Hybrid Intersection: Navigating Context and Constraint in AI for Social Good Among Thailand’s Smallholder Farmers

Abstract

Artificial intelligence is increasingly deployed as a vehicle for “social good” in agriculture, ostensibly advancing the UN Sustainable Development Goals whilst uplifting smallholders. This study examines how such claims materialise through a selective case study analysis of eleven Thai Agricultural AI providers, analysing governance practices and impact framing. The research develops the “hybrid intersection” concept as an analytical lens for understanding how Agricultural AI simultaneously delivers genuine social benefits whilst reproducing structural constraints that limit transformative change. Findings reveal that “social good” becomes operationalised primarily through economic gains, reflecting farmers’ immediate financial predicament and market-driven innovation constraints. Governance practices prioritise functional trust over procedural safeguards, reflecting institutional pressures to demonstrate immediate value. The study reveals two systemic tensions: data commodification models enabling free farmer access whilst extracting behavioural surplus for third-party monetisation, and market optimisation approaches delivering incremental improvements whilst leaving structural challenges unaddressed. Thailand’s Agricultural AI landscape thus embodies a “hybrid intersection” where genuine social good coexists with constrained transformation, providing analytical tools for understanding similar patterns in other Southern contexts.

1. Introduction

Artificial intelligence promises prosperity, yet its benefits and burdens are unevenly distributed along geopolitical and socio-economic fault lines. Much of that unevenness is captured by what scholars now call “the South” [1]—no longer a mere geographic tag for developing states but a symbolic shorthand for sites of structural subordination and resistance [2]. The term now spans post-colonial nationhood, marginalised groups within wealthy economies, and transnational movements contesting data colonialism and ecological extraction [3,4,5]. In the current scramble for AI supremacy, the South matters because narratives of catch-up modernisation collide with demands for epistemic, digital, and socio-environmental justice.

This collision produces what I term the hybrid intersection—the juncture where competing notions of “good” in AI meet Southern contexts. Section 1.1 and Section 1.2 unpack the concept, but, in brief, the hybrid intersection lies between a traditional view of AI as a growth engine and a transformative view of AI as emancipatory. In this contested ground, market-centred deployments deliver short-term gains to Southern constituencies (e.g., higher yields, faster credit) yet often re-inscribe vulnerabilities: inequitable data ownership, opaque algorithmic governance and ecological goals deferred to an unspecified future.

These global debates set the analytical frame for this study: agricultural AI in Thailand is not merely a bundle of tools but a socio-technical arena in which competing visions of “good” are negotiated. By foregrounding the concept of the hybrid intersection, I ask how market-centred AI deployments deliver immediate economic relief yet simultaneously reproduce the very structures that constrain smallholders.

1.1. Conceptual Framework

The notions of “good” and “South” are neither static nor universal. Debates on technology for good reveal as much about what is omitted as what is advocated [6,7]. Ethical language—fairness, transparency, accountability—often leaves the political economy of AI under-examined [7]. The proliferation of labels such as responsible AI [8], AI for social good [9] and AI for people [10] underscores both interest and conceptual complexity. Cultural relativity is also pivotal: Birhane [11] and Robert et al. [12] show that practices deemed beneficial in one context may be irrelevant—or even harmful—in another. China’s social credit scoring versus the EU’s prohibition illustrates this divergence.

In tandem, the connotation of South has been reframed by Santos [2], from once referred simply to as post-colonial, developing nations to a collective symbol of “the different, the underprivileged, the alternative, the resistant, the invisible, and the subversive” [3]. Many preceding decolonial AI scholars use this reframe as an anchor of their arguments [1,13]. This expanded view includes marginalised groups in the Global North and highlights disparities even within the South, such as between billionaires and impoverished communities [1]. Png [4] crystalises this complexity with the phrase “economic Souths in the geographic North and Norths in the geographic South.”

Together, these debates suggest that “good” and “South” intersect in multiple, historically contingent ways. To analyse how such intersections materialise in contemporary AI deployments—particularly in agriculture—I adopt the Intersection Thesis developed in my doctoral research.

• Traditional Intersection—The South is equated with developing nations, and “good” in AI is framed as accelerated economic development and national competitiveness.
• Hybrid Intersection—The South extends to marginalised populations embedded in global capitalism. Here, “good” in AI focuses on mitigating societal challenges, often articulated through the UN Sustainable Development Goals (SDGs).
• Transformative Intersection—The South becomes a locus of resistance to the status quo; “good” in AI is re-imagined as emancipation from the very power structures that shape AI’s design and deployment.

These intersections do not succeed one another in a neat chronology; instead, they co-exist and overlap, each privileging different values, priorities, and political economies. A shift from the traditional to the transformative is neither automatic nor uncontested; rather, it is negotiated through ongoing struggles over what counts as “good” in AI and for whom.

1.2. The Significance of Hybrid Intersection

The concept of the hybrid intersection is vital for contemporary AI governance because it captures the ongoing transition in AI discourses and practices—from a deterministic, technology-driven perspective towards a more socio-technically nuanced and human-centred framework. At this intersection, AI is neither perceived merely as an uncontrollable, complex technology nor as a neutral tool devoid of socio-political implications [14]. Rather, it is acknowledged explicitly as a socio-technical system—a product intentionally shaped by human design, embedded with specific cultural, institutional, and ethical values [15,16]. Recognising AI’s inherent human intentionality shifts the discourse away from passive acceptance of technological determinism [15,17] towards a proactive human-centred approach, emphasising that AI can and should be explicitly directed for societal benefit [18,19].

The term “hybrid” is inspired from the Three Horizons Framework [20] and Schiff’s [21] classification of STI (science, technology, and innovation) policies. The Three Horizons Framework has an intermediary phase (H2) where there is contestability and ambiguity in policy/goal transition, making a shift in perspective genuine yet provisional. Conversely, Shiff’s classification underscores the “hybrid” model—where transformative objectives are negotiated within traditional policy and incentive structures. Therefore, the hybrid intersection is an intermediary phase that incorporates “growth with governance and mission”. Thus, the concept of “good” revolves around creating opportunities for the Southern population to benefit from technological advancements. While this approach addresses immediate needs and integrates these groups into the broader socio-economic fabric, it often lacks the emancipatory impact to challenge the structural features and value systems that sustain the South’s deprivation condition.

To demonstrate and contextualise this remark, this article examines how the framing of “social good” and the context driving Agricultural AI applications in Thailand may fall short of achieving true prosperity for Thai farmers due to the prevailing focus on immediate financial gains at the expense of a broader agenda encompassing environmental sustainability and systemic reform. This narrow framing not only limits the transformative potential of AI but also entrenches the existing political economy, leaving deeper structural issues unresolved [22].

This research critically addresses an essential gap in current scholarship concerning how the concept of “AI for social good” is framed and enacted within Southern context. While AI-based agricultural innovations are frequently positioned as contributors to sustainable development and farmer empowerment, such claims often remain narrowly economic in scope, neglecting broader social, ethical, and ecological dimensions. This study is significant precisely because it interrogates how Agricultural AI in Thailand is frequently trapped within a “hybrid intersection”—a contested middle-ground characterised by incremental improvements and immediate economic relief but limited in achieving deeper structural transformation in power shifting.

This article contributes to scholarship on AI for social good by advancing the “hybrid intersection” as an analytical lens to examine the entrenchment of market logics within ostensibly transformative technologies. By doing so, it problematises simplistic assumptions that AI innovations inherently yield emancipatory outcomes, particularly in Southern contexts marked by infrastructural, institutional, and epistemic asymmetries.

1.3. Research Outline

The article proceeds as follows. Section 2, Section 3 and Section 4 provide the literature review and context: Section 2 introduces Agricultural AI; Section 3 dissects two notions of good—good governance and social good; and Section 4 details the precarities of Thai smallholders. Section 5 describes the research design and methods. Section 6 and Section 7 present empirical findings on the agricultural AI’s value chain and their underpinned business models; Section 8 and Section 9 assess governance practices and social-good impacts. Section 10 synthesises two systemic tensions—data commodification vs. social-good delivery, and market optimisation vs. structural reform—illustrating the constraints of the hybrid intersection. Section 11 concludes with policy and research implications.

2. AI in Agriculture

In recent years, authoritative frameworks such as the OECD [23] and the EU’s AI Act have sought to provide more precise definitions of AI. According to these definitions, AI refers to machine-based systems that process inputs to infer outputs such as predictions, recommendations, content generation, or decisions. These outputs, in turn, influence physical or virtual environments, with AI systems demonstrating various degrees of autonomy and adaptiveness throughout their deployment lifecycle. This dynamic capability allows AI to continuously evolve and optimise its performance in response to new data and conditions, making it an invaluable tool for addressing complex challenges in modern society. A compelling example of this transformative potential can be found in agriculture, where AI is driving the transition to the Agriculture 4.0 paradigm [24]. This paradigm shift is characterised by the integration of advanced science, digital technologies, and data-driven approaches into agricultural practices [24].

2.1. Drivers of Agricultural AI

The use of AI in agriculture embodies the transition onto the Agriculture 4.0 paradigm, which integrates advanced science, digital technologies, and data-driven approaches into agricultural practices. Reflecting the ethos of the “green revolution”, the transformation of agriculture is driven by the urgent need to scale up the global food supply to meet the demands of a rapidly growing population while simultaneously reducing inputs, minimising environmental impacts, and addressing critical issues such as climate change and food waste. We stand at a pivotal juncture where escalating demands and environmental constraints necessitate a fundamental shift in agricultural practices. By 2050, global food production will need to increase by 50% to feed a projected population of 10 billion [25,26]. Yet, this challenge is compounded by the declining fertility of agricultural lands, with approximately 34% of global agricultural land now degraded due to unsustainable farming methods [27]. Furthermore, agricultural activities have doubled greenhouse gas emissions over the past half-century, intensifying droughts, floods, and other climatic disruptions [24]. These changes have profound implications for agricultural yields, food quality, and accessibility, particularly for marginalised households.

