ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 137
www.rsisinternational.org
Humanizing Agricultural Robotics: A Constructivist Grounded
Theory of Farmers’ Perspectives on Adopting Chili Harvesting Robot
in Fertigation Farming
Mohd Fauzi bin Kamarudin
Fakulti Pengurusan Teknologi dan Teknousahawanan, Universiti Teknikal Malaysia Melaka
DOI: https://dx.doi.org/10.47772/IJRISS.2025.92800015
Received: 08 November 2025; Accepted: 14 November 2025; Published: 18 December 2025
ABSTRACT
The integration of robotics into agriculture offers opportunities to increase efficiency and address labour
shortages, yet its adoption is strongly shaped by farmers’ perceptions and lived realities. This study explores
how smallholder chili farmers perceive the adoption of a chili harvesting robot within fertigation farming, with
a particular focus on humanising agricultural robotics to ensure alignment with farmers’ social and economic
needs. Guided by a Constructivist Grounded Theory (CGT) approach, semi-structured focus group interviews
were conducted with five smallholder farmers from Kulai, Johor, Malaysia. The analysis generated five
interrelated domains that influence technology acceptance: (i) socio-needs of chili farmers, (ii) harvesting
practices, (iii) labour and human resource issues, (iv) farming economics, and (v) robotic handling. Together,
these domains capture the complex realities of farming and the conditions under which robotics may be accepted.
The study contributes by extending grounded insights into how agricultural robotics can be humanised through
the integration of farmer perspectives across these five domains, offering practical implications for innovators,
policymakers, and agritech developers aiming to design sustainable and farmer-centred smart farming systems.
Keywords: Human-Centred Robotics, Smart Farming, Technology Adoption, Chili Harvesting, Constructivist
Grounded Theory.
INTRODUCTION
The integration of robotics into agriculture has been widely promoted as a solution to labour shortages, efficiency
demands, and sustainability challenges in food production systems (Bechar & Vigneault, 2016; Liakos et al.,
2018). Within the broader agenda of smart farming, robotic solutions such as automated harvesting systems have
gained traction, particularly in high-value crops like chili, where labour-intensive practices limit productivity
(Shamshiri et al., 2018; Tzounis et al., 2017). In Malaysia, fertigation-based chili farming has become
increasingly significant in supporting food security and rural livelihoods (Rahman et al., 2020). However, despite
technological advancements, the successful adoption of agricultural robotics depends not only on technical
performance but also on how innovations are perceived, accepted, and integrated by farmers in their daily
practices (Eastwood et al., 2019; Klerkx & Rose, 2020).
Much of the existing research on agricultural robotics has prioritised technical optimisation, engineering design,
and cost efficiency (Bechar & Vigneault, 2017; Bac et al., 2014). Considerably less attention has been devoted
to the human dimension of adoption, particularly the perspectives, expectations, and lived realities of farmers.
Yet, end-users ultimately determine whether new technologies succeed or fail (Lajoie-O’Malley et al., 2020).
This gap is especially critical in the context of smart farming adoption in Southeast Asia, where socio-economic
diversity, labour constraints, and cultural practices strongly influence farmers’ willingness to engage with
technological innovations (Hafeez et al., 2022). Without integrating farmers’ voices into the innovation process,
robotic systems risk misalignment with the practical and social realities of agricultural communities (Klerkx,
Jakku, & Labarthe, 2019).
This study addresses this gap by adopting a Constructivist Grounded Theory (CGT) approach (Charmaz, 2014)
to explore chili farmers’ perspectives on adopting a chili harvesting robot in fertigation farming (refer Figure 1).
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 138
www.rsisinternational.org
CGT enables the co-construction of theory from participants’ experiences, making it well-suited for investigating
how farmers articulate concerns and expectations about robotic technologies (Bryant & Charmaz, 2007). Semi-
structured focus group interviews were conducted with chili farmers in Johor, Malaysia, and the analysis
generated five interrelated themes: (i) socio-needs of farmers, (ii) harvesting practices, (iii) labour
considerations, (iv) farming economics, and (v) robotic handling.
Figure 1: Proposed Chilli Padi Harvesting Robot
The study contributes to the literature in three ways. Theoretically, it extends technology adoption research by
grounding adoption factors in farmers’ lived experiences through CGT, offering a more nuanced understanding
of human–robot interaction in agriculture (Rose et al., 2021). Practically, it provides insights for innovators and
agritech developers to design farmer-centred robotic solutions that are contextually relevant and socially
acceptable. At the policy level, the findings highlight the importance of incorporating farmers’ voices into
Malaysia’s smart farming strategies to ensure inclusive and sustainable agricultural innovation (Food and
Agriculture Organization [FAO], 2022).
Scope of the Study
Johor is one of Malaysia’s leading agricultural states, with crop production contributing significantly to its
agricultural output. In 2023, Johor recorded RM27.2 billion in agricultural sales, of which RM20.9 billion came
from crops, underscoring the state’s strong reliance on small and medium-scale cultivation (The Sun, 2024).
While oil palm dominates, Johor’s agricultural landscape is also shaped by smallholders engaged in vegetables,
fruits, and high-value crops such as chillies, often on a part-time basis to supplement household income. These
smallholder contributions are crucial, as they ensure the diversity and resilience of the local food system, in line
with findings that small-scale farmers remain central to sustaining agricultural productivity in Southeast Asia
(FAO, 2022).
