FFTC Agricultural Policy Platform (FFTC-AP)

ABSTRACT

Smart farming technologies such as the Internet of Things, farm data management systems, automation, and digital decision-support tools are increasingly promoted as pathways to improve productivity and sustainability in high-value fruit systems. Evidence from a Malaysian durian farmer survey (n = 195) indicates that basic digital access is relatively common, with 149 surveyed farms reporting internet coverage, yet adoption of more advanced tools remains limited, with only 50 farms using a farm data management system and 31 farms reporting IoT use. Survey results also suggest that durian farming operations remain concentrated in manual work and semi-mechanization, while “smart farming” is confined to a small set of activities such as monitoring, irrigation, fertilization, and pest and disease management.  Non-adoption is driven primarily by structural constraints rather than rejection of innovation, with farmers reporting high technology cost (32%), limited knowledge of smart farming (19%), lack of opportunity to access technologies (16%), preference for traditional practice (16%), and infrastructure constraints including internet access (14%), while only 4% reported no interest. These findings imply that scaling smart durian farming in Malaysia requires coordinated interventions that reduce financial risk, strengthen digital capability, expand practical trial pathways, and improve rural connectivity. Policy alignment can be anchored under the National Agrofood Policy 2021–2030, which emphasizes a resilient and high-technology agrifood sector, complemented by financing instruments such as Agrobank’s Agro–PINTAS that explicitly support mechanization, automation, and digital technologies. Capability building can be accelerated through structured training and ecosystem programs such as MDEC’s Digital AgTech initiative, while infrastructure constraints require continued rural connectivity expansion under JENDELA Phase 2 initiatives.

Keywords: Smart farming, Durian orchards, Malaysian smallholder farmers, Technology adoption, Agricultural policy

INTRODUCTION

The "smart" technology is the intelligent and self-improving processes that allow other entities to be as smart as they are sentient with the least amount of human intervention. This paradigm, initially popularized through gadgets like smartphones, and now found its application in smart TVs, smart homes, smart vehicles, and smart cities, using advanced technology such as Radio Frequency Identification (RFID) sensors, Internet of Things (IoT), Big Data Analytics, and Machine Learning to improve the functionality and user experience, respectively, when using such technologies (Ahmed et al., 2020). These intelligent systems can solve daily problems more quickly and conveniently. Smart technologies have been crucial tools to better optimize the distribution and utilization of resources, assets, and services, and thus play an important role in urban management. Smart city initiatives, for instance, leverage various digital technologies for their solutions to urban planning, transportation, and resource management issues to improve urban life. As one of the key elements of a smart city model, smart mobility utilizes digitized solutions to meet the challenges connected to traffic congestion, infrastructure safety, environmental sustainability, and so on. These technologies are designed to optimize commuting and transportation, minimizing environmental impact and improving the functionality of urban efficiency (Ahmed et al., 2020).

Although the content in the summary above primarily examines smart technology adoption in urban mobility and city management, the principles and technological frameworks are transferable to other industries, such as agriculture. The same smart technologies that can be integrated into Malaysian durian farming will also encompass IoT, data analytics, and automation to improve productivity, manage resources, and enhance sustainability. The broader context of smart technology diffusion in Malaysia is therefore relevant to addressing the identified barriers and opportunities to the adoption among smallholder durian farmers.

Early explorations of digital transformation in farming indicate that adoption is not only a technical concern (devices, sensors, data, and connectivity) but also a socio-technical one (cost, skills, trust, and institutional support). Reviews and policy analyses repeatedly identify obstacles, including high initial and ongoing expenses, lack of accessible rural connectivity, gaps in skills for interpreting data, and uncertainty about benefits (U.S. Government Accountability Office, 2024; World Bank, 2025). These limitations are generally amplified for tropical perennial systems like durian, as large production cycles may delay the payback period and heighten the risk perception. Taken together, the literature reflects a common call for context-specific capacity building, participatory technology co-design, and delivery models (e.g., service-based tools and cooperative ownership) that minimize risk while increasing trust and perceived value.