In response to these challenges, Agricultural AI is increasingly framed as a form of AI for “social good”, positioned as a transformative tool to address these global issues. Among its most recognisable applications is precision agriculture (PA), or smart farming, which entails precisely monitoring, measuring, and controlling farming inputs—such as water, fertiliser, and pesticides—to meet the specific needs of crops, animals, or soil conditions [28]. By leveraging real-time data and automation, PA enhances resource efficiency, boosts productivity, and reduces environmental impact. However, the potential of Agricultural AI extends far beyond smart farming. It is increasingly employed in diverse areas [29], including crop quality inspection, determining credit scores based on past yields, and monitoring greenhouse emission. These applications demonstrate the expansive role of AI in reshaping the agricultural landscape. To capture the breadth of these innovations, the term “Agricultural AI” will be used as a collective reference for all AI technologies deployed within the agricultural sector.

Hackfort [30] notes that Agricultural AI encompasses both digitisation—the technical process of converting analogue information into digital data—and digitalisation, the broader societal process of integrating and embedding digital technologies into everyday practices, workflows, and systems to transform how society operates [31]. The digitisation of agriculture begins with collecting extensive data on various aspects of farming, including production metrics, environmental conditions, and machinery operations. These data are gathered from diverse sources such as wireless sensor networks, internet-connected weather stations, surveillance cameras, drones, and historical records. Once collected, the data are used to develop advanced analytics tools that facilitate more informed decision-making and enable automated actions (Figure 1). However, the digitalisation of agriculture extends beyond the technical process, requiring social configurations that ensure the acceptance and integration of these technologies within agricultural communities. As Bronson [32] observed, the appeal of precision agriculture lies in its promise to address farmers’ dual concerns about crop productivity and environmental stewardship. This concept, called “sustainable productivism”, presents PA as a “win-win” solution, allowing farmers to fulfil their roles as environmental stewards while maintaining or enhancing profitability.

2.2. Concerns over Agricultural AI

Critical scholars view Agricultural AI, and by extension, Agriculture 4.0’s technologies, as a “technofix” for addressing food insecurity, environmental degradation, and climate change [34]. Weinberg [35] defines technofixes as solutions that reconfigure, obscure, postpone, or even exacerbate underlying problems and trade-offs. These technological solutions depoliticise critical global challenges such as hunger, climate change, and poverty, detracting from the structural reforms necessary to address these issues at their core [36]. Precision agriculture, for example, extend “surveillance capitalism” [37] into rural frontiers: technology firms gain proprietary control over data, seeds, and algorithms, while financial institutions use these data flows to deepen agricultural financialisation [38,39]. Such arrangements may raise yields and farm income, but they also recast smallholders as data suppliers with little leverage, entrenching the power imbalance between farmers and ATPs [40] that this study later labels the data-for-profit tension within the hybrid intersection.

3. Notions of Good

The notion of good is notoriously elastic; across disciplines it spans incremental efficiency gains in computer science to structural emancipation in social justice. Following Green [6], I distinguish two complementary registers:

• Procedural good—concerned with how an AI system is built and governed.
• Substantive good—concerned with what that system achieves for society and the planet.

The remainder of this section aligns procedural good with good governance and substantive good with AI for social good, then shows why both are indispensable yet individually insufficient.

3.1. Good Governance

The first register equates procedural good with responsible AI governance. Here, legitimacy derives from due process: fairness checks [41], transparency notices [42], accountability trails, [43] and privacy safeguards [44]. These practices and instruments cultivate user trust by demonstrating that risks are anticipated and mitigated. Yet, as Hoffmann [45] argues, such “technical fixes” often optimise systems within existing power structures, leaving deeper inequalities untouched. Hence, procedural compliance alone cannot guarantee transformative outcomes—a concern echoed in responsible-innovation scholarship that questions the capacity of technical governance to redress systemic inequities [7,46].

3.2. Social Good

The second register shifts from non-maleficence to beneficence [10], which makes the substantive good aligns with the agenda of AI for social good. Here, the yardstick is the tangible contribution the AI system makes to societal or environmental objectives, often benchmarked against the UN Sustainable Development Goals, that were previously unattainable, financially infeasible, or less efficiently realised [9,47]. Using the SDGs as a yardstick offers clear advantages: they provide well-defined targets, an internationally endorsed framework, extensive underlying research and metrics, and a platform for coordination and resource-allocation among actors pursuing similar goals. Yet the very universality that makes the SDGs attractive can obscure local realities; without careful contextualisation, AI4SG projects risk becoming superficial—or worse, good-AI-gone-awry—when they fail to integrate with local infrastructures, knowledge systems, and networks [9,18,48], ultimately misaligning technological potential with the lived experience of the communities they intend to serve.

3.3. Interconnectedness of the Two

Crucially, procedural and substantive goods are mutually necessary but not mutually sufficient. An AI model can satisfy every governance checklist and still reproduce inequitable outcomes if its business logic extracts value away from marginalised users. Conversely, a project that pursues ambitious SDG targets may undermine its own legitimacy when it bypasses certain good governance practices. The hybrid intersection analysed in this article is characterised by exactly this slippage: Thai Agricultural AI platforms often clear baseline governance tests—accuracy thresholds, privacy notices—while substantive benefits remain confined to near-term market efficiencies, leaving questions of land justice, gender equity, and ecological regeneration unresolved. By foregrounding this tension between good governance (procedural good) and social good (substantive good), the study illuminates why incremental improvements can coexist with enduring structural inequities—a paradox that reappears in the empirical sections as the data-for-profit and market-optimisation tensions of the hybrid intersection.

4. The Precarity of Thai Farmers

Agrarian land constitutes 320 million rai (1 rai = 0.0016 km² or 0.16 hectares), or 32.7% of Thailand’s total land use. Approximately 66% of agricultural land is dedicated to cultivating key economic crops, including rice, cassava, maize, sugarcane, rubber, and palm oil. In 2024, Thailand’s agricultural exports reach USD 53 billion with rice remaining the top export product [49]. Economic values aside, the agricultural sector also houses 9.2 million registered farmers and 8 million households, which constitute 13.9% of the population and 29.0% of Thai households. With these significance in mind, Thailand is deeply rooted in agriculture.

While this statement acknowledges agriculture as the “backbone of the country”, it obscures a harsh reality. Beyond the fact that a significant portion of the population identifies as farmers, the sector offers little else to celebrate. Thai agriculture is marked by low productivity, declining competitiveness, an ageing and shrinking labour force, and a concentration of poverty within its demographic. These stark realities paint a picture of a sector fraught with challenges, making it a significant vulnerability within Thailand’s economy rather than a source of pride. In this section, I delve into the multifaceted challenges Thai farmers face, examining the traps and contexts of deprivation that contribute to their continued marginalisation. These challenges are explored from multiple perspectives, including demographics, infrastructure, economic constraints, and policy-induced clientelism that collectively keep them teetering on the edge of subsistence—able to survive but unable to thrive or prosper.

4.1. Farmers’ Demographic

Over the past two decades, there has been an increase in the educational levels of Thai farmers. Yet, as of 2020, the predominant level of education completed by the majority remained at the primary level [50]. This disparity suggests that individuals with higher educational achievements have been able to capitalise on structural economic shifts, transitioning to occupations outside of farming. In contrast, those with more limited education have continued to work within the agricultural sector. This makes the ageing population more severe in the farming sector. In 2020, elderly workers comprised 20% of the rural farm population compared with 7% in 2001 and 10% in 2010. Moreover, in 2020, young workers in the farming sector represented 30% of all rural farm workers, a decrease from 56% in 2001 and 45% in 2010 [50].

Attavanich et al. [51] estimate that the ageing problem reduced farm productivity by 2.4% and 3.7% in 2017 and 2023, respectively. Jansuwan and Zander [52] corroborate that older farmers are less inclined and motivated to invest in modern technology because of the need to save funds for their retirement, not to mention their digital illiteracy, which makes catching up with modern ag-techs even more difficult. As a result, even though farm households generally have the lowest income level of all rural households, income is lowest among households whose head is 60 years or older, indicating that ageing is a grave concern for productivity and income in rural farming households. Therefore, if the number of young farmers continues to decline, leaving only older farmers to handle increased farming workloads and risks, the agricultural sector’s competitiveness, sustainability, and national food security will likely become a challenge.

4.2. Factors of Production

The prevailing predicament for Thailand’s rural and small-scale farming households is restricted access to essential production inputs, such as water and farmland. Water accessibility remains a critical concern, with 58% of farmers lacking reliable access to water resources and only 26% of agricultural households having access to irrigation systems [50], primarily concentrated in the Central, lower North, and Bangkok metropolitan regions. Farmers in the Northeast, home to the largest proportion of farm households, face the greatest exposure to drought risks due to significantly lower rainfall compared to other regions [50]. The limited availability of water severely heightens the vulnerability of Thai agriculture, a situation likely to worsen with the increasing severity and frequency of droughts driven by climate change.

Compounding this issue is the fragmented nature of landholdings. The average size of agricultural land is 14.3 rai, but this figure masks significant disparities. Attavanich et al. [51] highlight that in 2017, 50% of Thai farming households operated less than 10 rai, while only 20% held more than 20 rai. This skewed distribution underscores the dominance of small-scale farmers. Such diminutive farm sizes hinder the realisation of economies of scale, making it difficult for farmers to offset the high capital costs associated with modern agricultural machinery and PA technologies, such as tractors and unmanned spraying drones.