In addition, institutional support in Johor reflects the growing recognition of vegetable and chilli farming as
viable income sources. For instance, the Johor state government has allocated over RM5 million to support
young farmers, particularly in chilli cultivation and vegetable projects (The Star, 2023). Peri-urban districts such
as Kulai have become hubs for smallholder vegetable and chilli farming, where access to markets and
infrastructure facilitates part-time and community-based production. This context makes Johor—and specifically
areas around Kulai—a highly relevant research site to explore the experiences of smallholder farmers.
Furthermore, as Klerkx and Rose (2020) emphasize, understanding smallholder perspectives is critical in the era
of Agriculture 4.0, where new technologies and practices must align with local realities to ensure equitable and
sustainable adoption
Academic recent work also backs up the importance of agrifood smallholders in Malaysia broadly. The
Khazanah Research Institute's Understanding the Landscape of Agrifood Smallholders in Malaysia: Climate
Risks, Sustainable Standards, and Gender Gap (2024) provides evidence that many smallholder farms are
involved in vegetable, fruit, and local food crop production; these farms often operate on smaller plots and
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 139
www.rsisinternational.org
combine farming with other income sources. Thus Kulai, being close to urban centers while still retaining
agricultural land, is a strategic location to study smallholders’ practices and challenges in technology adoption
(like chilli robot).
Figure 1 below illustrates the land use and agricultural zoning in Kulai District and its surrounding areas. The
green-colored zones represent designated agricultural land, which includes areas cultivated by smallholders for
crops such as vegetables and chilli. These green areas highlight the concentration of peri-urban farming activities,
reflecting the importance of small-scale agriculture in Johor’s food supply chain. Meanwhile, the yellow-colored
zones indicate residential or mixed-use settlements that often border farming areas, allowing farmers to combine
part-time agricultural activities with other livelihood sources. This spatial proximity of settlements (yellow) to
farmlands (green) provides a unique context in which smallholders and part-time farmers, such as those
cultivating chilli, operate within a semi-urban agricultural landscape. The co-existence of settlement and
agricultural land-use supports the choice of Johor, particularly Kulai and its surroundings, as a relevant study
location for smallholder chilli farmers (Department of Agriculture Malaysia, 2023).
Figure 2: Land use and agricultural zoning in Kulai District and surrounding areas, Johor.
LITERATURE REVIEW
Smart Farming and Agricultural Robotics
The adoption of smart farming technologies—including robotics, automation, and precision agriculture—has
been promoted as a pathway to improve efficiency, sustainability, and resilience in agriculture (Liakos et al.,
2018; Tzounis et al., 2017). Robotics in particular has received increasing attention due to its potential to alleviate
labour shortages, improve harvesting accuracy, and reduce production costs (Bechar & Vigneault, 2016;
Shamshiri et al., 2018). High-value crops, such as chili (Capsicum frutescens), demand intensive manual labour
for harvesting, creating an urgent need for robotic solutions in countries like Malaysia, where fertigation farming
is widely practised (Rahman et al., 2020). However, while technological development is advancing rapidly,
adoption has remained uneven, with farmers’ perceptions and socio-economic conditions playing a crucial role
in shaping acceptance (Eastwood et al., 2019; Klerkx & Rose, 2020).
Humanising Agricultural Technology
The concept of humanising technology refers to designing innovations that are not only technically functional
but also aligned with human needs, abilities, and socio-economic realities (Clarkson, Coleman, Keates, &
Lebbon, 2013). In agriculture, humanising innovation involves ensuring that new technologies are user-friendly,
economically viable, and adaptable to farmers’ working conditions. Research has shown that human abilities
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 140
www.rsisinternational.org
such as concentration, decision-making, and spatial perception are crucial for effective agricultural operations
(Macdonald, 2013). Yet, many agricultural machines are designed without fully accounting for the variability of
human skills, leading to usability challenges and low adoption rates (Häggström, 2015). Humanising agricultural
robotics also requires attention to environmental stressors—such as noise, dust, and ergonomic strain—that can
affect both system performance and human well-being (Kearnes et al., 2016). If these factors are ignored, new
technologies risk rejection by intended users, rendering them ineffective or wasted investments.
Technology Adoption in Agriculture
Technology adoption in agriculture has been extensively studied through models such as the Technology
Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and innovation
diffusion theory (Venkatesh et al., 2003; Davis, 1989; Rogers, 2003). Key factors influencing adoption include
perceived usefulness, ease of use, socio-economic conditions, and institutional support (Rose et al., 2021).
However, scholars have increasingly argued that adoption cannot be explained solely by functional attributes, as
cultural, ethical, and contextual issues also shape decision-making (Klerkx, Jakku, & Labarthe, 2019). In the
context of agricultural robotics, adoption depends on whether the technology reduces labour intensity, fits within
existing farming practices, and aligns with farmers’ values and capacities (Eastwood et al., 2019). Thus,
exploring adoption from farmers’ perspectives is crucial for designing farmer-centred innovations that go beyond
technical performance.