METHODOLOGY

Study design and evidence base

This paper presents a desk-based narrative review and secondary synthesis of institutional evidence on smart durian farming in Malaysia. The manuscript consolidates adoption patterns, constraints, and policy implications, incorporating the findings reported by the Malaysian Agricultural Research and Development Institute (MARDI) and supporting policy and literature as cited in the article.

Data sources

The primary empirical evidence was collected through a structured questionnaire distributed to durian farmers from May to August 2025. The study targeted 65,349 registered durian farmers listed with the Department of Agriculture (DOA) Malaysia. Out of this population, 195 durian farmers participated in the survey, forming the final sample for analysis. The questionnaire was designed to gather information on various aspects, including the socio-demographic background of the farmers, their farm activities, and their use of technology in durian farming. Technology adoption was evaluated across several domains, such as the use of modern farming equipment, digital tools including mobile applications, sensors, and farm management systems, superior planting materials, precision input applications, and technologies utilized during post-harvest processes. To contextualize and interpret the MARDI evidence, additional sources referenced in the manuscript include, for example, national production statistics and government policy documents, and international reports discussing digital agriculture adoption barriers.

RESULT AND DISCUSSION

Level of technology adoption

In 2024, Malaysia had a harvested durian area of 63,966.83 ha and a total production of 568,806.84 metric tonnes (mt). The leading producing states among those identified were Johor (17,062.33 ha; 189,778.78 mt), Pahang (13,108.76 ha; 124,120.68 mt), and Kelantan (9,141.45 ha; 66,800.27 mt), reflecting their significance in national output (Department of Agriculture Malaysia, 2024). Currently, more than 90% of Malaysia’s durian production is consumed domestically. As depicted in Figure 1, 64% of the respondents are small-scale growers. In this paper, small durian farms are defined as having less than 3 hectares.

According to a survey finding, it can be seen that basic digital access is rather common among the durian farms surveyed, which are small farms in the major durian-growing regions in Peninsular and East Malaysia, where 149 farms had internet coverage. However, the uptake of advanced smart technologies is still low; only 50 farms operate a farm data management system, and 31 farms are using IoT technology. The farm adoption groups are approximately classified into “medium low” and “medium high,” indicating varying degrees of technology use. The majority of the sample consists of farms producing D197 (Musang King) type, local variety (Kampung), Duri hitam, D200, D24, and others. Collectively, the findings suggest that smallholders likely make up an important proportion of internet-using farmers, but they are underrepresented in the technology transfer, for example, in advanced digital technology adoption and Internet of Things (IoT) applications. This limitation signals a clear opportunity to enhance focused support and interventions for small durian farmers in order to increase technology adoption and enhance the management and productivity of the farms.

Farms activity that implements the use of smart farming (n=195)

In durian farm activities, the survey finds that technology is concentrated in semi-mechanization. The automation and smart farming remain limited and confined to a small set of operations (monitoring, irrigation, fertilization, and pest and disease management). This distribution supports evidence that adoption of advanced precision and smart agriculture is often limited by capital cost, operational complexity, and uneven access to enabling infrastructure and technical support (U.S. Government Accountability Office, 2024; World Bank, 2025). More practically, the results indicate that the majority of durian farmers are not implementing integrated “smart farming systems” but instead opt for discrete technologies, where benefits are clearer and operational disruption is comparatively low.

Table 1 presents the utilization of technology in durian farming for the activities of 195 farmers. Land preparation is rather semi-mechanized (129 farms) but is not automated, and smart farming is absent. This tendency indicates durian farmers are probably more inclined to implement mechanical services and equipment, such as tractors or contractor services for clearing, plowing, and drainage shaping, than digital systems. This is mainly due to the immediate labour and time savings mechanization yields, since mechanization does not require digital skills or continuous connectivity. Broad assessments of precision agriculture as a whole draw attention to the fact that mechanization tends to spread earlier than digitally enabled precision and automation, since the latter require higher investment, skills, and reliable support services.