In addition to smallholding, land ownership remains precarious for many farm households, particularly in the central region. Research by Pochanasomboon et al. [53] demonstrates that secure land ownership significantly improves productivity. For small and mid-size farms, complete ownership increases rice yields by 115.789 to 127.414 kg per hectare and 51.926 to 70.707 kg per hectare, respectively. These productivity gains are attributed to the enhanced liquidity and investment capacity afforded by secure tenure. Fully owned land has a higher appraisal value, enabling farmers to access credit and invest in machinery to boost productivity. Conversely, insecure land tenure constrains liquidity and stifles investment, perpetuating low productivity. Together, these elements constitute the poverty trap for Thai farmers from geographical and infrastructural perspectives.

4.3. Economic Conditions

In terms of income, a comprehensive agricultural economic survey conducted by the Ministry of Agriculture and Cooperatives (MOAC) in 2021 revealed that the average net income from agricultural activities in Thailand amounted to just THB 78,322 per annum. In stark contrast, the annual income of a worker earning the minimum wage (THB 328 per day, with 246 working days per year) is calculated at THB 80,688, surpassing the income generated by farming activities. This disparity underscores the precarious financial situation of smallholder farmers in Thailand, who occupy the lowest rung of the nation’s economic hierarchy. Moreover, farmers’ incomes are inherently seasonal and subject to fluctuations in global commodity market rates, reflecting the occupation’s lack of income security and its vulnerability to external economic forces.

The challenges faced by Thai farmers have been compounded by historical price volatility in agricultural commodities. Following a historic peak in 2011, global agricultural commodity prices, particularly for rubber, plummeted dramatically. This decline coincided with a series of natural disasters between 2011 and 2015, which caused extensive damage to farm production across the country. The declining prices and adverse climatic events put immense pressure on farmers’ incomes. Meanwhile, key crop production costs have steadily risen over the past decade. According to Attavanich et al. [51], labour, fertiliser, and land rent constitute the largest components of production costs. For Thai rice farmers, the economic burden is particularly stark: over the past 50 years, the selling price of rice has increased by a modest 3.9 times, while production costs have soared by an alarming 11.4 times [50]. This widening gap between production costs and selling prices exacerbates financial stress for farmers, leaving them with shrinking margins and limited capacity to reinvest in their operations.

4.4. Policy-Induced Monoculture and Clientelism

Two-thirds of Thai farmers engage in monoculture, a practice often criticised by economists for yielding low risk-adjusted returns. When farmers synchronously cultivate the same economic crops as their neighbours within a specific period, it leads to the geographical and temporal concentration of production. This phenomenon is particularly pronounced in rice farming, where 44% of the nation’s yield is harvested in November in the Northeastern region [54]. Such concentrated production often results in market oversupply, driving down commodity prices and offsetting any potential benefits of economies of scale associated with monoculture. Addressing the interconnected issues of monoculture, soil degradation, and the poverty trap requires confronting a critical challenge: unconditional state assistance. Programmes such as price guarantees, income support, and input subsidies have been criticised for incentivising monoculture (see [55] for maize and [56] for rice).

According to Lertrat [57], the Thai government has relied on two primary subsidy mechanisms to bolster farmers’ incomes: price support for agricultural produce and harvest support based on farm size. The former guarantees farmers a price for their crops above market rates, while the latter provides additional payments tied to land size, primarily benefiting rice and longan growers. Between 2019 and 2022, these subsidy programmes exclusively targeted farmers cultivating major economic crops such as rice, rubber, cassava, sugarcane, oil palm, and maize, covering approximately 7.9 million households. During this period, the government allocated an average of THB 150 billion annually to these subsidies, exceeding the Ministry of Agriculture and Cooperatives’ total budget of THB 126 billion in 2023. Much of this funding is sustained through loans from the BAAC, adding to the future fiscal burden.

4.5. Vicious Cycle

Economists often regard “farmers produce unsuitable commodities” as the first step of wrong decision-making that spirals downward. Lertrat [57] stresses that such production issues are not the fault of individual farmers alone. Often, they are reinforced by government policies that dictate their actions (Figure 2). Because the government provides subsidies only for seven major economic crops, farmers are inclined to continue or even expand the cultivation of these crops despite market losses because the subsidies ensure a predictable and secured income. In contrast, shifting to other crops or professions would mean losing subsidy entitlements, in addition to shouldering new investments and risks. The government also provides subsidies without regard to the quality of the yield and production efficiency. If the government subsidises the traditionally cultivated low-quality produce as much as high-quality produce, which requires additional investment, knowledge, and technology, then the farmers would be dissuaded from adapting.

Despite billions spent annually on subsidy programmes, the majority of Thai farmers remain impoverished and heavily indebted. Thailand’s agricultural sector exemplifies a classic neoclassical economic paradox: government interventions, though well intentioned to alleviate poverty, often perpetuate a cycle of dependency and poverty among the very farmers they aim to support. Economists and scholars do not advocate for the outright cessation of these programmes. Instead, they argue that such interventions should be conditional and strategically designed to promote capacity-building and empower farmers toward achieving long-term self-sufficiency. Kanchoochat [59] has noted that these short-term remedial policies distract from pivotal systemic challenges such as the growing monopoly in the agricultural inputs market, land ownership complications, and the lack of reliable water resources.

As aptly depicted in Figure 2, the demographics, infrastructural, and policy traps ensure that Thai farmers value immediate solvency over long-term sustainability. This context is crucial: it explains why Agricultural AI solutions promising quick productivity gains achieve rapid uptake, even when they deepen dependencies that the hybrid-intersection lens later problematises.

5. Methodology

While previous studies of Agricultural AI in Thailand [60,61] have primarily focused on technological adoption—particularly the challenges and potential of precision agriculture—they have paid limited attention to the diversity of business models and governance structures that underpin the power dynamics between agricultural technology providers and farmers. This study seeks to address that gap by examining not only the functional aspects of AI solutions but also the institutional and commercial arrangements that shape their deployment.

Understanding the business architecture of Agricultural AI is essential, as it directly influences the distribution of agency and benefit within the ecosystem. For example, whether AI services are monetised through farmer payments, subsidised by the state, or funded by third-party stakeholders has critical implications for user autonomy, data control, and long-term sustainability. In cases where services are offered free of charge to farmers, questions of accountability, data commodification, and governance become particularly salient.

This research adopts a socio-technical systems perspective, which conceptualises technologies not as neutral tools but as embedded within broader social, institutional, and economic arrangements. This lens enables a critical interrogation of Agricultural AI as a site where power relations, value systems, and institutional constraints are both reflected and reproduced [62,63]. Through this approach, the study foregrounds how technological design, business models, and governance practices intersect to shape the lived realities of smallholder farmers in Thailand.

5.1. Scope of the Research

This research predominantly focuses on ATPs that publicly and competitively offer their AI solutions, ensuring the examined technologies are comparable against alternatives and their impact is readily assessable. Regarding the presence of multinational tycoons, which is the focus on Duncan et al.’s [39] analysis, there is no prominent player in Thailand’s Agricultural AI landscape. Microsoft had plans to introduce its PA services, FarmBeats, in Thailand, but the project was terminated in September 2023. IBM engaged in a joint initiative with Mitr Phol on sugarcane farm management in 2019 [64] but has yet to upscale outside the joint initiative. Domestic telecom conglomerates like True and AIS have entered the fray with initiatives like TrueFarm and AIS iFarm, albeit as companies’ side projects. Hence, most of the Agricultural AI applications studied in this research primarily come from ag-tech start-ups, with few exceptions from state agencies.

Although Agricultural AI manifests in multiple forms [65], I use the term “AI services” to reflect the business model, “AI-as-a-service” (AIaaS), which underpins both AI-embedded software and hardware. AI is often described as a service rather than a product because its functionality typically depends on subscription-based data analytics performed on the cloud platform. In this business offering, ATPs deliver AI as a service that users employ as a solution to address specific challenges. From hereupon, I will refer to Agricultural AI as both a service and a solution interchangeably.

The scope of this study is limited to crop cultivation. The exclusion of animal husbandry and aquaculture is because they utilise AI services from providers distinct from those providing for crop cultivation, which dominate the landscape per the observation of Attachvanich et al. [60]. Considering that the data collected by the Ministry of Agriculture and Cooperative (MOAC) indicate that a mere 11.5% of farming households partake in animal husbandry and aquaculture exclusively, and with agricultural land use predominantly allocated to field crops (70.8%) and horticulture (24.7%), this narrowed focus still comprehensively encompasses a large proportion of Thai farmers and the Agricultural AI services landscape.

5.2. ATP’s Selection Criteria

A systematic, four-stage screening process was used to move from an initial pool of 66 to the 11 ATPs analysed in this study (

Table 1. ATP’s selection flow.

Phase	Records (n)	Rationale for Exclusion
Identification	66 ATPs 59 in NIA report [66] 4 via NIA network 3 state-led AI projects	–
AI relevance filter	25 retained	41 excluded: lacking AI-enabled core service
Scope filter	20 retained	5 excluded: animal husbandry only (out of scope)
Eligibility screening	11 retained	9 excluded: duplicate model with sample, no data/interview, or company shutdown
Included in analysis	11 ATPs 9 Private ventures 2 State-led projects	–

). The process began with the 59 ag-tech start-ups catalogued in the National Innovation Agency’s (NIA) Agri-Tech Start-up Ecosystem Report [66], to which four additional start-ups identified through NIA network queries (omitted from the report) and three government-run AI projects were added, yielding 66 records.

In total, 41 were excluded at the AI-relevance stage. Agricultural AI providers were defined as organisations that deploy one or multiple AI models as a core service rather than a peripheral feature. Digital marketplaces and e-grocery platforms were excluded due to their primary use of AI in operational optimisation rather than mission-oriented objectives. Likewise, farm-management tools that provided only descriptive dashboards without prescriptive analytics or automation were removed, because their primary value lay in record-keeping rather than algorithmic decision support.

The remaining 25 records were screened for scope, and 5 animal-husbandry-specific systems were omitted since this research focused exclusively on crop cultivation. In the final eligibility assessment, nine additional firms were excluded: four duplicated a business model already represented in the sample, three could not supply interview data and had no alternative media or documentation, and two had ceased operations or undergone major rebranding.