Human–Technology Interaction in Farming
The study of human–technology interaction highlights the interdependencies between technological
performance and human operators. As automation increases, the farmer’s role is shifting from manual labourer
to technology manager, requiring new skills in interpretation, monitoring, and decision-making (Bronson &
Knezevic, 2016). Humanising robotic systems in agriculture means recognising farmers not just as end-users but
as active co-designers whose insights and concerns must shape innovation pathways (Eastwood et al., 2019).
Ignoring the socio-technical interface risks creating “solutions in search of problems” where robots are
developed without regard to actual farming contexts (Klerkx & Rose, 2020).
Constructivist Grounded Theory in Agricultural Innovation Research
To capture these complex, context-specific adoption dynamics, Constructivist Grounded Theory (CGT)
(Charmaz, 2014) offers a rigorous methodological lens. CGT emphasises the co-construction of knowledge
between researchers and participants, allowing theory to emerge from farmers’ lived experiences rather than
being imposed a priori. This approach is particularly valuable in agriculture, where socio-cultural practices,
family dynamics, and local economic constraints strongly influence technology adoption (Bryant & Charmaz,
2007). By applying CGT, researchers can uncover not just barriers and facilitators of adoption but also the deeper
meanings farmers attach to innovation, thereby advancing both theoretical understanding and practical guidance
for humanising agricultural robotics.
Proposed Conceptual Model
The proposed conceptual model illustrates the interconnected themes that underpin this study. At the foundation,
smart farming technologies such as agricultural robotics represent the driving force for innovation, particularly
in addressing labour shortages and efficiency challenges. However, successful implementation requires
humanising technology, ensuring that robotics are user-centred, socially acceptable, and adaptable to farmers’
working conditions. This leads to the critical stage of technology adoption, where farmers’ perceptions of
usefulness, ease of use, and socio-economic feasibility shape their willingness to adopt innovations. Adoption,
in turn, is influenced by human–technology interaction, which recognises farmers not merely as passive users
but as active agents whose experiences, practices, and cultural contexts shape innovation outcomes. To capture
these complex interdependencies, the study employs Constructivist Grounded Theory (CGT) as a
methodological lens, enabling theory to emerge inductively from farmers’ perspectives. The model highlights
that the pathway from technological innovation to adoption is non-linear and requires attention to socio-technical
and human factors to ensure sustainable and meaningful integration of agricultural robotics into farming systems.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 141
www.rsisinternational.org
Figure 3: Conceptual Model of Agricultural Robotics Adoption
METHODOLOGY
This study applies a qualitative approach, specifically the Constructivist Grounded Theory (CGT) method, to
explore farmers’ perspectives on the adoption of chili harvesting robots in fertigation farming. The CGT
approach aligns with the interpretive research paradigm, which is rooted in the constructivist philosophy of
learning. This paradigm posits that individuals construct meaning through their lived experiences and
interactions, and thus emphasizes the subjective interpretation of reality rather than objective measurement
(Charmaz, 2014; Creswell & Poth, 2018). The choice of CGT allows the researcher to investigate how farmers
make sense of agricultural robotics, uncovering the underlying social and psychological processes that influence
technology adoption.
Qualitative research is particularly well-suited for this inquiry as it enables the examination of dynamic
complexities within agricultural settings and captures the richness of individual experiences (Flick, 2018).
Through CGT, the study does not seek to verify pre-existing theories but instead focuses on developing theory
grounded in empirical data (Glaser & Strauss, 1967; Charmaz, 2020).
Research Design and Sampling
Constructivist Grounded Theory (CGT), pioneered by Charmaz, emphasizes the co-construction of knowledge
between researcher and participants, recognizing the interpretive nature of data analysis (Charmaz & Thornberg,
2021). It offers a systematic yet flexible process to build theory from lived experiences and contextual meanings.
The process begins with open coding, where initial categories emerge from data such as interviews, observations,
or documents. This is followed by selective coding, in which categories are compared, refined, and integrated
through a constant comparison process until saturation is achieved. In the final stage, theoretical coding revisits
and connects core categories, enabling the development of a coherent theoretical framework that explains both
actions and meanings.
A central feature of this framework is theoretical sampling, where participants are selected not randomly but for
their potential to contribute to emerging categories and concepts. This iterative process—driven by ongoing
analysis—ensures that data collection continues until theoretical saturation is reached, when no new insights
appear (Corbin & Strauss, 2015; Saunders et al., 2018). Throughout these stages, memoing and diagramming
capture the researcher’s reflections, guide analysis, and bridge the transition from coding to writing the final
theory.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 142
www.rsisinternational.org
Figure 4: Constructivist Grounded Theory Framework
In this study, participants were farmers actively engaged in chili fertigation farming, particularly those with
exposure to or interest in agricultural automation and robotics. A total of 5 participants were interviewed,
comprising smallholder farmers from areas in Pontian Johor. This range of participants was chosen to ensure
diversity of perspectives across farm size, technical competence, and organizational structure.
Data Collection
Data were collected through semi-structured interviews and document analysis. The interviews allowed
participants to articulate their experiences, motivations, and concerns in adopting robotic harvesting
technologies. This approach also enabled the researcher to follow emergent leads and explore unexpected
insights raised by participants (Charmaz, 2014; Given, 2016). Interviews were conducted face-to-face and via
online platforms when necessary, with each lasting between 45–90 minutes. All interviews were audio-recorded
with consent and transcribed verbatim.
