation, it identifies and categorizes emerging AI 
applications, develops a conceptual framework to capture technological, 
social, environmental, individual, and institutional factors, and proposes 
practical strategies to promote adoption. A structured literature review 
of peer-reviewed articles, government reports, and industry publications 
was conducted to manually classify AI technologies into content layer 
classifications: descriptive, diagnostic, predictive, and prescriptive, and 
map them to a framework. Diagnostic and prescriptive technologies 
dominate in horticulture, while AI applications in livestock are fewer and 
more evenly distributed across functional layers. Out of the 24 
technologies identified, only four technologies, three in horticulture and 
one in livestock, demonstrated all analytical functions, highlighting the 
need for more integrated AI solutions. Key barriers include high cost, 
interoperability challenges, data privacy concerns, technical skill gaps, 
and limited digital infrastructure. Recommendations include promotion 
of targeted institutional support, operational efficiency, and ethical data 
governance. The framework provides practical guidance for responsible 
AI adoption and a foundation for future empirical research. 

mailto:ataharul.chowdhury@uoguelph.ca
https://orcid.org/0000-0003-2432-0933
mailto:uedet@uoguelph.ca
https://orcid.org/0000-0002-1675-0442


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Introduction and Problem Statement 
 
Innovations such as AI, robotics, and the Internet of Things (IoT) reshape farming practices and 
decision-making across the agri-food sector (Klerkx et al., 2019; Leader et al., 2020). Among 
these, AI plays a central role. It is defined as a computational construct that stimulates 
intelligence through algorithmic outputs, unlike human intelligence, which evolves biologically. 
AI is widely applied in agriculture to monitor crop and soil health, manage pests, and enhance 
yields through data-driven insights (Gignac & Szodorai, 2024). 
 
In Canada, the total farmland area has remained relatively stable at around 158 million acres in 
2016 (Statistics Canada, n.d.). Canada is also emerging as a leader in agricultural innovation, 
with the increasing use of precision tools such as crop monitoring software, drones, and 
machine learning (Greene & Murphy, 2021; Lemay & Boggs, 2024). Yet, adoption remains 
shaped by context-specific factors that are not always easily defined (Lemay et al., 2021).  
 
Figure 1 
 
 Level of Technology Adoption in Canadian Agriculture 

 
Note. Adapted from “Barriers to Adoption: Digital Agriculture in Ontario’s Food Production 
Landscape,” by  K. O. Twum, 2025 Canadian Agri-Food Policy Institute (CAPI). https://capi-
icpa.ca/wp-content/uploads/2025/05/2025-05-23-Kwaku-Twum-Digital-Agriculture-EN.pdf 

 
Figure 1 shows the technology adoption levels across Canadian provinces. Typically, large farms 
(>5000 acres) achieve high technology adoption, medium farms (2000–5000 acres) maintain 
moderate-to-high levels, and small farms (<2000 acres) continue to lag due to resource 
constraints (Lazurko et al., 2024; Lemay & Boggs, 2024; Twum, 2025).  
 
 

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Canadian provinces with higher technology adoption levels are better positioned to adopt 
advanced technologies, largely due to their large-scale operations, higher revenues, and 
institutional support, which favor investment in precision agriculture and digital tools (Twum, 
2025). Sector-specific demands shape moderate level adoption, while structural challenges, 
such as farm sizes and limited infrastructure, drive low adoption (Twum, 2025).  
 
Ontario, which accounts for over 25% of Canada’s farms and 60% of its greenhouse area, plays 
a central role in the Canadian Agri-Food Sector (Hall et al., 2024; Rana et al., 2024; Hiebert et 
al., 2025). Farmers in the province use data-connected devices, remote sensing, robotics, and AI 
systems to enhance monitoring, prevent waste, automation, and decision-making (Hall et al., 
2024; Smart Prosperity Institute., 2021). Dairy farming is a dominant sector, actively exploring 
AI and Big Data to address environmental challenges like methane emissions (Neethirajan, 
2024; Rana et al., 2024). Beef cattle producers use technology mainly for breeding and herd 
management, while greenhouse farming is expected to be a future focus for innovation 
(Fonseka et al., 2024; Lazurko et al., 2024).  
 