The final study cohort therefore comprised 11 ATPs—9 private ventures and 2 state projects (Rice Disease Linebot and HandySense). Although, seven of the eleven offered farm-management or precision-agriculture platforms, service logics diverged: For example, Ricult and Farmbook both provide farm advisory, yet Ricult monetises satellite-derived analytics through data brokerage, whereas Farmbook finances operations via supply-chain traceability fees. Similarly, Farm Connect Asia, HandySense, and Techmorrow all employ IoT-driven fertigation control but target distinct value propositions ranging from hardware sales to open-source retrofits. This heterogeneity makes the cohort well suited to analysing how different business architectures distribute power, data rights, and economic benefit along Thailand’s Agricultural AI value chain.

To illustrate a broad cross-section of the Thai Agricultural AI ecosystem, I classified the value chain of Agricultural AI into four phases: pre-harvest, farming, post-harvest, and retail.

• The pre-harvest phase involves activities that prepare for effective crop cultivation, including soil analysis, seed selection, and weather forecasting.
• The farming phase focuses on managing crops’ growth cycles to optimise yields and resource usage.
• The post-harvest phase encompasses processes such as crop processing, quality inspection, and assessing greenhouse gas (GHG) emissions generated during farming activities.
• The retail phase involves bringing agricultural goods to market, incorporating technologies for supply chain optimisation and food traceability.

Table 2. Profiles of the 11 studied ATPs.

ATPs	Value Chain	Crop Coverages	AI Service Provisions
Farmbook	Pre-harvest to retail	Any	Demand-supply ERP, farm analytics, traceability, financial services
Ricult	Pre-harvest to post-harvest	Field crops	Demand-supply ERP, farm analytics, financial services
ListenField	Pre-harvest to farming	Field crops	Genomic selection, soil analysis, farm analytics
Farm Connect Asia	Farming	Melon, durian	Farm automation
Techmorrow	Farming	Horticulture	Farm automation, chatbot to control automated devices
Infuse	Farming	Rice, maize	Crop damage insurance verification
HandySense	Farming	Rice, horticulture	Precision farming (farm automation)
Rice Disease Linebot	Farming	Rice	Rice diseases detection via chatbot
DeFire	Post-harvest	Field crops	Carbon credit monitoring, report, and verification (crop burning)
Spiro Carbon	Post-harvest	Rice	Carbon credit monitoring, report, and verification (dry-wet alternative)
EasyRice	Post-harvest	Rice	Quality and variety inspection

highlights the position of each selected ATP in the value chain as well as detailing the ATPs’ profile, including the specific crops they focus on and the AI solutions they specialise in.

The engagement of ATPs in the retail phase is notably underrepresented in this study, with only Farmbook participating in this specific segment. This underrepresentation can be attributed to two key factors: a skewed distribution on the crop production side of the value chain [60]; and second, within the retail phase, most e-grocery platforms were not included in the study. Farmbook is distinct because its consumer-facing traceability feature, FarmStory, facilitates value addition to crop prices and leverages yield history in its credit solution, both of which have a direct and tangible impact on farmers (see more in Section 6.4).

5.3. Data Collection

This study employed a multi-method approach comprising semi-structured interviews, participation in relevant events (e.g., start-up demo days), and secondary data analysis. Primary data were collected through interviews and field observations conducted between August 2022 and September 2023, while secondary data collection continued into 2024 during the thesis writing phase. Although direct interviews with each ATP were the preferred method, only four were completed in this format (

Table 3. Studied ATPs and source of data collection.

ATPs	Sources of Data Collection				Secondary Source References
	AI	EO	SI	SD
Farmbook		✓	✓	✓	[67,68,69]
Ricult			✓	✓	[70,71,72,73,74]
ListenField			✓	✓	[75,76,77,78]
Farm Connect Asia		✓		✓	[79,80]
Techmorrow	✓
Infuse	✓			✓	[81]
HandySense		✓		✓	[82,83,84]
Rice Disease Linebot		✓		✓	[85,86]
DeFire	✓				[87]
Spiro Carbon	✓			✓	[88]
EasyRice		✓		✓	[89,90]

Note: AI = author interview, EO = event observation, SI = secondary interview, SD = secondary data (including company’s website and third-party publications).

). Given that the study was conducted as part of a doctoral thesis, a portion of the available time was dedicated to parallel research activities. Priority for interviews was given to AI providers with limited presence at Bangkok-based exhibitions and those that infrequently update their services or organisational activities online.

Event observation served as an alternative means to engage with multiple relevant ATPs within a single setting. For instance, participation in the NIA’s AgTech Connext 2022 enabled the direct observation of technology demonstrations and facilitated interactions with representatives from EasyRice, Farm Connect Asia, and Farmbook.

All interview participants were provided with a detailed research information sheet outlining the study’s aims, scope, and intended use of the data. A consent form accompanied this sheet, clearly articulating participants’ rights, including the right to withdraw, and listing the available channels for raising concerns or inquiries. Interviews were conducted either online or onsite, based on participants’ preferences and availability. For online interviews, UCL-authenticated platforms such as Zoom and Microsoft Teams were employed to uphold data security standards, particularly in the secure handling and storage of audio-visual recordings. Observational access at events was granted through prior approval from the organisers.

In addition to primary data, secondary sources played a critical role in triangulating findings and supplementing gaps where direct engagement with ATPs was not feasible. These sources included publicly available materials such as company websites, discussion forums, press releases, investor presentations, and media interviews with company executives. Care was taken to ensure that all secondary materials were aligned with the research objectives and derived from credible, verifiable outlets.

5.4. Data Analysis

Most of the data analysed, whether primary or secondary, were in Thai. For author and media interviews, conversations were transcribed verbatim in Thai and then translated into English when used as quotes in the analysis. To ensure confidentiality, any personally identifiable information of participants was excluded during transcription, allowing the use of ChatGPT (model 4o) to assist with translations without privacy risks.

The analysis was structured around five key domains derived from the interview protocol and theoretical orientation: (1) problem addressed, (2) technical implementation, (3) business model, (4) governance, and (5) societal impacts. Following the approach outlined by Williams and Moser [91], the thematic analysis proceeded through three hierarchical stages of coding (

Table 4. Thematic coding framework.

Domains	Questions	Open Codes	1st Order Category	2nd Order Theme
Pain points addressed	What pain point desire to address?	“Cannot sell produce”, “low yields”, “loan”	Market mismatch, financial stress	Immediate precarity alleviation
Technical implementation	How does AI function?	“Satellite NDVI”, “IoT fertigation”	Algorithmic input, farm automation	Digitisation of agricultural value chain
Business model	Who pays for the solution?	“GP-sharing”, “subscription”	Revenue capture logic	Data commodification for free services
Governance	What safeguards are in places?	“PDPA consent”, “no-liability clause”	Risk-mitigation practice	Opaque algorithmic governance
Societal Impact	What changes for farmers and industry?	“15%+ income”, “less fertilisers uses”	Outcome metrics	Market optimisation

). Open coding formed the initial phase, in which the raw data were examined line by line and assigned conceptual labels without imposing any predetermined categories. This inductive stage allowed the researcher to remain grounded in participants’ own terms and to capture emergent meanings. In the second phase, axial coding, the open codes were grouped and linked to identify relationships, forming more abstract and structured 1st-order categories. This stage focused on connecting patterns across interviews and refining categories based on shared properties or contrasting features. Finally, in the selective coding phase, the analysis progressed toward the formation of broader conceptual patterns—2nd-order themes—that synthesised multiple categories into coherent explanatory constructs. These themes were then woven into an overarching narrative that aligned with the theoretical lens of the hybrid intersection.

In this study, the resultant 2nd-order themes enabled the identification of two central tensions discussed in Section 11—data commodification vs. social-good delivery, and market optimisation vs. structural reform—thus contributing to theory development within the hybrid-intersection framework.

5.5. Methodological Limitation

This research adopted a selective case study approach designed to illustrate the lived realities and institutional constraints that characterise the hybrid intersection rather than to provide comprehensive coverage of Thailand’s Agricultural AI landscape. The study’s eleven ATPs represent a purposive sample chosen for theoretical insight rather than statistical generalisability. This approach aligns with established practice in AI ethics scholarship, including Umbrello and van de Poel’s [92] value-sensitive design exploration and Bondi et al.’s [18] PACT framework development.

Additionally, while the research captured ATP perspectives through interviews and secondary sources, direct farmer voices remained underrepresented—a limitation that future research should address through participatory methodologies. Despite these limitations, the selective approach enabled a detailed analysis of how competing notions of “good” materialised within specific institutional arrangements, providing empirical grounding for the hybrid-intersection concept that broader surveys might have obscured.

6. Agricultural AI’s Value Chain

Figure 3 provides a comprehensive overview of Agricultural AI applications currently available in Thailand across the agricultural value chain, from the initial stages of farm planning to the destination at retail outlets for consumers.

6.1. Pre-Harvest

The pre-harvest phase encompasses farm planning and preparation activities, including selecting crops best suited to local soil conditions and climate windows—often informed by weather-risk forecasting—and alignment with market demand through mechanisms such as forward contracts. In Thailand, AI technologies are increasingly embedded in pre-planting decisions, although the focus and implementation vary across ATPs. For example, Ricult offers the free Bai Mai farmer application, which provides plot-specific satellite-derived crop health maps, seven-day microclimate forecasts, and soil-based crop recommendations. Its enterprise solution, RicultX, supports agricultural mills in procurement planning and the issuance of forward contracts. Ricult’s agronomic insights are primarily driven by the AI analysis of high-resolution satellite imagery.