Document analysis included reports from agricultural agencies, robotics development initiatives, and policy
documents related to smart farming and automation in Malaysia. These secondary sources provided contextual
understanding and complemented farmers’ narratives (Bowen, 2009; O’Leary, 2021).
Data Analysis
Data analysis followed the Constructivist Grounded Theory coding procedures outlined by Charmaz (2014,
2020). This involved three stages:
1. Initial Coding – line-by-line coding to identify significant actions, meanings, and processes in farmers’
accounts.
2. Focused Coding – clustering the most significant and frequent initial codes into broader conceptual
categories.
3. Theoretical Coding – integrating and refining categories into a coherent framework that explains farmers’
perspectives on adopting chili harvesting robots.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 143
www.rsisinternational.org
Throughout the process, constant comparative analysis was employed, whereby data were continually compared
across interviews to refine categories and develop emerging theory (Bryant & Charmaz, 2019). Memos were
written throughout to capture analytic insights and guide further data collection.
Trustworthiness and Ethical Considerations
To enhance the trustworthiness of the study, several strategies were adopted. Credibility was ensured through
member checking, where selected participants reviewed their transcripts and emerging interpretations (Lincoln
& Guba, 1985; Nowell et al., 2017). Transferability was addressed by providing rich, thick descriptions of the
research context. Dependability and confirmability were supported by maintaining an audit trail of analytic
decisions and reflexive memos (Tracy, 2020).
Ethical approval was obtained from the host university’s ethics committee. Participants were provided with an
informed consent form explaining the study’s purpose, voluntary participation, and confidentiality of their
responses. Pseudonyms were used in reporting to protect participants’ identities.
RESULTS AND DISCUSSION
The Chili Padi Farm
Findings from the study indicate that most participating farmers cultivate chilies on a part-time basis, often
balancing farming with other sources of income. The farms studied were largely leased plots ranging from 1 to
5 acres, reflecting small-scale agricultural practices that cater to farmers’ socio-economic needs.
Leasing land was found to be both a practical and strategic choice for farmers. It provides access to farmland
without requiring substantial upfront capital investment, thereby lowering entry barriers for smallholders. For
some, leased land also functions as a space for short-term experimentation and expansion, especially in testing
new techniques such as fertigation or mechanized harvesting. However, reliance on leased land also means
farmers face insecurity and limited autonomy over long-term planning, particularly when landlords restrict
modifications to land use.
The small-scale size of farms (1–5 acres) offers several advantages such as lower operating costs, closer
monitoring of crops, and flexible management practices. Yet, these farms also face structural challenges,
including limited access to formal financing, bargaining power in markets, and economies of scale. These
constraints often influence the farmers’ risk appetite in adopting new technologies like chili harvesting robots.
Another notable feature of these farms is their location on uneven terrain, which complicates irrigation,
fertigation, and mechanization. Despite this challenge, uneven land also presents opportunities for the adoption
of sustainable farming practices, including contour farming, terracing, or integration of agroforestry systems.
Indeed, many farms in this study displayed a diverse arrangement of trees and intercropping systems, suggesting
that farmers already employ forms of agroecological practices. These systems not only support soil fertility and
pest regulation but also increase farm resilience against climate variability.
Taken together, the results suggest that cili padi farming in the study context is characterized by small-scale,
leased, and unevenly structured plots that rely on multi-cropping or agroforestry systems. Such farm
characteristics present both opportunities (low entry cost, ecological diversity) and constraints (market access,
land tenure insecurity, and mechanization challenges) that shape how farmers perceive and respond to
agricultural innovations, including robotic harvesting technologies.
Harvesting Process
The second theme identified from the study concerns the harvesting process, which is a labor-intensive and
recurrent activity. Farmers reported that chilies are typically harvested once or twice a week, depending on crop
maturity and market demand. Harvesting is usually performed either by the farm owners themselves or by hired
laborers, often at relatively low cost.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 144
www.rsisinternational.org
The process of harvesting, as described by participants, involves a series of interrelated tasks:
● Chili ripeness assessment – Farm workers evaluate the maturity of chilies, identifying those ready for
harvest while leaving others to ripen. This manual assessment requires skill and experience, particularly as
premature harvesting can reduce quality and market value.
● Damage inspection – Workers examine both chilies and plants for pest damage, disease symptoms, or
physical injury, which could affect overall yield and post-harvest quality.
● Weeding management – Weeds are removed during harvesting rounds to prevent competition for nutrients,
water, and light.
● Input application – Fertilizers and pesticides are sometimes applied concurrently with harvesting activities,
ensuring crop health and mitigating pest infestations.
● Transportation (Move) – Harvested chilies are gathered and moved to designated collection areas for
temporary storage or further processing.
● Grading and sorting – Chilies are graded based on size, shape, color, and overall quality, which directly
influences their market price. Farmers emphasized that grading is a crucial step in maintaining buyer trust
and accessing higher-value markets.
This multi-step process highlights that harvesting is not merely the act of collecting chilies but an integrated
management activity involving monitoring, maintenance, and quality control. Importantly, farmers
acknowledged that while the process is repetitive, it demands considerable labor input and attention to detail,
making it one of the most resource-intensive aspects of chili production.