Seedling preparation and propagation methods are predominantly manual (185 and 190 farms, respectively). The process does not include substantial semi-mechanization and automation, nor is smart farming reported. This finding is also in agreement with the artisanal nature of many nursery and propagation tasks in perennial fruit crops, heavily reliant on tacit knowledge and hands-on skill, for example, selection and grafting practices. Roadmaps of digital agriculture suggest that smallholder adoption in the early stage comes from field monitoring and input management areas, which provide more standardized workflows and clearer short-term returns and which are associated with early-stage adoption rather than nursery-focused tasks (World Bank, 2025). Planting remains predominantly manual (157 farms), with a smaller number reporting semi-mechanization (35 farms) and minimal automation or smart farming (1 and 2 farms, respectively). Orchard planting decisions are commonly shaped by site-specific constraints such as slope, drainage, spacing, and access routes, which encourage reliance on labor and simple tools. Where smart farming is reported in planting, it plausibly reflects foundational orchard digitization activities such as GPS-based mapping, geotagging, and digital record systems that create a data backbone for subsequent decision support (World Bank, 2025).

Smart monitoring is the activity in which smart farming appears most frequently (10 farms), while manual monitoring remains dominant (148 farms). This trend is durian-relevant in the sense that orchard monitoring entails a continual and labor-intensive activity (monitoring of flowering and fruit set, canopy conditions, and pest/disease symptoms), and digital tools can ease the burden of scouting and timing of interventions. Durian phenology evidence suggests that weather such as dry spells can stimulate flowering, thus underlining the agronomic appropriateness of systematic microclimate and weather monitoring to the farming decisions of orchard managers (Eguchi et al., 2025). As a result, monitoring is a logical stepping stone to smart farming, since it is linked to the crop's sensitivity to environmental timing as well as management responsiveness (Eguchi et al., 2025).

Irrigation has a mixed pattern: 69 farms reported manual irrigation, while fewer reported semi-mechanization (18 farms), automation (4 farms), and smart farming (4 farms). The presence of automation and smart irrigation conceptually satisfies the requirement for durian production as water management can be directly related to vegetative growth and fruit development and measured by the soil moisture and crop water requirement (Figure 2). There are a few studies that have focused specifically on durian that measured the irrigation practice based on crop water requirement, which supports the scientific basis for model- or sensor-based irrigation scheduling for a durian orchard (Fazlil Ilahi et al., 2024). Further evidence from an intelligent precision control study on a durian orchard shows how soil moisture targets and control protocols can be applied to stabilize field conditions on different irrigation occasions (Chatrabhuti et al., 2025). These sources explain why irrigation is one of the first durian farm operations for which intelligent technologies can reasonably provide value (Chatrabhuti et al., 2025; Fazlil Ilahi et al., 2024).

Fertilization remains predominantly manual (156 farms), but some mentions of automation and smart farming (6 farms each). In Malaysia, drones are increasingly being explored for fertilizer application in durian farming as a way to improve field efficiency, reduce labor dependence, and support more precise nutrient management (Figure 3). In durian orchards, especially those located on hilly or uneven terrain, drone-based application can help distribute liquid fertilizer or foliar nutrients more uniformly across tree canopies while reducing the time and physical effort required compared with manual spraying. This technology is also useful for targeted application, where farmers can apply fertilizer only to selected areas based on crop condition, tree growth, or nutrient deficiency symptoms. However, its effectiveness depends on proper calibration, suitable fertilizer formulation, weather conditions, operator skill, and the cost of drone services or ownership. The significance of this finding is evident when considering that nutrient management for durian is too expense-heavy and time-sensitive, and improper fertilizer use can increase production costs without improving yield. In theory, digital decision-support tools can assist in optimizing fertilizer time and dose decisions in fertilizer applications by combining soil and plant status with yield values based on soil and plant state. However, policy and roadmap documents emphasize that technical feasibility in and of itself is not sufficient to uptake; affordability, ease of use, training, and ongoing advisory support continue to be key drivers of acceptance (World Bank, 2025).

For pest and disease management, this is arguably the strongest shift away from purely manual practice, with 100 farms using manual approaches and 85 farming semi-mechanized with limited automation (1 farm) and smart farming reported (9 farms). This distribution aligns with the high economic impact of pest and disease damage in durian, with reduced grade and income due to quality loss. Drone-based orchard survey and spraying workflows have been implemented in durian settings, which may create the perception among farmers of a “smart” crop based on improved targeting and reduced labor exposure, evidenced in the form of practice-oriented documentation. More recent assessments have also noted that scaling technologies does require a service ecosystem, technical support, and capacity building rather than one-off deployment of equipment.