In contrast, ListenField adopts a more sensor-intensive approach by combining soil analysis through portable near-infrared spectroscopy with advanced genomic–environmental modelling to simulate the performance of crop varieties under specific climate–soil conditions. On one hand, these innovations exemplify how AI can transform pre-harvest planning from a largely intuitive process into a data-driven one—enabling farmers to select optimal crops, determine the best planting windows, budget inputs efficiently, and access timely credit. This not only enhances yields but also reduces production costs and mitigates risk. On the other hand, these services pre-configure the farm plan in software, thereby shifting agronomic discretion from the farmer to the platform.

6.2. Farming

In the farming phase, precision agriculture (PA) takes centre stage. Duncan et al. [39] define PA as a “farm management strategy” designed to address spatial and temporal variability within fields, embedded within a suite of digital technologies. Farm advisory and farm automation represent two interlocking yet analytically distinct expressions of AI within precision agriculture.

6.2.1. Farm Advisory and Automation

Advisory systems place most of the analysis in the cloud: machine learning models examine satellite images, soil tests, and weather forecasts, then issue recommendations that farmers can consider over the course of hours or days. In this study, comprehensive advisory service providers included Ricult and ListenField, meanwhile the Rice Disease Linebot (RDL) is specialised toward rice disease diagnosis and treatment recommendations [86]. In contrast, automation systems bring the intelligence into the field itself: cameras and other on-board sensors feed real-time data to algorithms that steer machinery, target sprays, or adjust seed rates within seconds. As mentioned earlier, Farm Connect Asia, Techmorrow, and HandySense fall into this category.

In short, while advisory AI guides the “what, when, and how much” of farm management, automation AI executes the “where and how” on the ground. The two functions reinforce each other—data from autonomous machines improve future advice, and prescription maps from advisory tools direct the machines—but they differ in processing location, speed of decision-making, and the level of human oversight they require. This bifurcation sets up differential data dependencies later analysed in Section 7.

6.2.2. Crop Damage Verification

Beyond being used directly in farming, AI is also streamlining disaster verification processes. Traditionally, field officers were dispatched to validate affected areas, a process that was time-consuming and resource intensive. To address these inefficiencies, Infuse’s Malisorn enables farmers to self-report disaster impacts by outlining their farm plots and uploading photographs of the damage. The app uses image processing techniques to analyse the uploaded images and cross-references the findings with satellite imagery through computer vision (Figure 4). This streamlined approach significantly accelerates verification process, ensuring that affected farmers receive timely financial assistance.

6.3. Post-Harvest

6.3.1. Carbon MRV

Carbon monitoring, reporting, and verification (MRV) is an AI-driven post-harvest application. While farmers typically register with intermediary platforms at the start of the harvest cycle, the successful conversion of their activities into carbon credits are finalised during the post-harvest stage.

DeFire focuses on reducing carbon dioxide emissions by addressing crop-burning practices, a common issue in maize cultivation. On the other hand, SpiroCarbon targets methane emission reductions by promoting wet–dry rice cultivation techniques. AI plays two essential roles in these platforms: prediction and vision. The predictive model estimates GHG emissions based on baseline scenarios that follow the methodology set by the Verified Carbon Standard [93] or the United Nations Framework Convention on Climate Change [94]. This enables platforms to calculate the potential carbon credits generated during a harvest cycle, which are then marketed to buyers, often through forward contracts (Figure 5). Following the harvest, the image processing model utilises multispectral satellite imagery to verify GHG reductions. This process provides time-stamped and geolocated evidence of sustainable farming practices, ensuring both transparency and accountability in the conversion of these practices into verifiable carbon credits.

6.3.2. Crop Quality Inspection

Crop quality inspection represents a key AI application in the post-harvest stage. EasyRice employs hyperspectral scanners to analyse 25 g rice samples. The scanned data are processed in the cloud using AI algorithms that evaluate rice quality and identify defects based on physical characteristics such as chalkiness, discolouration, and grain damage (Figure 6). By analysing spatial visual features and spectral signatures, hyperspectral imaging delivers a more precise and efficient quality assessment than traditional manual methods, enhancing accuracy and reliability [90]. This AI-powered quality inspection not only saves time and reduces labour costs but also improves inspection standardisation and comprehensive feedback to farmers for future improvement.

6.4. Retail

Food traceability is “the ability to follow the movement of food through specified stages of production, processing, and distribution” [95]. While internal traceability has long been standard practice for large agribusinesses to manage inventory, external traceability, aimed at providing transparency to end consumers, is a more recent development. Farmbook offers a comprehensive suite of services that supports food traceability, from demand planning to farm management apps to packing stations. Figure 7 shows Farmbook’s value chain, which includes stages such as farming, processing, and distribution, culminating with a QR-coded Food Story that details the product’s journey from farm to shelf. The Food Story includes the farmer’s profile, the geo-location of the plot, and timestamps documenting every stage of the process, from planting seeds to packaging and placement in modern trade outlets. This level of transparency builds consumer trust and supports compliance with regulatory standards and certification requirements.

Farmbook’s traceability system extends its capabilities by incorporating AI to calculate farmers’ credit scores, facilitating peer-to-peer lending through its innovative service, FarmFinn. Thai farmers often face financial constraints due to delayed payment terms from large buyers who operate on long credit cycles, creating liquidity challenges [68]. FarmFinn addresses this by enabling farmers to sell their delivery contracts to investors through the platform, receiving immediate payments at a discounted rate rather than waiting for the credit period to end [69]. This system effectively transforms future receivables into immediate liquidity, allowing farmers to reinvest promptly in their next production cycle while providing investors with secured agricultural assets.

AI-powered food traceability creates a continuous loop where insights and data from the previous cycle’s traceability efforts give farmers an edge for better yield planning and credit access in the next iteration of pre-harvest phase. Taken together, the four phases expose a consistent pattern: AI modules insert new intermediary functions—farm prescription, alternative credit risk scoring, carbon accounting—that lock farmers into data-sharing arrangements [40]. Each phase therefore operationalises the same structural logic diagnosed by the hybrid-intersection framework: incremental functional gains at the cost of expanded platform dependency [33,40].

7. Cost of Services

Technological merit alone does not determine impact; access is mediated by the business model behind each ATP. Of the eleven initiatives, only the three farm-automation services oblige farmers to pay directly, and their adoption remains confined to a few hundred users in high-value horticulture (

Table 5. ATPs’ business model.

ATPs	Farmers’ Coverage	Farmers’ Access	Paying Customers	Revenue Model
Farmbook	≃40,000 [68]	Free	Modern trades	Transaction GP from forward contract; THB 500,000 of packaging station franchise; 80,000 THB/year operating system; 5% royalty sales
Ricult	≃600,000 [70]	Free	Mills, process factories	Transaction GP; subscription to Ricult X for supply forecast and location.
ListenField	≃30,000 [75]	Free	Cooperative, agri-businesses	API licensing; paid service (i.e., genetic prediction model); service contract
FCA	≃100 [80]	Paid	Farmers	THB 20,000–35,000 for hardware, 1000 THB/year/farm for annual subscription.
Techmorrow	≃100 *	Paid	Farmers	Connect IoTs to Techmorrow’s board for farm automation. 150 THB/month or 1500 THB/year for subscription
Infuse	N/A	Free	State	Sponsored by BAAC as part of government policy to promote crop insurance scheme
HandySense	≃200 [84]	Paid	State (R&D) farmers	Budget allocation for initial R&D; farmers buy hardware from vendors
RDL	≃3500 [85]	Free	State	Budget allocation for service provision and maintenance
DeFire	≃10,000 [87]	Free	Credit buyers	20% of carbon credit trading fees
Spiro Carbon	≃10,000 *	Free	Credit buyers	20% of carbon credit trading fees
EasyRice	≃20,000 [89]	Free **	Rice mills, exporters	Inspection scanner around THB 100,000–170,000 and annual subscription.

Note: * figure quoted from interviews, ** contingent on accessibility to networked millers or processed factory.

). Every other service shifts the cost burden onto better-capitalised actors allowing farmers to enrol free of charge. With no upfront cost, farmers’ onboarding significantly rise to the level of ten thousand, with Ricult reaching a remarkable number of 600,000 users in its farm advisory application, Baimai. This goes to underscore how relevant cost structure and service delivery model are to Agricultural AI’s impact assessment.

7.1. Data Aggregator and GP-Sharing

The “free-to-farmers” model reflects genuine efforts to make agricultural AI accessible while creating sustainable business models. ATPs like Ricult have pioneered approaches that recognise farmers’ limited purchasing power while still requiring revenue streams to fund service development and maintenance. This creates what might be termed a value circulation paradox: platforms must extract economic value from farmer interactions to sustain the very services that benefit farmers. This “free” service model exemplifies what Zuboff [37] terms “surveillance capitalism” applied to agriculture—farmers contribute behavioural surplus that is processed into predictive products sold to third parties, creating a fundamental asymmetry in value capture [38].

While it is true that all AI providers collect data from users, data aggregators collect data more extensively for the sake of “connecting” farmers to other well-capitalised stakeholders (e.g., financial institutions, millers) rather than purely for “improving” their models and service deliveries. Ricult [70] explains its business as similar to food delivery companies. The platforms make money by charging gross profit share (GP) from the restaurants. That’s a very similar concept. I don’t charge farmers, but I do connect them with other service providers such as banks, insurance companies, vendors, and transport services. Then, we charge the GP in the middle.

Effectively, farmers pay for prediction and analytics services by contributing their personal and farm activity data, which are commodified for profiling and monetisation. In return, farmers benefit from improved access to formal credit, as their transactional records and yield histories are shared with financial institutions via the ATPs.

Farmers’ familiarity with sharing data for government assistance programmes also makes them more open to providing data to ATPs if it increases profitability. For instance, Ricult’s Bai Mai system integrates big data to monitor local product prices, helping farmers decide where and when to sell their crops for the highest returns. Ricult [70] illuminates that:

We met some farmers in Saraburi who drove across town to Khorat [roughly 150 km apart] just to sell their crops because they knew [they were getting a better price and] it was worth the transportation time and cost. We give them an opportunity. We give them data. So that they can make a better decision and get a better price.