AI is gradually being integrated into Canadian agriculture, particularly in robotics, machine 
learning, and data analytics (Hall et al., 2024).  In Ontario, its application is limited by high costs, 
poor broadband connectivity, interoperability challenges, weak stakeholder coordination, data 
privacy concerns, and pressure to increase sustainability without compromising productivity  
(Dara et al., 2022; Leader et al., 2020; Lemay et al., 2021; McCaig & Rezania et al., 2023 
Neethirajan, 2024). Although these barriers are well-documented, they remain underexplored 
in Ontario’s agri-food context, particularly in relation to how they can interact with AI 
technology functions to shape adoption decisions (Ahmed & Shakoor, 2025). This study 
addresses this gap by identifying emerging AI technologies according to their content-layer 
analytical functions. It shows how existing barriers can interact with these functions to 
influence adoption and outlines context-specific measures to support inclusive and responsible 
adoption in Ontario’s agri-food sector. Ultimately, this understanding can encourage adoption, 
bridge knowledge gaps, and guide tailored institutional support that meets the specific needs of 
producers in Ontario’s agri-food sector.  
 

Conceptual Framework 
 
The systems perspective highlights interconnected actors and interactions across technologies, 
processes, and institutions, and shows that effective systems depend on coordination, feedback 
loops, and adaptability (Arnold & Wade, 2015; Klerkx et al., 2012; Mansoor & Williams, 2024).  
Integrating AI into agriculture raises significant ethical and social concerns, including issues of 
privacy, animal welfare, and accountability (Dara et al., 2022; Papagiannidis et al., 2025). These 
concerns highlight the need for responsible innovation, emphasizing four key dimensions: 
including anticipation, inclusion, reflexivity, and responsiveness to ensure that technological 
advancements align with societal values and priorities (Gremmen et al., 2019; Massfeller, 
2025).  Similarly, responsible AI seeks to maximize benefits and minimize risks by aligning with 
eight key thematic trends: privacy, accountability, safety and security, transparency and 

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explainability, fairness and non-discrimination, human control of technology, professional 
responsibility, and the promotion of human values (Papagiannidis et al., 2025). The systems 
perspective informs the identification of individual, social, and institutional factors shaping 
adoption patterns across horticulture and livestock sectors. Responsible innovation, in turn, 
guides the analysis of ethical, social, and governance issues, including privacy, accountability, 
and stakeholder inclusion. While the systems perspective highlights the need for robust 
infrastructure and institutional linkages, responsible innovation emphasizes stakeholder values, 
expectations, and governance concerns. Effective adoption, therefore, depends on coordinated 
systemic interventions (Klerkx et al., 2012). These perspectives structure the study’s design and 
analysis, providing the foundation for the conceptual framework used to examine AI adoption 
in Ontario’s agri-food sector. 
 

Purpose 
 
This review identifies and categorizes emerging AI technologies relevant to Ontario’s 
horticultural and livestock sectors, develops a conceptual framework that captures the 
multidimensional factors influencing adoption, and proposes practical strategies for the 
responsible and inclusive integration of AI in agriculture. Ontario’s horticultural and livestock 
sectors were selected due to their economic significance, diversity in production systems, and 
varying levels of digital readiness (Lemay & Boggs, 2024; Twum, 2025). Although over half of 
Ontario farms already use digital tools, adoption patterns vary across sectors and farm sizes 
(Hall et al., 2024). Combined with an aging producer base and growing calls for stronger public-
private investment  (Fonseka et al., 2024; Lazurko et al., 2024). Ontario emerges as an ideal 
setting to examine responsible AI adoption. More importantly, the province must leverage 
more than its natural advantages by strengthening innovation systems that integrate advanced 
technologies and promote global competitiveness (Lemay et al., 2021).  
 