Pruning and harvesting are still largely manual or semi-mechanized, with no automation or smart farming reported. This is possible because pruning is judgment-intensive and context-specific; harvesting in tall tree crops is hard to automate owing to the complexity of the canopy, fruit-handling risk, and safety limitations. Policy evaluations on precision agriculture also illustrate that automation is least likely to diffuse in physically complex, highly variable, and difficult-to-standardize operations, especially in smallholder contexts (U.S. Government Accountability Office, 2024). The findings of the survey demonstrate a pragmatic adoption trajectory in durian farming; smart technologies are most likely to emerge first in monitoring, irrigation, nutrient management, and pest/disease management compared to more complex manual dexterity and safety-based operations (World Bank, 2025).

Table 1. Detailed findings from durian farmers on technology use across farm activities (n = 195)

Source: Survey data (2025)

Issues concerning smart farming adoption in durian cultivation

The durian farmers’ survey in Malaysia shows that the failure to adopt smart farming technology is determined by practical limitations in the Malaysian durian production environment, rather than a refusal to innovate. The most frequently reported barrier was the issue of high cost (32%), followed by limited knowledge of smart farming (19%), “no opportunity” (16%), preference for traditional practice (16%), and issues with infrastructure like internet access (14%), while only a small minority reported no interest (4%) (Figure 4). The relatively low “not interested” share also indicates that many Malaysian durian farmers are not formally rejecting technology as such; rather, adoption is being hindered by factors such as affordability, capability, access pathways, and enabling infrastructure barriers that have been consistently spotlighted in digital agriculture assessments and technology adoption reviews.

High cost of technology (32%) is the most obvious constraint and is especially credible in a durian context like Malaysia, where the orchard market is typically operated by medium- and small-sized operators with cash flow issues and increasing input costs. Smart farming often involves upfront investment and ongoing costs for sensors, gateways, subscriptions, repairs and maintenance, and connectivity to data. It may lead to uncertainty about returns and slower realization over time, particularly when a technology is introduced without an integrated advisory service (World Bank, 2025). In the context of durian production, this issue can be illustrated through an Internet of Things (IoT)-based orchard monitoring system comprising soil moisture sensors, a microclimate station, and a mobile dashboard. Such a system may enable growers to optimize irrigation timing and improve situational awareness regarding potential disease risks. However, when the financial burden of purchasing the equipment, replacing components, and maintaining the service is borne entirely by the individual grower, rather than distributed through cooperative ownership arrangements or service-based delivery models, adoption may become economically challenging. This is particularly relevant for smallholders, for whom the private costs of digital technology adoption may outweigh the perceived short-term benefits. Further evidence from global precision agriculture evaluations suggests that high acquisition and operating costs are an impediment and recurring obstacle to adoption when the benefits cannot be easily assessed ex ante (U.S. Government Accountability Office, 2024).

A low percentage of farmers (19%) who are knowledgeable about smart farming indicates that the digital divide remains prevalent. This is particularly important because most durian cultivation areas in Malaysia are located in rural agricultural zones, where access to digital infrastructure, training, and technical support may be limited. Assessments of digital-age agriculture have highlighted that low levels of technology literacy and disparities in digital infrastructure can restrict the uptake and meaningful use of digital devices (U.S. Government Accountability Office, 2024; World Bank, 2025). So, in durian production, this barrier isn’t just “awareness”, it is about the ability to respond to data like soil moisture readings, rainfall patterns, disease risk indicators, etc. An example is sensor- or weather-informed orchard management. The farmer must also possess sufficient knowledge to translate dashboard alerts into practical management decisions, such as adjusting irrigation volumes, improving drainage, or planning preventive treatments. This learning burden may be intensified by Malaysia’s disease pressure in durian production, where numerous pathogens, including Phytophthora palmivora, have been reported to cause substantial orchard losses. In this context, IoT-generated data may have limited value if farmers lack the technical capacity to interpret alerts accurately and respond in a timely manner, as delayed or inappropriate action could increase both crop losses and disease-management costs (Chong et al., 2024).