The findings suggest that any attempt to introduce robotic harvesting technologies must take into account these
fine-grained and multi-dimensional tasks. For instance, automated systems must be capable not only of
identifying ripeness but also of detecting damaged produce, navigating uneven terrain, and integrating
seamlessly with existing grading practices. Farmers’ perspectives indicate both recognition of the potential labor
savings from robotics and skepticism about whether machines can effectively replicate the nuanced judgments
involved in chili harvesting.
Human Resource
The dataset describes human resource practices in small-scale chili and rice farming. Figure 4 illustrates the
Human Resource keywords and Themes Extraction.
Figure 5: Human Resource – Keywords and Theme Extraction
Keywords / Phrases from Interviewees
Initial Coding (Researcher Notes)
Extracted Theme
“We usually pay workers by the kilo they
harvest, cheaper that way” / “Payment is
based on how many kilos of chillies or rice
they can collect”
Piece-rate payment; l
inking wage to output rather than
hours
Low-cost labor
system
“We don’t hire full-time workers, only when
needed” / “Most of them come part-time,
especially during harvesting season”
Flexibility in contracts; temporary
workforce
Part-time
employment
“Harvesting is only on weekends” /
“Sometimes we call them every two weeks
depending on the crop”
Seasonal work; intermittent
scheduling
Irregular labor
scheduling
“Workers must know how to fertilize, spray
poison, and weed properly” / “If they don’t
Technical knowledge expected;
Skilled labor
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 145
www.rsisinternational.org
have experience, they slow down the work”
limited training offered
necessity
“Those who have planted chillies and rice
before are better” / “We prefer experienced
people who can do mapping themselves”
Reliance on past farming expertise
Experience-based
recruitment
● Labor is hired at a cheap price per kilo of chilies and rice: Farm owners commonly pay harvest workers on
a piece-rate basis (payment by weight/output), i.e., workers are remunerated according to the quantity
(kilograms) they harvest. This piece-rate arrangement reduces fixed wage commitments for farm owners
and links pay to productivity, but may also create income insecurity for laborers and incentivize speed over
quality.
● Labor on a part-time basis: Most labor engaged on these farms is not permanent; workers are hired on a
part-time or casual basis to perform seasonal or task-specific work (e.g., harvesting rounds, weeding,
fertigation rounds). This arrangement gives farmers flexibility to match labor input to crop cycles but limits
continuity of employment and on-the-job skill development for workers.
● Harvesting is on weekends and every two weeks: Harvest schedules are intermittent: many farms conduct
harvesting primarily on weekends and follow a roughly bi-weekly harvesting rhythm, reflecting labour
availability, crop maturity cycles, and market timing. Such scheduling minimises regular wage
commitments but can create bottlenecks when multiple plots mature simultaneously or when labour
becomes scarce. (This pattern was reported directly by participants in the field data.)
● Laborers need to be experienced with planting chillies and rice to be able to map the chillies that need to be
fertilized, harvested, insecticides applied, weeded and so on: Farmers emphasised that labourers must
possess practical, tacit knowledge of chilli and rice cultivation — the ability to “read” the field, identify
which patches need fertiliser or pesticide, distinguish ripeness stages, and prioritise which rows to harvest.
This experienced judgement is critical to operational efficiency and crop quality, and is not easily replaced
by short-term casual labour. The literature on tacit knowledge in agriculture supports the importance of
these local, experiential skills in farm decision-making.
These HR practices point to a labour system that is flexible and low-cost for farm owners yet dependent on
skilled, experienced casual workers. For technology adoption (e.g., harvesting robots), such labour arrangements
imply that farmers will evaluate robots not only for cost savings but also for their ability to replicate or
complement the tacit decision-making that experienced workers provide.
Costing and Financial
Participants described the financial setup and cost structure of small-scale chili padi farming as follows:
● Self-financing by entrepreneurs: Most farmers reported that they finance their chili padi operations from
personal savings, household income, or informal sources rather than formal bank loans. This reliance on
self-finance limits scale-up options and explains a cautious approach to high-cost investments. Studies on
smallholder finance show many farmers depend on personal or informal finance because formal credit
access is limited.
● Small capital: Entrepreneurs stated that establishing and running a chili padi farm requires relatively small
initial capital compared with many other agricultural enterprises. Start-up costs focus on inputs (seedlings,
media, fertiliser), basic irrigation/fertigation setup, and small equipment. Because capital outlay is modest,
chilli farming is attractive to smallholders seeking fast entry into horticulture. This aligns with
horticulture/value-chain literature which notes that short-duration vegetable/horticulture crops often require
lower entry capital and can be feasible for smallholders.
● Expect a quick return on capital: Farmers expect short payback periods due to fast crop cycles and regular
harvests (weeks to months rather than years). This quick-return expectation shapes investment decisions:
farmers favour inputs or technologies with rapid payback and are cautious about high-cost items that
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 146
www.rsisinternational.org
promise long-term but uncertain returns. Research on smallholder vegetable/horticulture systems similarly
documents the attraction of short-cycle crops for improving cash flow.
● Electricity bill, maintenance bill, etc.: Participants listed recurring operating expenses—particularly
electricity for pumps and fertigation systems, maintenance of equipment (drip lines, pumps, shade nets),
and replacement of inputs—as ongoing costs that must be budgeted. Studies of micro-irrigation and
smallholder technologies show that energy/electricity and maintenance are significant recurring costs
influencing technology adoption decisions.