Methods 
 
Using a structured literature review (Okoli, 2015), this study examined factors influencing 
technology adoption and AI applications in Ontario’s horticulture and livestock sectors. The 
literature review was conducted between October and December 2024. Searches were 
conducted in Google Scholar using keyword combinations to identify relevant literature, 
including peer-reviewed journal articles, and grey literature from government agencies. Grey 
literature were further accessed directly through industry or academic institutional websites.  
Some peer-reviewed sources identified through Google Scholar may also be indexed in Scopus 
or Web of Science, but these databases were not directly prioritized due to limited institutional 
access and minimal added value. The limited volume of peer-reviewed studies on AI adoption in 
Canadian agriculture, particularly in Ontario, required consulting broader literature on digital 
agricultural technologies to identify adoption factors. Nevertheless, peer-reviewed sources 
were prioritized, with industry reports and technology provider websites used only to fill gaps 
and document practical AI applications. For instance, to identify AI technologies in Canadian 

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agriculture, particularly in Ontario, we consulted technology developer websites such as 
SoundTalks, DeLaval, and Farmonaut. 
 
Literature was identified using keywords such as “factors influencing AI adoption in Ontario 
livestock and horticulture,” “technology adoption,” “drivers and barriers of AI/digital 
technology adoption,” “digital/precision agriculture technologies,”  “AI technology providers in 
agriculture,” “technology adoption in controlled environment agriculture,” “agri-food 
innovation systems,” “digital transformation in agriculture,” “Ontario agriculture,” and 
“Canadian agriculture.”  Sources were primarily assessed for recency, with most published 
between 2022 and 2025 except earlier studies such as  (Gremmen et al., 2019; Klerkx et al., 
2012; Leeuwis & Aarts, 2011), which were included to provide a theoretical foundation. The 
included sources provided empirical or theoretical evidence on AI adoption and digital 
transformation, primarily focused on Ontario’s horticulture and livestock sectors. To 
contextualize findings, relevant studies from other Canadian regions or global settings were 
also considered when they offered relevant information. Studies were excluded if they were 
unrelated to AI adoption in agriculture or focused on non-Canadian contexts without broader 
applicability. 
 
AI Technologies were documented and organized manually using Microsoft Excel, which served 
as the primary tool for organizing the literature. Data was organized across three Excel 
spreadsheets: the first recorded key study details (title, authors, publication date, aim, findings, 
source URL, and citation); the second categorized AI technologies by application e.g automated 
harvesting, animal identification, sector (horticulture or livestock), and function (e.g., disease 
detection, yield prediction), associated benefits, challenges, and the skills required for effective 
use were also identified. To avoid duplication, the review selected a single representative 
technology when multiple AI technologies addressed the same function. This process facilitated 
the further mapping of each technology to its corresponding analytical functions  (Njuguna et 
al., 2025; Püschel et al., 2016). For example, Tools were categorized as descriptive (summarizing 
past patterns), diagnostic (identifying root causes), predictive (anticipating risks and outcomes), 
or prescriptive (recommending actions). No bibliometric or qualitative software was used; 
instead, manual synthesis was applied by sorting and comparing studies to identify recurring 
patterns. Findings were triangulated with industry and academic reports to enhance validity 
and guide the development of the conceptual framework. For the technology content layer, 
analysis relied on basic descriptive techniques such as frequency counts of functions, sectors, 
and applications, to identify patterns, which were summarized in a table. This approach aligns 
with scoping review guidance in Peters et al. (2020), where draft charting tables can be used to 
capture core study information and refined as the review progresses.  
 
To develop the framework, we synthesized three key literatures on digital technology adoption 
in agriculture. Leeuwis and Aarts (2021) contributed a sociological perspective emphasizing 
trust, power dynamics, and policy influence across individual, social, and institutional levels. 
The ELSA framework (van Hilten et al., 2025), informed the normative and governance 
components of the study, aligning with principles of Responsible AI, including transparency, 
trust, regulation, data governance, and sustainability. Njuguna et al. (2025) further informed 

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the individual level by highlighting cognitive factors, risk perception, alongside broader 
technological considerations such as technology performance and user capacity. Their work 
also outlined the analytical capabilities of digital technologies: descriptive, diagnostic, 
predictive, and prescriptive, which were integrated into the technological layer of the 
framework to understand the functions and practical applications of AI technologies in 
agriculture. The final framework was then adapted to align with the factors influencing 
adoption, while the technological content layer highlights how the characteristics of AI 
technologies interact with the interconnected levels.  
 