The 16% “no opportunity” response is particularly important because it points to an ecosystem-level constraint rather than a purely individual adoption barrier. These farmers are not necessarily rejecting smart technologies; rather, they may be excluded from the practical channels through which such technologies can be observed, tested, purchased, serviced, or supported. In this regard, the absence of demonstration opportunities, dependable vendors, affordable service models, and extension follow-up may restrict adoption even when farmers recognize the potential benefits of digital tools. This aligns with digital agriculture studies showing that adoption requires an enabling ecosystem of service delivery, institutional coordination, and implementation support, rather than the mere availability of suitable devices or hardware (World Bank, 2025). A durian-specific constraint is the limited availability of local technology-as-a-service models in some producing districts, such as subscription-based orchard monitoring, contracted drone scouting, or bundled advisory-and-equipment packages. The absence of such service models may reduce trialability, increase perceived financial and operational risk, and make adoption more difficult for farmers who are unwilling or unable to invest in full ownership of smart farming technologies. Recent government–private sector smart farming initiatives in Malaysia, including partnerships linked to the MyDIGITAL agenda, suggest that institutional coordination can play an important role in supporting technology uptake. However, such coordinated delivery mechanisms do not yet appear to be widely accessible across all fruit-producing regions, which may limit the diffusion of smart agriculture among durian growers (MyDIGITAL, 2025).

Preference for conventional practice (16%) must therefore be perceived as risk management in the context of the Malaysian durian production reality. Not as a matter of simple cultural resistance alone, this preference should indeed be recognized. Durian is a highly sought crop characterized by reliance on income from fruit quality and orchard health. In general, local practices have been widely used and proven successful locally for the coming seasons, but farmers are wary of changing their habits to new techniques that may not work, particularly if support and repair services are not well planned or delivered. This may be more rational in Malaysia, where the development of durian systems may become less suitable in some cases due to the fact that some durian diseases are reported to grow and be late noticeable in Malaysia. In this context, trial may initially be perceived negatively (Chong et al., 2024). For example, resistance to using digital warnings as disease-prevention tools is evident when outbreaks (stem canker and root rot) are consistently reduced through community visits compared with known orchard practices in Malaysia. Malaysia-based studies specifically stress the requirement for improved surveillance and predictive capacities in relation to durian stem canker and soil-associated disease risk, explaining why farmers need to have good local proof of disease prevention before a change in routine is required (Chong et al., 2024).

Infrastructure constraints and internet connectivity (14%) illustrate Malaysia’s rural connectivity gaps, particularly critical, as a significant proportion of durian orchards are located in non-urban locations where connectivity quality may not be reliable. For example, it has been reported that there are national reports on Malaysia’s rural connectivity initiatives as part of the National Digital Network Plan (JENDELA) and related programs for its coverage and infrastructure to develop nationwide (both targets and rollout measures) (Bernama, 2025). An example in the durian context is cloud-dependent monitoring systems, which require continuous connectivity to synchronize sensor readings and trigger real-time alerts. Poor coverage can lead to gaps, missed alarms or loss of warnings leading to premature termination. Rural digitalization-related discussions in Malaysia also highlight that barriers to adoption are not only technical accessibility but also the quality and security of access for rural communities (U.S. Government Accountability Office, 2024; World Bank, 2025). These limitations indicate that scalable interventions in Malaysian durian orchards should prioritize low-bandwidth, offline-capable solutions, such as buffered data logging, SMS alerts, and periodic synchronization. They should also be supported by robust local support ecosystems, rather than relying on indiscriminate, universal broadband deployment.

In general, the survey of durian farmers in Malaysia shows that only limited smart farming adoption will come to fruition unless such measures are directed toward affordability, capacity development, access channels, and the reliability of rural infrastructure. Awareness alone is unlikely to boost adoption, as the data show that constraints, rather than disinterest, are the biggest deterrent. Policy recommendations that integrate facilitating infrastructure (e.g., rural connectivity improvements under JENDELA), smart-farming partnerships such as coordinating smart farming projects (e.g., MyDIGITAL-linked partnerships), and models of delivery (service-based tools, cooperative ownership, and locally based training), are more in tune with the actual constraints reported by Malaysian durian farmers versus the strategy of device-only distribution (Bernama, 2025; MyDIGITAL, 2025; World Bank, 2025).