● Labor costs, sales profit: Profitability was described as a balance between labour expenditure (piece-rate or
part-time wages) and sales revenue. Farmers monitor market prices closely because fluctuations in chilli
market prices directly affect profit margins. Maintaining low labour costs helps margins, but labour
shortages or rising wages can quickly erode profitability. Evidence from vegetable profitability studies
supports the centrality of labour and market channels to farm income.
Financial constraints and short payback expectations mean farmers favour low-capital, quick-return innovations.
For robotic solutions to be attractive, they must either (a) be low-cost or scalable in stages, (b) demonstrably
shorten payback periods, or (c) be subsidised/financed via accessible credit or value-chain arrangements.
Chili Padi Robot Issues
The findings reveal that while robotics in agriculture presents promising opportunities for efficiency and labour
reduction, smallholder farmers encounter multiple barriers and risks in adopting this technology. The issues are
summarised below:
● Cost of buying a robot: Robots are capital-intensive, often requiring substantial upfront investment that is
prohibitive for small-scale farmers. The high acquisition cost makes affordability a central barrier to
adoption, especially for self-financed farmers who expect short payback cycles. Studies confirm that high
capital requirements limit technology adoption in smallholder contexts.
● Maintenance (spare parts, oil, and others): Robots require consistent maintenance, including access to spare
parts, lubricants, and technical servicing. For rural or remote farms, the availability and cost of such services
can pose significant challenges. The literature highlights that maintenance and after-sales service
infrastructure are often overlooked barriers in agri-tech adoption.
Figure 6: Chili Padi Robot Issues – Keywords and Theme Extraction
Keywords / Phrases from Interviewees
Initial Coding (Researcher Notes)
Extracted Theme
“The robot is too expensive for us small
farmers” / “How can we buy it when the price
is higher than our yearly income?”
High upfront cost; affordability as a
barrier
Cost of acquisition
“If it breaks down, where do we get spare
parts?” / “Maintenance like oil and servicing
will be costly”
Continuous servicing; lack of local
spare parts
Maintenance
challenges
“We cannot keep the robot in our house, it
might get stolen” / “It needs a proper place for
storage away from rain and people”
Need for secure, weather-proof
storage
Storage and security
“We need proper training to know how to use
it” / “Older farmers may not understand the
system without courses”
Training requirement; generational
skill gap
Training
requirement
“The machine is too complicated to operate” /
Perception of complexity; fear of
Technological
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 147
www.rsisinternational.org
“If it is not user-friendly, farmers will avoid
it”
misuse
complexity
“Our farm has no strong internet connection” /
“Without stable internet, we cannot operate
the robot properly”
Digital infrastructure as prerequisite
Connectivity issues
“What happens if the battery runs out?” /
“Electricity costs and charging are another
burden”
Energy source dependency; limited
rural electricity
Power dependency
“It’s not easy to move the robot around the
farm” / “Transporting between plots will be
troublesome”
Limited portability; heavy design
Robot mobility
“Our land is uneven; the wheels might not
work here” / “Robots may not suit the rough
terrain we have”
Terrain-related limitations
Terrain
compatibility
● Storage and security (away from the farm operator’s residence): Farmers expressed concern over safely
storing robots when not in use. Since robots are high-value assets, risks of theft, vandalism, or weather-
related damage require proper storage infrastructure, which smallholder farms often lack. Security concerns
are frequently cited as deterrents to mechanisation in rural settings.
● Training for use: Effective use of agricultural robots requires farmers to undergo training to acquire new
technical skills. This involves additional time, cost, and willingness to learn, which can be a barrier for older
or less tech-savvy farmers. Evidence shows that inadequate training is one of the main reasons for under-
utilisation of agricultural technologies.
● Complexity of using robots: Farmers highlighted that robots may be technically complex, requiring higher
digital and mechanical literacy compared with traditional tools. This creates a steep learning curve and may
discourage adoption unless simplified interfaces and farmer support are provided.
● Internet access: Many robots rely on connectivity for data collection, monitoring, or remote control. In rural
farming contexts, limited or unreliable internet access poses a significant challenge. Research indicates that
digital infrastructure is a precondition for precision agriculture and robotics adoption.
● Power for the robot (battery or the like): Reliable power sources (e.g., rechargeable batteries, solar charging
stations) are essential for operating robots. Farms in areas with inconsistent electricity supply face additional
costs in securing reliable power. Sustainable energy solutions are therefore critical for long-term viability.
● Mobility of the robot: Farmers stressed the importance of moving robots easily across plots or between farm
sites. Bulky or poorly designed robots can create logistical challenges for small farms. Mobility
considerations are often underreported in robotics literature but are crucial for user acceptance.
● Robot wheels and terrain compatibility: Given that chilli farms often sit on uneven terrain, wheel design and
overall terrain compatibility are key concerns. Robots designed for flat, uniform fields may struggle in such
environments, limiting their usefulness. Recent studies on agricultural robots emphasise the importance of
adaptable designs that account for diverse field conditions.