While efforts were made to ensure objectivity, we acknowledge potential subjectivity in 
classifying AI technologies, especially when relying on commercial information that may 
overemphasize AI technology benefits over limitations. To mitigate bias, preference was given 
to peer-reviewed sources and government-backed reports, and information from industry 
websites, policy briefs, and reports was cross-checked against these sources. URLs to 
technology provider websites were documented for reference and verification, and 
inclusion/exclusion criteria were applied to filter sources lacking relevance or regional 
significance. Nevertheless, these considerations contextualize the study findings and encourage 
cautious interpretation. The following section presents the findings which are grounded in co-
dependent systems perspectives, responsible AI, and content-layer classification of AI 
technologies (Leeuwis & Aarts, 2021; Njuguna et al., 2025; van Hilten et al., 2025). 
 

Findings 
 

The findings presented below reflect the study’s objectives to identify and categorize emerging 
AI technologies relevant to Ontario’s horticultural and livestock sectors; develop a conceptual 
framework that captures technological, social, environmental, individual, and institutional 
factors influencing adoption; and propose practical strategies for the responsible and inclusive 
integration of AI in agriculture. 
 
  

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Table 1  
 
AI Technologies in the Horticultural and Livestock Sectors 

 

AI Technology 
Content Layer Classification 

Sample Organization Descriptive Diagnostic Predictive Prescriptive 
HORTICULTURE      
Plant Disease Prediction     Ukko Agro 
AI Fertilizer application     Ukko Agro 
AI Weeder     BH Frontier Solution 
Climate Control systems for 

greenhouses 
    Hoogendoorn 

Drone-based Real-Time 
Aerial Surveillance and 
Field Monitoring 

    Windwarddrones 

Weed Prediction     GECO-Vancouver 
Soil Health Monitoring      Chrysalab-Montreal 
AI-driven Pollination 

Robotics 
    Arugga AI/Biobest 

Group 
Produce Sorting using AI     Clrifruit AI 
Automated/Smart Irrigation     Farmonaut 
Automated Seeding & 

Harvesting 
    Haven Greens 

 AI-driven Farm Advisory 
Tool 

    Farmonaut 

Quality Testing     P & P Optica 
LIVESTOCK      
Animal Identification Based 

on Muzzle Prints 
    Onecup AI-Vancouver 

 
Animal Identification Based 

on Biometrics 
     Onecup AI-Vancouver 

Animal Identification Based 
on Retina Imaging 

    Onecup AI-Vancouver 

AI Sensors for Respiratory 
Disease Detection in Swine 

    Soundtalks 

Monitoring Livestock Health 
and Behavior 

    Herdwhistle-Alberta 

Drone-based Livestock 
Management and Health 
Assessment 

    Zenadrone 

Milk Yield Prediction     DeLaval  
Automated Feeding Systems      DeLaval 
Breeding Management     DeLaval 
Sex Determination with 

integrated AI solutions 
    Hypereye-Montreal 

Automated Calving 
Monitoring 

    Onecup AI-Vancouver 

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Table 1 presents twenty-four AI technologies, with a higher concentration of prescriptive and 
diagnostic technologies in horticulture, reflecting a trend toward tools that not only identify 
challenges but also offer solutions to optimize production. In contrast, livestock technologies 
show a more balanced distribution across the four content layer characteristics and are 
generally fewer in number compared to horticulture. Predictive tools are less common across 
both sectors, indicating opportunities for growth in AI technologies that can anticipate risks and 
future trends to inform decision-making. Generally, the distribution of AI tools across content 
layers reflects specific preferences, which may also hint at unique challenges and opportunities 
within sectors. A key observation is the potential for each layer to support the function of the 
next. Descriptive tools can gather raw data, diagnostic tools detect possible issues, predictive 
tools forecast risks, and prescriptive systems recommend actions. However, only four 
technologies were found to demonstrate all analytical functions, highlighting opportunities for 
further technology development in the sector.  
 
This study develops a conceptual framework to examine how systemic factors across 
technological, social, environmental, individual, and institutional levels influence AI adoption.  
 
Figure 2  
 
Key Factors that Influence the Adoption of AI Technologies 

 
 
Technological Level  
Interoperability remains one of the greatest challenges in integrating digital technologies 
(Indira et al., 2023). It concerns the ability of diverse systems and devices to function together, 
with standardized data protocols, and is therefore significant in advancing digital adoption in 
Canada (Bioenterprise, 2024; Green et al., 2021; Lemay & Boggs, 2024). While there is clear 
potential for technological integration to enhance the operational efficiency of emerging tools 
(Lazurko et al., 2024), existing solutions are often specialized. For example, Huneke et al. (2024) 
found that Canadian Agri-tech organizations tend to be commodity-specific, with livestock, 
major crops, and vegetables each representing about one-quarter of those examined. 