Technology adoption to be more feasible among small-scale rural communities.

Improving data governance and creating trust

Regulatory and organizational barriers, including issues of data management and ownership, also pose substantial barriers to technology adoption. Farmers’ fears about data privacy and little on the ground about how to use farm data and who controls it make them wary of regulators and technology providers. To help combat this problem, open data policies should be adopted to uphold farmers’ right to see for themselves. Open data initiatives, however, need to be matched by clear data governance frameworks that specify how data will be used, who owns it, and why technology providers should be held to account. Informing and involving farmers in the design of these policies would build trust and promote more engagement with digital platforms overall. Open and responsible data management are essential if we are to allay concerns and gain the confidence to adopt widely.

Overcoming socio-cultural and educational barriers

Socio-cultural factors, including demographic and educational challenges, are among the most persistent barriers to smart technology adoption. The strategies on the road to overcoming these hindrances should aim to sensitize the focus of education and capacity building for smallholder durian farmers. Accessible, hands-on, and context-relevant training programs serve to close knowledge gaps and demystify digital technologies. The use of local champions or peer organizations to communicate information or disseminate success stories can also help raise perceptions of the relevance and accessibility of smart technologies. Overcoming socio-cultural barriers to technology use in remote areas is an ongoing effort, which requires an active approach, involving farmers in the economic ecosystem and enhancing their participation and use of the technology, for the development that is not only available but easily accessible to and usable by a wide range of rural communities.

Developing technical infrastructure and connectivity

Technical barriers, such as poor connectivity in rural areas, remain a major obstacle to smart technology deployment. In order to tackle this huge gap, solutions must also focus on the scale and upgrading of digital infrastructure, from internet access and mobile networks, so that smallholder farmers can leverage all the digital capabilities. Public-private collaboration and targeted investment by governments in rural connectivity can be critical in bridging the digital divide. Further, the availability of technologies resistant to low-bandwidth systems or operated offline and able to synchronize data when connectivity is available could enhance the uptake among farmers living in more rural or underserved areas.

Leveraging market and policy drivers

Economically, when effectively communicated, barriers are subtle, but economic drivers like the prospect of better production control, optimization, and planning can lead to adoption. Policy frameworks designed to encourage sustainable practices, such as precision agriculture for reduced fertilizer inputs and blockchain technology for traceability, can drive market demand and technology adoption. The drivers could be built around dedicated subsidies, certification schemes, or mechanisms or market access programs that reward farmers on their implementation of smart technologies that promote environmental sustainability and transparency. This may be achieved by aligning incentives at a policy level with the market environment and farmers' rights, as it may result in a more conducive situation for the adoption of digital agriculture solutions.

Policy and institutional interventions

Malaysian government intervention can increase the number of durian farmers adopting smart farming technology by addressing the structural bottlenecks in cost, skills, access to opportunities, and infrastructure identified in the survey through a holistic policy package that supports innovation rather than fragmented pilot projects. At the national level, the National Agrofood Policy 2021–2030 (NAP 2.0) explicitly presents the “modernization and smart agriculture” agenda as a strategic thrust in addition to supporting “equipping the enabling ecosystem” (finance, infrastructure, investment, governance, and talent development), which forms “a policy umbrella to ensure continued public investment into pathways to digital agriculture” (Ministry of Agriculture and Food Security, 2021).

Cost remains the most obvious barrier in the survey, and public intervention should mitigate the risk of adoption via blended financing and selective incentives. There are already publicly linked agricultural financing systems that could be developed for durian orchard technology, including soil moisture sensing, smart irrigation/fertigation, drone services, and basic orchard digitization. Agrobank’s Agro-PINTAS programme is positioned as a financing scheme for innovation and agrotechnology, including mechanisation, automation, precision farming, and digital technologies. Its provision of financing margins of up to the full investment cost, defined repayment tenures, and possible deferment features is particularly relevant to orchard investments, where returns may only be realized over a longer production cycle (Agrobank, 2025). In the context of durian production, such financing could be structured around integrated adoption packages that combine: (i) a basic orchard monitoring kit, such as soil moisture, rainfall, or microclimate sensors; (ii) simple automation tools, such as timers and irrigation valves; and (iii) advisory support for interpreting data and implementing management responses. However, financing should not be limited to hardware acquisition alone. Adoption is likely to weaken when farmers are provided with equipment without the technical capacity, maintenance arrangements, and continuity of service required to convert digital tools into effective orchard management practices (World Bank, 2025).