While farmers receive immediate functional benefits—weather forecasts, market intelligence, advisory services—platforms necessarily accumulate datasets that provide them with analytical capabilities unavailable to individual farmers. This information asymmetry emerges organically from the platform’s role as data aggregator. The challenge lies not in the intent but in the structural mechanics of this arrangement [96].

Although both Farmbook and Ricult provide free farmer access to farm management, supply–demand matching platforms, their business models reveal two distinct approaches to Agricultural AI monetisation. Farmbook pursues infrastructure integration, investing in physical packing stations and cold-chain facilities to capture value from premium retail markets willing to pay for traceability and food safety. Ricult operates as a pure data aggregator, monetising satellite imagery and farm records through crop forecasts sold to mills and alternative credit scoring for financial institutions. Ricult thus exemplifies the pure data-broker model in Thailand’s Agricultural AI landscape—optimising for scale, near-zero marginal cost, and multi-sided data monetisation.

7.2. State Funding

State funding remains a pivotal lever for reducing or eliminating farmers’ out-of-pocket costs for Agricultural AI services, and it can be deployed through several complementary mechanisms. First, direct budget appropriations cover the full development and operating expenses of certain services, as in the RDL, which the NECTEC maintains entirely at public expense. Secondly, agencies can release open-source, licence-free platforms and hardware schematics—HandySense being the leading example—so that farmers only need to purchase low-cost micro-controller boards, sensors, and actuators, or adapt equipment they already own. Third, the state can take an equity or procurement stake in start-ups whose technologies align with policy objectives; the BAAC’s backing of Infuse’s enables free, AI-based insurance claims processing for growers. By absorbing development costs or subsidising deployment in these ways, government programmes expand the reach of Agricultural AI solutions while minimising the financial burden on resource-constrained smallholders.

However, state funding also carries limitations. Firstly, while subsidies from the state typically enhance affordability, they do not necessarily guarantee scalability. For instance, despite being offered at no cost to farmers, the publicly funded RDL serves only around 3500 users—approximately two orders of magnitude fewer than its privately operated counterparts. Secondly, although open platforms like HandySense are more cost-effective (ranging from THB 3100 to 9095 for a ready-to-use setup) compared to proprietary systems such as Farm Connect Asia (approximately THB 20,000 to 35,000), they pose significant barriers for farmers who lack technical skills. The installation and ongoing maintenance of these open systems demand proficiency in electrical engineering and coding.

7.3. Charging Wealthier Stakeholders

Another method to shift costs away from smallholders is to bill capital-rich stakeholders—food processors, millers and exporters—for the service. Crop-quality inspection illustrates the approach. EasyRice, for example, supplies hyperspectral scanners that cost about THB 100,000 per unit, a sum far beyond the reach of most small and mid-sized rice growers. Consequently, farmers can use the technology only when they belong to a well-organised co-operative or sell to mills and exporters that have purchased the scanners. Although farmers do not pay directly, they still get the benefit since grading now takes three to five minutes instead of hours, cash is released sooner, and higher returns are obtained for those producing premium-quality milled rice or other desirable varieties.

However, a critical drawback of this model is its limited accessibility. According to EasyRice [90], its customer base includes over 235 paying clients, predominantly rice millers, Thai exporters, and foreign importers. This limitation still leaves the majority of farmers outside its network and therefore subject to conventional, often opaque price assessments by local middlemen.

7.4. Cost of Services’ Diagnosis

ATPs face genuine dilemmas: how to provide valuable services to farmers with limited purchasing power while maintaining financial sustainability? Most of the time, they find revenue from someone else or extract surplus from improved market transaction. Platforms operate in innovation ecosystems that demand rapid scaling and clear monetisation pathways, while farmers operate in volatile agricultural markets with thin margins. These different operating environments create natural tensions around who bears various types of risk that are explored in the next sections.

8. Good Governance Analysis

Responsible AI aims to ensure that AI applications align with societal values and goals by addressing their social, economic, and environmental impacts [97]. In his meta-analysis, Ryan [65] highlighted the differences in how ethical concerns and principles are framed and prioritised in Agricultural AI and normative AI ethics.

Figure 8 illustrates that sustainability emerges as a central theme in the ethical discourse surrounding Agricultural AI—a sharp contrast to its relatively lower priority in broader AI ethics frameworks. Coupled with the relatively high emphasis on beneficence, Ryan’s findings further illustrate why the use of Agricultural AI is highly accentuated on delivering social good impact.

Another insight is pragmatism over procedural ethics in agricultural AI. Adoption on farms is decided by functional trust: “Does the model improve yield and reduce risk?” Hence, trust and non-maleficence rank highly, whereas abstract procedural duties (i.e., explainability audits, bias assessments) receive less attention compared to normative AI ethics. This pragmatism, while understandable, risks leaving structural power imbalances unexamined—for example, data asymmetries between smallholders and ATPs [97]. This further explains the under-representation of justice-oriented principles, such as justice, fairness, responsibility, and transparency in Agricultural AI.

To testify Ryan’s meta-analysis, which is predominantly Western-oriented, this section examines the prevailing norms in “good governance” of Agricultural AI in Thailand. Because Section 9 assesses outcome-oriented principles such as sustainability and beneficence, this section concentrates on the principles critical for safeguarding farmers’ rights: privacy, autonomy, reliability, transparency, and accountability.

8.1. Privacy

Privacy has consistently been a primary ethical concern in AI, though it appears to be less emphasised within the domain of Agricultural AI, as observed by Ryan [65]. This stems from the assumption that most data collected pertain to non-sensitive, farm-related information. Comparing privacy practices across different ATPs is challenging for several reasons. Many services are not publicly available as mobile applications with easily accessible privacy policies. Instead, access to privacy notices for proprietary software is often restricted until after a purchase. Despite these limitations, an analysis of the available terms and conditions and privacy policies from ATPs such as Ricult (a data aggregator provider), compared to Infuse (a specialised service provider), reveals notable differences in their data collection and usage practices (

Table 6. Comparison of ATPs’ privacy policies.

Information	Ricult’s Bai Mai	Infuse’s Malisorn
Identification	Name, ID or passport number, DoB	Name, ID or passport number, DoB, farmer registration number
Contact	Address, email address, phone numbers, social media accounts	Address, email address, phone numbers, social media accounts
Geographical	Geographical coordinates of farms. GPS location (on-device permission)	Geographical coordinates of farms. GPS location (on-device permission)
Technical	IP address, browser type and version, time zone setting, and cookies	Hardware model, operating system and its version, mobile network information, cookies
Usage engagement and behaviour	URLs visited on the platform Products viewed or searched Duration of each visit User interactions on the page, such as scrolling, clicks, or mouse movements	URLs visited on the platform Products viewed or searched
Financial	Bank account, payment information, transaction data	BAAC account number, branch details
Other	User feedback, call-centre recordings, comments/messages	Call-centre recordings, photos/videos captured via camera permission
Cross-border	Yes	-
Data subject’s rights	Access, copy, rectification, erasure, restriction, objection, portability, consent withdrawal	-
Complain	30 days for notification	-
Data retention	Deleted 5 years from last activity	-
Contact channel	DPO’s email	Company’s email

Source: Bai Mai (Leaf–Recult) downloadable on App Store and Play Store; ref. [81] for Infuse’s Malirson.

).

ATPs routinely require baseline identifiers—name, national ID number, postal address, e-mail—and geo-coordinates of farm plots to deliver site-specific services. Both Infuse’s Malisorn and Ricult’s Bai Mai therefore mirror mainstream agri-tech practices. Where they begin to diverge is in the breadth and granularity of the “digital exhaust” they each regard as commercially valuae.

Infuse logs standard website-traffic metrics (pages viewed, search terms) primarily to gauge product demand and tailor in-app promotions. Beyond this, it captures device diagnostics—hardware model, OS version, mobile-network data—and, with the user’s permission, the camera roll, allowing it to ingest photographs and videos farmers submit as evidence of crop damage or for profile personalisation. The policy does not record session-time or pointer-movement analytics and makes no explicit reference to behaviour-based advertising.

Ricult, by contrast, applies the playbook of e-commerce and social-media firms. In addition to the usual traffic counters, it records dwell time on each page, scrolling patterns and mouse-movement traces. Such fine-grained telemetry underpins Ricult’s role as a data aggregator: the company can build probabilistic profiles that support targeted offers—e.g., soft-loan adverts embedded in Bai Mai—and can monetise anonymised cohort insights with third-party partners. Ricult is explicit that only aggregated statistics leave its servers (for instance, “500 men under 30 who clicked an advert in one day”), thus lowering re-identification risk while still enabling behavioural advertising revenue. Neither provider seeks highly sensitive attributes—biometrics, health data, ethnicity or political opinions—so the overall sensitivity level remains moderate.

From a compliance perspective, Ricult again goes further. Its notice enumerates Personal Data Protection Act’s (PDPA) data-subject rights, prescribes a five-year erasure horizon for inactive accounts, and appoints a Data Protection Officer. Infuse states the lawful bases for collection and lists security safeguards, yet omits a retention schedule, cross-border transfer clause, or DPO contact. Taken together, the comparison portrays Ricult as the more data-hungry—yet also the more PDPA-mature—ATP.

8.2. Autonomy

As Floridi et al. [10] describe, autonomy is the ability to make decisions independently based on one’s discretion. Farmers’ autonomy is highest when they are direct users of AI technologies and bear the financial costs themselves. For instance, in precision farming, farmers can configure pre-set conditions, such as timing and climate triggers, for automated irrigation systems and retain the ability to deactivate the system whenever necessary. This level of control empowers farmers to adapt AI solutions to their specific needs and preferences. However, higher financial investment in AI technologies does not always translate to greater user control. For example, IoT systems with open-source platforms like HandySense allow farmers to choose their connected hardware and determine where their data are stored and processed. In contrast, proprietary systems may restrict these choices, limiting farmers’ control in return for better ease of use. When farmers are not the financial contributors to an AI service, their autonomy diminishes. For instance, in rice quality assessments or carbon MRV processes, farmers have limited influence over the methodologies and rebuttal of the outcomes.