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Furthermore,  Phillips et al. (2019) observed that discussions on Canadian digital agriculture 
focus more on data governance, particularly ownership and control, than on the capabilities of 
the technologies. These debates are especially relevant due to the central role of big data in 
advancing sustainability and optimizing agricultural operations (Birch & Bronson, 2022; 
Bronson, 2022; Dara et al., 2022; Neethirajan, 2024). However, examining digital tools through 
the technology content layer offers a complementary perspective by clarifying how AI 
technologies can inform policy and create value through their analytical functions. Classifying 
these tools within a structured taxonomy enables a clearer understanding of their role in 
supporting decision-making (Püschel et al., 2016). 
 
Social Level  
Factors such as power relations, network effects etc play a key role in technology adoption.  For 
instance, information sources from peers, extension services, media, or training shape 
individual perception of the technology, and can determine how it is viewed as useful, 
trustworthy, or risky (Leeuwis & Aarts, 2021).  Similarly, Canadian farmers are more likely to 
adopt technologies when they observe others in their networks doing so (Bioenterprise, 2024; 
Hall et al., 2024). Although digital technologies can improve efficiency and yields, they also 
create new challenges such as skill divides, rising land costs, and questions over control of 
digital data. These carry social implications, including potential risks of exploitation for 
marginalized groups (Leader et al., 2020; Makinde et al., 2022). Power dynamics further 
influence who benefits, with adoption often favoring larger, more educated, or multi-
generational farms (Wang et al., 2016). 
 
Environmental Level  
Environmental factors influence technology adoption by encouraging solutions that reduce 
waste, support ecological balance, and meet environmental standards  (van Hilten et al., 2025). 
AI plays a dual role in agriculture, simultaneously helping to mitigate environmental pressures 
while contributing to them. AI tools optimize resource use and reduce methane emissions, 
supporting sustainability goals in high-value sectors like Ontario’s dairy industry (Hall et al., 
2024; Neethirajan, 2024). On the other hand, AI’s high energy demands raise environmental 
concerns, emphasizing the need for low-energy solutions tailored to agricultural contexts 
(Raghav et al., 2024).  
 
Individual Level  
Adoption is often understood at the individual level as a process where decisions rely on 
personal evaluation and judgment (Leeuwis & Aarts, 2021). Farmers may adopt technologies to 
increase yields, reduce labor, or pursue sustainability (MacPherson et al., 2022). Nevertheless, 
the extent of adoption depends on whether these tools align with their knowledge, skills, and 
existing practices (Chowdhury et al., 2025). Evidence from Canadian farmers shows varying 
perceptions as technology adoption in Canadian agriculture depends less on availability but on 
farmers’ unique needs and contextual factors (Lemay et al., 2021).  While some view digital 
tools as supportive, others see it as unreliable (McCaig & Dara et al., 2023). This suggests that 
adoption can be relational, shaped by confidence in providers and institutions (Hall et al., 
2024). Trust in providers and institutions, therefore, becomes critical, as credibility encourages 

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experimentation and reduces uncertainty (Hall et al., 2024). At the individual level, the aging 
demographic of Ontario farmers raises succession concerns, shaping risk preferences and 
openness to innovation (Hall et al., 2024). Financial capacity further influences adoption, 
favoring those better positioned to absorb high upfront costs (Green et al., 2021). 
 