Capacity building constitutes the second primary lever, as the “limited knowledge” identified in the survey shows that many durian farmers are unable to be familiar with or even interpret digital tools. There is a role for government in boosting uptake through expanded guided training, technology validation, and advisory support, leveraging proven national initiatives. Malaysia Digital Economy Corporation's Digital AgTech initiative is clearly designed to focus on training, consulting, transformation schemes, and ecosystem engagement, and identifies technology domains that mirror the needs of durian orchards (e.g., smart irrigation / smart soil monitoring / smart fertigation / smart pest control/drone technology / smart geo mapping) directly (Malaysia Digital Economy Corporation, 2022). An example of durian-specific implementation is “orchard decision training,” where farmers learn to transform soil moisture and rainfall records into irrigation and management planning, to use geo-mapping to track tree-linked problems, and to apply pest and disease interventions based on weather timing through extension services and qualified trainers and not one-off workshops.

Barriers of access, described as “no opportunity” in this survey, need policies that give workers clear avenues for testing, checking, and receiving services. The adoption by Malaysian durian farmers will be facilitated by providing technology as a service model and demonstration ecosystem and not the assumption that most durians should buy and maintain a full stack. Government-funded public–private partnerships could be the anchor in this strategy by developing proof-of-concept environments, testing technologies, and scaling service delivery models. A national case in point of this approach is the partnership MyDIGITAL–MADA–Toshiba for data-driven farming that unites advanced weather forecasts with on-farm stations for timing of pesticide and fertilizer applications, and also to develop yield prediction skills (MyDIGITAL, 2025). While the example initiative is focused on paddy, the policy template generalizes to durian with orchard-focused pilots, which show concrete results and are then scalable to district-level service providers delivering monitoring and decision support for them as a subscription or pay-per-use rather than a capital purchase model (MyDIGITAL, 2025; World Bank, 2025).

Infrastructure upgrade is a condition, as smart orchard systems tend to deteriorate in weak rural connectivity, and Malaysia has a national program explicitly addressing this issue. Phase 2 of the National Digital Network Plan (JENDELA) is an initiative, noted in official communication by the Malaysian Communications and Multimedia Commission, which aims to enhance high-speed internet service in rural areas and to provide infrastructure such as Points of Presence, and its broader reach is aligned with the wider-reaching national agenda to reach the digital frontier of rural areas (Bernama, 2025). Examples relevant to the Durian context include support for low-bandwidth, offline-capable advisory tools that maintain offline capacity and sustain low-signal operation during signal failure periods, while JENDELA investments incrementally develop the baseline coverage of orchard districts. This would minimise the amount of system abandonment that occurs when cloud-based platforms are no longer capable of reliably syncing data in the field (Bernama, 2025; World Bank, 2025).

CONCLUSION

The implementation of smart technology in Malaysian durian farming is hindered by economic, educational, infrastructural, and policy barriers. High investment costs, uncertain cost-effectiveness, limited digital literacy, and weak rural infrastructure, particularly internet connectivity, continue to restrict adoption among smallholder farmers. In addition, the lack of accessible local technology-as-a-service, such as drone scouting, orchard monitoring, and advisory support, limits farmers’ ability to test and use smart farming tools with confidence. These barriers reduce opportunities for productivity improvement and may affect the long-term sustainability of durian farming. Therefore, stronger policy and institutional support are needed through strategic frameworks, knowledge transfer, improved infrastructure, better data governance, and more accessible technology-as-a-service models. A coordinated, multi-sector effort is essential to help Malaysian durian farmers benefit from smart technology and to support the sector's future productivity and sustainability.

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