These findings demonstrate that adoption of robotic solutions in small-scale chilli farming is not only a matter
of cost but also of technical, infrastructural, and socio-cultural readiness. For successful diffusion, robot
developers must consider affordability, durability, ease of training, and adaptability to uneven terrains typical of
smallholder farms. Policies that improve internet access, provide farmer training, and subsidise initial
investments could help overcome these barriers.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 148
www.rsisinternational.org
CONCLUSION
This study demonstrates that farming is not only a technical process but a deeply human-centered activity where
cultivation practices, financial considerations, and labor management intersect with technological change.
Farmers’ lived experiences reveal that any attempt to introduce chili padi harvesting robots in fertigation farming
must go beyond technical efficiency and explicitly address usability, affordability, and adaptability to local
contexts.
The findings highlight that humanising agricultural robotics requires a holistic integration of five key domains.
First, human resource management, where robots must complement the skills of hired labor rather than replace
them abruptly. Second, costing and financial planning, since affordability and return on investment are critical
for farmers operating on tight margins. Third, chili padi robot issues, where design must consider ease of use,
maintenance, and durability in farm conditions. Fourth, chili padi farming practices, such as fertigation systems,
soil and water management, and pest control, which must align with robotic functions. Finally, the harvesting
process, where robots must handle delicate chili padi fruits without compromising speed, accuracy, or quality.
From a constructivist grounded theory perspective, this study contributes by framing robot adoption not only as
a technological shift but as a socio-economic and cultural process shaped by these five domains. For innovators,
the lesson is clear: successful chili padi robots must be designed with farmers, not just for them. Prototyping
must integrate farmer feedback, cost models must reflect farmers’ financing realities, and training must empower
users to adopt robots confidently and sustainably.
Ultimately, this research moves the discussion beyond feasibility and into the realm of practical implementation,
offering critical insights for policymakers, technology developers, and researchers. By listening to and
prioritising farmers’ voices, innovation in chili padi fertigation farming can be humanised—ensuring that
robotics supports, rather than disrupts, the biological, financial, and social fabric of farming communities.
ACKNOWLEDGEMENT
The authors would like to express their sincere gratitude to Universiti Teknikal Malaysia Melaka (UTeM) for
the financial support provided through the Short-Term Research Grant. Appreciation is also extended to the
Faculty of Technology Management and Technopreneurship (FPTT), UTeM, for their continuous support and
encouragement throughout this study.
REFERENCES
1. Bac, C. W., Hemming, J., & Van Henten, E. J. (2014). Harvesting robots for high-value crops: State-of-
the-art review and challenges ahead. Journal of Field Robotics, 31(6), 888–911.
https://doi.org/10.1002/rob.21525
2. Bechar, A., & Vigneault, C. (2016). Agricultural robots for field operations: Concepts and components.
Biosystems Engineering, 149, 94–111. https://doi.org/10.1016/j.biosystemseng.2016.06.014
3. Bowen, G. A. (2009). Document analysis as a qualitative research method. Qualitative Research Journal,
9(2), 27–40. https://doi.org/10.3316/QRJ0902027
4. Bryant, A., & Charmaz, K. (Eds.). (2007). The SAGE handbook of grounded theory. Sage.
5. Bryant, A., & Charmaz, K. (Eds.). (2019). The SAGE handbook of current developments in grounded
theory. Sage.
6. Charmaz, K. (2006). Constructing grounded theory: A practical guide through qualitative analysis. Sage.
7. Charmaz, K. (2014). Constructing grounded theory (2nd ed.). Sage.
8. Charmaz, K. (2020). The genesis, grounds, and growth of constructivist grounded theory. In A. Bryant &
K. Charmaz (Eds.), The SAGE handbook of current developments in grounded theory (pp. 153–177). Sage.
9. Charmaz, K., & Thornberg, R. (2021). The pursuit of quality in grounded theory. Qualitative Research in
Psychology, 18(3), 305–327. https://doi.org/10.1080/14780887.2020.1780357
10. Cohen, L., Manion, L., & Morrison, K. (2007). Research methods in education (6th ed.). Routledge.
11. Corbin, J., & Strauss, A. (2008). Basics of qualitative research: Techniques and procedures for developing
grounded theory (3rd ed.). Sage.
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 149
www.rsisinternational.org
12. Corbin, J., & Strauss, A. (2015). Basics of qualitative research: Techniques and procedures for developing
grounded theory (4th ed.). Sage.
13. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five
approaches (4th ed.). Sage.
14. Eastwood, C., Klerkx, L., Ayre, M., & Dela Rue, B. (2019). Managing socio-ethical challenges in the
development of smart farming: From a fragmented to a comprehensive approach for responsible research
and innovation. Journal of Agricultural and Environmental Ethics, 32(5–6), 741–768.
https://doi.org/10.1007/s10806-017-9704-5
15. FAO. (2022). The state of food and agriculture 2022: Leveraging automation in agriculture for
transforming agrifood systems. FAO. https://doi.org/10.4060/cc2210en
16. Flick, U. (2018). An introduction to qualitative research (6th ed.). Sage. sssssssGeoJohor. Johor State
Government — Geoportal. Retrieved from https://geoportal.johor.gov.my
17. Given, L. M. (Ed.). (2016). 100 questions (and answers) about qualitative research. Sage.
18. Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative
research. Aldine.