Institutional Level  
AI adoption can leverage on existing foundations of local Canadian innovation capacity 
supported by research initiatives, precision agriculture start-ups, and big data platforms. Yet, 
weak connections to global networks limit broader diffusion and scalability (Phillips et al., 
2019). Adoption remains highly context dependent. Lemay and Boggs (2024) observe that 
uptake is shaped by farm type, infrastructure availability, and the policy environment. Even 
where technologies show clear benefits, their impacts depend on inclusive, region-specific 
policies that align innovation with ecological and social priorities (Green et al., 2021). 
Infrastructure gaps worsen these challenges. Limited rural broadband access continues to 
constrain adoption in Ontario, forcing some farms to rely on costly alternatives such as Starlink 
(Greig et al., 2023; Hall et al., 2024). Without reliable connectivity, digital tools remain out of 
reach for many producers, slowing diffusion and reinforcing existing divides. Ethical and 
governance issues shape farmer’s decisions as poorly designed AI systems risk undermining 
trust, raising animal welfare concerns, and compromising privacy (Greig et al., 2023; Hall et al., 
2024). At the same time, the concentration of power in multinational agribusiness firms 
exacerbates fears around data rights and reinforces structural inequities (Dara et al., 2022) 
 
Table 2 links technology content layer classifications to the framework in Figure 2 above, 
reflecting how interrelated factors may further influence AI adoption.  
 
  

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Table 2  
 
Relating Technology Content Layer Classification with Other Factors Influencing AI Adoption 
Content 
Layer 
Classification 
of AI Tech. 

Analytical 
Function  Examples  

Possible 
Adoption 
Barriers 

Factors Likely 
to Influence 
Adoption 

Potential 
Drivers 

Descriptive 
 

What is 
happening? 

Soil health 
Monitoring 

Data variability, 
lengthy data 
processing 
time, 
interoperability 
issues 

Individual, 
Institutional 

Standardized 
data formats, 
reliable 
infrastructure
, and 
technical 
assistance 

Diagnostic What 
problems 
exist? 

AI Sensors 
for 
respiratory 
disease 
detection 

May require 
technical 
expertise, data 
privacy 
concerns 

Individual, 
Social, 
Institutional 

Availability of 
Skilled labor, 
Advisory 
support, and 
Knowledge 
sharing 
opportunities 

Predictive What might 
happen? 

Milk yield 
prediction 

High costs, risk 
of 
environmental 
damage due to 
unreliable data 

Individual, 
Social, 
Environmenta
l, Institutional 

Demonstrate
d benefits, 
financial 
incentives, 
and training 
support 

Prescriptive What can 
be done? 

Automated 
irrigation 
systems 

High upfront 
costs, lack of 
trust in 
recommendatio
ns 

Individual, 
Institutional 

Appropriate 
regulatory 
framework, 
transparent 
data 
practices, 
policy 
incentives 

 
Table 2 highlights how social, institutional, and environmental contexts interact with AI 
technology functions to influence adoption, while Figure 3 illustrates the links between these 
systemic factors and corresponding analytical functions. 
 
 
 
 
 

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Figure 3  
 
Interaction of Technological Content Layers with Other Systemic Factors Influencing AI Adoption 

 
 
The conceptualization of technological functions across systemic factors shows adoption as a 
continuous, interactive process rather than a linear one, enabling engagement across multiple 
levels. The inherent complexity of AI systems present adoption challenges that require 
proactive strategies to prevent harmful societal impacts (Buhmann & Fieseler, 2021; Raman et 
al., 2024). Key concerns such as unemployment, privacy, bias, misinformation, and digital 
inequities, give rise to the need for equitable, inclusive data-sharing agreements aligned with 
societal interests (Birch & Bronson, 2022; Polyportis & Pahos, 2024; Twum, 2025). Technical 
solutions like standardized APIs, open-source platforms, and integrated systems can support 
data optimization (Lemay & Boggs, 2024). Preparing farmers, agricultural workers, and rural 
communities for digital transitions also demands proactive state involvement and a multi-
stakeholder approach centered on early engagement, participatory co-design, and ongoing 
reflection on ethical, social, and cultural implications (Buhmann & Fieseler, 2021; Leader et al., 
2020; Polyportis & Pahos, 2024).  
 