8.3. Transparency

Farmers’ autonomy is inherently linked to the principle of transparency as it enables insights and the understanding necessary for effective accountability [98] from ATPs and due discretion exercised by farmers. Transparency operates on multiple levels. At the institutional level, it involves openness about policies, actions, and laws to promote communication, collaboration, and trust among stakeholders [33]. At the system level, transparency pertains to the interpretability of AI systems, allowing users to understand the decision-making processes and the factors influencing those decisions [99]. Transparency in Agricultural AI predominantly manifests at the institutional level, driven by legal requirements, such as PDPA.

Transparency is also fundamental for managing user expectations of technology. It anchors users to a realistic understanding of the technology’s capabilities and limitations [100]. None of the studied ATPs claim perfect accuracy for their AI systems, highlighting their commitment to setting clear expectations. EasyRice [90] claims to achieve 95% accuracy. Ricult claims 90+% accurate crop scan but advises farmers to retain human judgment in conjunction with AI assistance [71].

8.4. Reliability

Reliability in AI ethics involves ensuring that AI systems are dependable, consistent, and capable of performing as expected under various conditions. For Agricultural AI, ease of use is a critical component of reliability, particularly given that many farmers—the primary users—are older and less familiar with digital technologies. Established ATPs, such as ListenField, Ricult, and Farmbook, have invested heavily in human-centred design. By incorporating continuous feedback loops, these companies prioritise user experience and strive to make their applications intuitive and accessible to a less tech-savvy audience.

Reliability is further reinforced through familiarity and simplicity [101]. Research conducted by the Department of Rice and institutional partners [102] found that farmers’ use of mobile applications decreases with age and that LINE (similar to WhatsApp in the West) is the most widely used communication application across all age groups. Recognising this, ATPs such as Techmorrow and the RDL have embedded their services within the LINE platform. This strategy reduces software development costs and lowers the learning curve, enabling farmers to adopt the technology more easily without significant time or effort.

Another aspect of reliability lies in the consistent delivery of accurate results. Many ATPs exercise caution when launching AI services, ensuring the systems meet stringent accuracy thresholds before widespread deployment. For example, the Rice Disease Linebot can detect up to 16 rice diseases, but general users can currently access diagnostics for only 10 diseases. The remaining six are reserved for beta testing until their detection accuracy matches the initial offerings’ 90–95% benchmark. Once this standard is achieved, the service will expand its official diagnostic capabilities.

8.5. Accountability

According to Novelli et al. [43], an AI system is truly accountable only when three conditions are met—an authority is clearly recognised, that authority can be openly questioned, and meaningful limits or sanctions can be applied for wrongdoing. In the context of this research, an authority essentially refers to the ATPs that develop, deploy, and control the algorithmic service used by farmers. While the authority is recognised, Terms and Conditions (T&Cs) that absolve the companies from liabilities potentially caused by their AI systems, which means the second and third components are not fulfilled.

For example, Ricult’s T&Cs explicitly disclaim any liability for damages, including direct, indirect, special, incidental, or consequential damages, losses, or expenses resulting from errors, omissions, interventions, data deficiencies, suggestions, advice, or methods related to agricultural operations or cultivation. Users bear the risk of relying on such information, and Ricult does not verify, endorse, or guarantee the content, accuracy, opinions, or other connections made available [on] the platform.

Genuine answerability collapses when the “implications” element—sanctions or remedies for harm—is erased; the sweeping liability waivers in Ricult’s T&Cs therefore rendering the accountability chain non-functional.

The empirical evidence of Agricultural AI substantiates the hypothesis that regulatory frameworks establish a baseline standard of good governance in AI. Legislative mandates are instrumental in enforcing AI ethics, a capacity in which standalone guidelines are notably deficient [103,104]. The operationalisation of AI accountability similarly encounters significant challenges without clear legal directives regarding responsibility for faults and errors.

8.6. Good Governance Diagnosis

Thailand’s Agricultural AI landscape exhibits a persistent asymmetry between functional and procedural trust. Functional trust thrives because farmers can readily observe that yield-forecast models and advisory apps “work” in practice: predictions are accurate, interfaces fit local digital habits, and tangible gains—higher yields, better market access, improved cash-flow—quickly accrue [105]. By contrast, procedural trust—built on the transparency of AI systems and the accountability of their developers [106]—lags. The algorithms remain opaque and “no-liability” clauses in end-user agreements shield providers from redress. This hollowed-out form of accountability exemplifies what Novelli et al. [43] term “regulative noise”, in which aspirational language substitutes for enforceable rights—a pattern that dovetails with the hybrid-intersection logic whereby functional trust eclipses procedural safeguards.

Interview evidence reinforces this pattern. ATPs concede that commercial success hinges less on watertight privacy notices or algorithmic explainability than on delivering visible, field-level benefits. Consequently, reliability—expressed through accuracy thresholds, robustness testing, and easy-to-use interfaces—is marketed vigorously, whereas deeper procedural safeguards receive cursory attention. To their credit, ATPs practice a narrow form of accountability: new features enter production only after surpassing stringent performance benchmarks, and most companies avoid overstating accuracy rates. Yet in the absence of explicit AI liability statutes, smallholders—often digitally under-resourced and, in many cases, merely downstream recipients rather than direct users—possess scant legal recourse when erroneous outputs inflict economic harm.

9. Social Good Impact Analysis

The agri-tech ecosystem report by the NIA [107] states that addressing societal issues is the primary motivation for 82% of agri-tech founders, closely followed by profitability at 80%. While claims of prioritising societal goals over or equal to financial performance might be dismissed as public relations tactics, they may hold genuine significance for many ATPs, particularly start-ups. Addressing agricultural challenges requires focusing on rural populations and their specific needs. This is particularly difficult when working with smallholders, who often lack the purchasing power and the willingness to adopt innovations from lesser-known ventures [61]. These barriers complicate the monetisation of services and the adoption of advanced technologies.

From a financial perspective, it would be more straightforward for agri-tech start-ups to provide AI solutions to large agri-businesses. However, many ATPs examined in this research have chosen to engage with smallholder farmers, often at significant financial and operational trade-offs. From this lens, many ATPs are genuinely committed to making a social good contribution. What remains underexplored is a nuanced understanding of what “social good” means in the context of Thai agriculture.

9.1. Agricultural AI × SDG

As discussed in Section 2, the Sustainable Development Goals (SDGs) provide a standardised framework for evaluating AI’s contributions to social good [9]. My analysis reveals that Agricultural AI contributes to eight SDGs: goals 1, 2, 5, 8, 9, 10, 12, and 13 (Figure 9). These contributions vary in scope, intent, and immediacy. Some SDGs, such as SDG 8 (Decent Work and Economic Growth) and SDG 9 (Industry, Innovation, and Infrastructure), are addressed primarily because AI-driven solutions are often developed and deployed as part of agri-tech initiatives that generate employment and modernise the agricultural sector with innovative tools. These contributions are largely a by-product of the industry’s growth rather than a direct outcome of AI applications benefiting farmers.

Other SDGs, such as SDG 1 (No Poverty), SDG 2 (Zero Hunger), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), represent tangible and intended outcomes of engaging with Agricultural AI. For instance, AI-driven precision farming can optimise resource use, enhance yields, and reduce agricultural waste, directly contributing to poverty alleviation, improved food security, and climate resilience. However, certain goals, such as SDG 5 (Gender Equality), are less directly impacted by current Agricultural AI implementations. These goals require deliberate and targeted interventions to ensure that marginalised groups—such as female-headed farm households practising monoculture on small plots—can access and benefit from AI technologies.

9.2. Empirical Translation of Social Good

Because most ATPs do not frame their outcomes in SDG language,

Table 7. Translation bridges between local themes and SDG targets.

Themes	Description	SDG Targets Relevance
Productivity	Higher yields per input	2.3 (double productivity of small-scale producers) 8.2 (raise productivity through tech and innovation) 9.4 (upgrade industries for resource-efficiency)
Income	Higher farm profits or new revenue streams	1.2 (halve poverty in all dimensions) 2.3 (double incomes of smallholders) 8.5 (full and productive employment, equal pay) 10.1 (income growth of bottom 40%)
Finance	Better access to financial services and assistances	1.4 (access to basic services incl. financial services) 8.3 (financial inclusion for MSMEs) 9.3 (increase access of SMEs to financial services) 10.2 (promote social, economic inclusion)
Agri-waste	Reducing input waste or upcycling residues	12.2 (sustainable management of natural resources) 12.5 (substantially reduce waste generation) 13.2 (integrate climate measures in agriculture)
Food safety	Promoting organic, harm-free crops to consumers	2.1 (access to safe, nutritious food all year round) 12.3 (halve global food loss and waste)
Net-zero	Reducing or sequestering GHG emissions	8.4 (decouple growth from environmental degradation) 13.1 (strengthen resilience to climate-related hazards)
Food Waste	Prevent or minimise edible losses along the chain	9.1 (develop sustainable transport and logistics infrastructure) 12.3 (halve global food loss and waste)

re-maps the impacts they do report—productivity, income, finance, agri-waste, food safety, net-zero, and food waste—to the corresponding SDG targets, creating a practical “translation bridge”. One goal is conspicuously absent: SDG 5 (Gender Equality). None of the initiatives examined sets explicit gender metrics. Ricult merely notes in a USAID brief [108] that most Thai farmers are women, while Spiro Carbon remarks that female growers earn extra income from carbon-credit trading. In both cases, the benefits to women are incidental rather than the product of intentional, gender-responsive design.