19. Hafeez, M., Ahmad, T., & Anwar, A. (2022). Adoption of smart farming technologies in Asia: Challenges
and policy directions. Technological Forecasting and Social Change, 176, 121444.
shttps://doi.org/10.1016/j.techfore.2021.121444
20. Hoepfl, M. C. (1997). Choosing qualitative research: A primer for technology education researchers.
Journal of Technology Education, 9(1), 47–63. https://doi.org/10.21061/jte.v9i1.a.4
21. Kementerian Pertanian dan Keterjaminan Makanan. (2023). Perangkaan agro-makanan Malaysia 2023
[Malaysia agro-food statistics 2023]. Putrajaya: Author. Retrieved from
https://www.kpkm.gov.my/images/08-petak-informasi/penerbitan/perangkaan-agromakanan/Perangkaan-
Agromakanan-Malaysia-2023.pdf
22. Khazanah Research Institute. (2024). Understanding the landscape of agrifood smallholders in Malaysia:
Climate risks, sustainable standards, and gender gap. Kuala Lumpur: Khazanah Research Institute.
Retrieved from
https://www.krinstitute.org/assets/contentMS/img/template/editor/KRI%20Report_Understanding%20the
%20Landscape%20of%20Smallholders%20in%20Malaysia.pdf
23. Klerkx, L., & Rose, D. (2020). Dealing with the game-changing technologies of agriculture 4.0: How do
we manage diversity and responsibility in food system transition pathways? Global Food Security, 24,
100347. https://doi.org/10.1016/j.gfs.2019.100347
24. Klerkx, L., Jakku, E., & Labarthe, P. (2019). A review of social science on digital agriculture, smart
farming and agriculture 4.0: New contributions and a future research agenda. NJAS—Wageningen Journal
of Life Sciences, 90–91, 100315. https://doi.org/10.1016/j.njas.2019.100315
25. Lajoie-O’Malley, A., Bronson, K., van der Burg, S., & Klerkx, L. (2020). The future(s) of digital
agriculture and sustainable food systems: An analysis of high-level policy documents. Ecosystem Services,
45, 101183. https://doi.org/10.1016/j.ecoser.2020.101183
26. Liakos, K. G., et al. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
https://doi.org/10.3390/s18082674
27. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage.
28. Marshall, C., & Rossman, G. B. (2010). Designing qualitative research (5th ed.). Sage.
29. Neuman, W. L. (2006). Social research methods: Qualitative and quantitative approaches (6th ed.).
Pearson.
30. Nowell, L. S., Norris, J. M., White, D. E., & Moules, N. J. (2017). Thematic analysis: Striving to meet the
trustworthiness criteria. International Journal of Qualitative Methods, 16(1), 1–13.
https://doi.org/10.1177/1609406917733847
31. O’Leary, Z. (2021). The essential guide to doing your research project (4th ed.). Sage.
32. Rahman, M. S. (2020). The advantages and disadvantages of using qualitative and quantitative approaches
and methods in language “testing and assessment” research: A literature review. Journal of Education and
Learning, 6(1), 102–112. https://doi.org/10.5539/jel.v6n1p102
33. Rose, D. C., Wheeler, R., Winter, M., Lobley, M., & Chivers, C.-A. (2021). Responsible innovation in
agricultural robotics: Aligning technological development with the needs of farmers. Journal of
Agricultural and Environmental Ethics, 34(2), 231–249. https://doi.org/10.1007/s10806-020-09836-2
ICTMT 2025 | International Journal of Research and Innovation in Social Science (IJRISS)
ISSN: 2454-6186 | DOI: 10.47772/IJRISS
Special Issue | Volume IX Issue XXVIII November 2025
Page 150
www.rsisinternational.org
34. Shaharuddin, A., & Hashim, H. S. (2018). Agriculture in Johor: What’s left? Trends in Southeast Asia, 12.
Singapore: ISEAS–Yusof Ishak Institute. Retrieved from
https://www.researchgate.net/publication/327549005_Agriculture_in_Johor_What%27s_Left_Trends_in
_Southeast_Asia_192018_2018
35. Saunders, B., Sim, J., Kingstone, T., Baker, S., Waterfield, J., Bartlam, B., Burroughs, H., & Jinks, C.
(2018). Saturation in qualitative research: Exploring its conceptualization and operationalization. Quality
& Quantity, 52(4), 1893–1907. https://doi.org/10.1007/s11135-017-0574-8
36. Shamshiri, R. R., Weltzien, C., Hameed, I. A., et al. (2018). Research and development in agricultural
robotics: A perspective of digital farming. International Journal of Agricultural and Biological
Engineering, 11(4), 1–14. https://doi.org/10.25165/j.ijabe.20181104.4278
37. Temple, B., Edwards, R., & Alexander, C. (2006). Grasping at context: Cross language qualitative research
as secondary qualitative data analysis. Forum: Qualitative Social Research, 7(4), Article 10.
https://doi.org/10.17169/fqs-7.4.145
38. Tracy, S. J. (2020). Qualitative research methods: Collecting evidence, crafting analysis, communicating
impact (2nd ed.). Wiley-Blackwell.
39. Turnbull, D. (2002). Performance and appraisal: Research methods in context. Routledge.
40. Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of Things in agriculture, recent
advances and future challenges. Biosystems Engineering, 164, 31–48.
https://doi.org/10.1016/j.biosystemseng.2017.09.007