Conclusions, Discussion, and Recommendations  
 

This review examined emerging AI technologies in Ontario's horticultural and livestock sectors 
and the key factors shaping their adoption. Using the technology content layer classification, it 
analyzed how AI functions support agricultural decision-making, then developed a framework 
linking these layers with systemic factors to broaden understanding of adoption. Horticulture 
favors diagnostic and prescriptive tools that not only identify issues but also optimize 
production, while livestock technologies are fewer and more evenly distributed across all four 
content layers. Yet only a few technologies demonstrated all analytical functions, 
 

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Focusing on the content layer classification offers a structured view of technology functions, 
but could overlook the broader contextual factors influencing adoption as observed by 
(Outcault et al., 2022). According to Ndah (2015), adoption is shaped by both technological and 
non-technological factors, Leeuwis and Aarts (2021) also emphasize that adoption rarely occurs 
in isolation but within interrelated system dynamics. Responsible innovation further reinforces 
this perspective, emphasizing that effective solutions must emerge through stakeholder 
engagement and practical action (Kroesen et al., 2015). This justifies the value of a framework 
that links technological content layers with systemic factors to capture the complexity of AI 
adoption in Ontario’s agriculture. 
 
The theoretical perspectives applied in this study informed both the design of the framework 
and the interpretation of findings. The AI technologies identified in this study are often 
specialized in one to three content layer functions, with few possessing all content layer 
analytical capabilities. Tailored AI applications improve resource management by enabling 
timely interventions (Miller et al., 2025). However, interoperability adds value by linking data 
within farms and across the value chain (Lemay & Boggs, 2024), yet it requires integrating 
diverse data formats, naming conventions, and operational requirements into a unified 
framework, which may pose significant technical challenges (Ahmed & Shakoor, 2025; Lemay & 
Boggs, 2024). Furthermore, the interaction of the functional capabilities of AI with systemic 
factors, reveal the broader conditions under which adoption occurs. For example, descriptive 
analytics depends on standardized data and reliable infrastructure, yet barriers such as weak 
interoperability, limited broadband, and fragmented institutional support often constrain 
implementation. At the same time, adoption requires synchronised investment in farmer 
upskilling and policy support to build familiarity with AI tools. Trust in providers reinforced by 
networks of positive influence further reduces uncertainty and encourages use. These dynamics 
reflect a systems perspective, where innovation emerges from managing interdependencies 
across technology, society, economy, and institutions (Arnold & Wade, 2015; Klerkx et al., 2012; 
Mansoor & Williams, 2024). 
 
The framework proposed in this study can be applied in qualitative data collection through 
interviews with key informants as well as quantitative surveys to assess stakeholder 
perspectives. It can also be used as a case study tool to examine specific AI technologies, 
allowing for further refinement. The next step in this research will empirically test the 
framework to identify the most critical factors for targeted interventions in Ontario. A SWOT 
analysis guides the identification of internal (strengths, weaknesses) and external 
(opportunities, threats) factors (Puyt et al., 2023). These factors will then be evaluated using 
the Analytic Hierarchy Process (AHP), which uses pairwise comparisons in decision making, 
allowing for a deeper understanding of these findings and a transparent prioritization of key AI 
adoption drivers to inform targeted strategies, and policies  (Ishizaka & Labib, 2011; Saaty, 
1987). Future research could examine stakeholder priorities to contextualize these findings 
across agri-food sectors and uncover sector specific adoption drivers. 
 
Although the focus of this study was Ontario, AI technologies from firms in other provinces 
were often used as proxies where Ontario-specific data was unavailable. While this introduces a 

https://doi.org/10.37433/aad.v6i4.609


Chowdhury and Edet  Advancements in Agricultural Development 
 

https://doi.org/10.37433/aad.v6i4.609   14 
 

minor limitation, it does not detract from the overall objective of mapping key AI technologies 
in agriculture. Overall, the findings highlight that the successful adoption of AI in agriculture 
depends not only on technological advancements but also on reducing systemic barriers while 
cultivating trust among stakeholders, addressing ethical concerns, and establishing robust 
regulatory frameworks.   

 
Acknowledgments 

 
Funding Information: This research was funded by the Ontario Agri-Food Innovation Alliance, 
Canada. Grant number -UG-T1-2024-102402. 
 
Conflict of interest: The authors declare no conflicts of interest.  
 
Previous Dissemination: Preliminary findings were presented at the 2025 Rural Symposium, 
held at the University of Guelph in March 2025. 
https://doi.org/10.21083/ruralreview.v9i1.8330  
 
Artificial Intelligence:  Artificial intelligence tools were not used in the preparation of this 
manuscript. 
 
Author Contribution Statement: A. Chowdhury – investigation, writing-review and editing, 
formal analysis. U. Edet  – investigation, writing-original draft, writing-review and editing, 
formal analysis.  

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