The process of validating the impacts addressed by each ATP hinges on two criteria: first, whether their services fulfil the specified theme, and second, whether the positive contributions are a deliberate target of the ATP or simply byproducts of service usage not explicitly promoted by the ATP. This distinction is crucial because direct impacts reflect intentional contributions that are typically internalised, measured, and promoted. In contrast, indirect impacts represent positive externalities that may not be systematically monitored or leveraged. Without regular measurement and progress tracking, the sustainability and scalability of these indirect benefits remain uncertain. Using net-zero contributions as an example, precision farming enhances resource efficiency, curtailing excess input waste. Nonetheless, without a comprehensive monitoring system, it is challenging to determine how much this reduction contributes to the broader decarbonisation goal.

9.2.1. Explicit Economics Focus

Table 8. ATPs’ metrics of social good impacts.

ATPs	Productivity	Income	Finance	Agri-Waste	Food Safety	Net-Zero	Food Waste
Farmbook	△ Resource tracking	✓ Pre-order sales	✓ Credit lending	△ Input optimisation	✓ QR-coded traceability		✓ Market match
Ricult	✓ 22% productivity growth [72]	✓ 17% profit increases [72]	✓ Credit scoring	✓ Agri-waste initiative		✓ EUDR traceability	✓ Market match
ListenField	✓ 20% productivity growth [75]	✓ 20% price increases [78]		△ Input optimisation	✓ Reduced chemical input	✓ Carbon MRV
FCA	✓ Reduced labour/time	✓ 20% profit increases [79]		△ Input optimisation
Techmorrow	✓ Reduced labour/time	✓ Yields		△ Input optimisation
Infuse			✓ Timely relief claims
HandySense	✓ 20% higher yields, 10% less labour [83]	✓ 20% income increases [82]		△ Input optimisation
RDL	✓ Timely intervention	△ Yield loss prevention
DeFire	△ Improved soil condition	✓ 300 THB/tCO2e *		✓ Stubble upcycling		✓ Carbon MRV
Spiro Carbon	✓ Grain filling, root growth, and nutrient uptake	✓ 400 THB/tCO2e * 15% income increases [88]		✓ Reduced water usage, input run-off		✓ Carbon MRV
EasyRice		✓ Finer grading, fair pricing			✓ Defect and impurity detection		✓ Pre-shipment rejection prevention

Note: ✓ = direct, △ = indirect, * = figures quoted from interviews.

indicates that ATPs’ direct and quantifiable contributions to social good are predominantly economic. Their primary objectives involve reducing input costs, enhancing yields, and improving market connectivity—all of which collectively aim to increase smallholder incomes. Indeed, nine out of eleven ATPs explicitly assert a direct and positive impact on farmers’ incomes, with reported farmer-level improvements in yields, prices, or net profits consistently ranging between 15% and 22%. Given that agriculture consistently ranks as Thailand’s lowest-remunerated occupation, it is unsurprising that most ATPs define their contributions to social good primarily through their capacity to uplift smallholder incomes.

This economically oriented approach not only characterises ATP practices but also mirrors the priorities of state actors facilitating farmer–ATP partnerships. For instance, promotional materials from the NIA’s AgTech Connext [109] highlight agricultural technologies through slogans such as “making work easier, improving productivity, reducing costs, and increasing income”.

Within this framework, sustainability appears as a secondary consideration in Thailand’s agricultural context. Although ATPs involved in farm advisory and automation regularly document and advertise their economic impacts, quantifiable data on how precision farming reduces input usage and contributes to better preservation of natural resources remain noticeably underreported. ListenField is the only farm management platform that reports a reduction in fertiliser use by 25% in pilot communities [77], thanks to its real-time soil nutrition analysis and crop health monitoring, which help farmers apply fertilisers and chemicals only where and when needed.

9.2.2. Hybrid Sustainability Model

Contrary to the findings of Ryan [65] where sustainability often occupies a central role in the discourse on Agricultural AI, the environmental focus in Thailand frequently exhibits a “hybrid” model [21], where, to progress to a transformative agenda and out-of-convention practices, it needs to be justified through economic benefits. The logic is straightforward—farmers need to “do well” financially when they are expected to “do good” for society.

A clear illustration is Ricult’s agri-waste scheme. Post-harvest, smallholders typically torch cassava rhizomes because baling and hauling them costs time, labour, and cash. Ricult solves the incentive problem: it aggregates residues from nine provinces, guarantees collection, pays within 48 h, and lifts monthly farm income by about THB 18,000 [74]. Farmers now have a clear incentive to bale residues instead of lighting them, overcoming the labour- and transport-cost barriers that made burning the cheapest option. A similar incentive architecture underpins Spiro Carbon and DeFire, which reward climate-smart practices with carbon-credit revenue.

Greenwashing is always a concern when strong economic motives drive sustainability-oriented initiatives. In this regard, Spiro Carbon is committed to upholding the credibility and transparency of its credit conversion to mitigate greenwashing:

When the farmer registered a 50-rai plot, the expectation was to receive full credit for the total land use. However, in reality, the 50 rai aren’t all on the same level, with low-lying and elevated areas, which causes the soil to dry unevenly. After we used AI to analyse the images, only about 17 rai were undergoing wetting and drying cycles.

Beyond honest dialogue with farmers, Spiro Carbon mints an NFT (non-fungible tokens)—essentially a tamper-proof digital certificate—each time it completes a monitoring cycle. Because every credit issued later carries the smart-contract address of its underlying NFT, buyers and regulators can click the address in a public blockchain explorer and inspect the supporting evidence that justifies the claimed tonnes of CO2e.

In return for thorough carbon verification, the company does not ask for a land ownership or lease contract as part of the registration process. In the interview, Spiro Carbon states:

Greenhouse gas reductions derive from farming activities, not from land ownership; consequently, the reward for such positive action should go to the farmer rather than the landowner, who may be a different person altogether.

This criterion would benefit elderly, female, or disabled farmers who often lack formal land titles. Spiro Carbon’s deliberate commitment to environmental and social justice slightly pushes the horizon of how social good can be operationalised in the sector.

9.2.3. Influences on Environmental Goal Setting

The environmental focus among Agricultural Technology Platform (ATP) providers in Thailand exhibits a noticeable divergence, strongly correlated with their engagement with international stakeholders. A clear trend emerges: ATPs with significant international exposure tend to demonstrate a stronger commitment to environmental goals compared to those primarily confined to the domestic market.

This trend is particularly evident among farm management platform providers. ListenField, which benefits from support from the Japanese government and operates across five countries, has set an ambitious target to reduce GHG emissions by 48% by 2030 relative to 2021 levels [75]. Similarly, Ricult, with operations spanning Thailand, Vietnam, and Pakistan and funding from the Gates Foundation, is strategically pivoting towards carbon traceability solutions [73]. Such international funding and market access create a strong external impetus for these ATPs to align with global sustainability standards and develop features that meet these requirements.

In stark contrast, ATPs focused predominantly on the domestic market are relatively slow to embrace environmental objectives as a core part of their value proposition or business model. For example, Farmbook, despite being an early adopter of traceability services, has not yet integrated comprehensive environmental metrics like carbon footprint monitoring into its current offerings.

This divergence underscores that domestically focused ATPs face a local market where the immediate economic return on investment for farmers often outweighs the perceived value or urgency of environmental metrics alone. Adoption barriers such as cost, technical knowledge, and the immediate need to improve livelihoods mean that environmental features may only gain traction domestically if they can be linked to short-term financial gains or are mandated by future domestic policies.

10. Tensions in Hybrid Intersection

The empirical analysis reveals how Thailand’s Agricultural AI landscape embodies the hybrid intersection’s core paradox: platforms deliver genuine social good through immediate economic relief while simultaneously reproducing structural constraints that limit transformative change. This section synthesises key findings to illuminate two systemic tensions that constrain Agricultural AI’s potential for deeper transformation: (1) the fine line between data commodification and social good delivery, and (2) the proclivity of market optimisation over systemic reform. These tensions demonstrate how the hybrid intersection operates, not through deliberate exploitation but through institutional arrangements that channel social good efforts toward incremental rather than emancipatory outcomes.

10.1. Intensified Profitability Pressure vs. Integrity of Social Good Delivery

Ricult features prominently in this study because its service stack is unusually broad: core modules for crop advice, health monitoring, market matching, and credit scoring sit alongside its agri-waste residue-aggregation scheme, giving the firm touch-points across almost every social-good theme. That breadth is funded by a “free-to-farmers, pay-with-data” model. To make its datasets marketable, Ricult must satisfy the four “Vs” of big data—volume, velocity, variety, and veracity [40,110]—a requirement that drives the continuous collection and algorithmic inference of ever-richer farmer profiles. This echoes the paradox identified by Magalhães and Couldry [111]: in delivering social good, companies simultaneously “take away”. The result is a structural tension: the company can deliver genuine public benefit, yet the same data engine could subordinate farmers’ interests to those of paying customers if left unchecked.

That concern has sharpened in the post-“easy money” era. Ricult’s founder admits that “a large user base and a worthy social mission are no longer enough; we must show hard profitability” [112]. An early attempt to charge farmers THB 1 per day for advice saw engagement dropped by 90% [71], forcing a pivot to the “forever-free” Bai Mai app and intensifying pressure to monetise every byte via data brokerage and gross-profit shares. Although no evidence yet suggests direct harm or trust breaches, scholars warn that such models can slide into “surveillance capitalism”—the systematic prediction and modification of behaviour for revenue and control [37]. Repetition normalises the practice: farmers become “favourably disposed” [113], valuing the immediate benefits while discounting the long-term loss of data autonomy. Without external audits, consumer watchdogs or activist investors, market discipline is weak and malpractice hard to spot.

In short, profitability pressures as equity funding becomes more stringent are driving Agricultural AI firms to harvest and trade ever more granular data, yet the governance architecture—regulatory, market, and civil society—has not kept pace. Robust guardrails, independent audits, and enforceable rights to contest algorithmic outcomes are now essential if the promise of data-enabled agriculture is to uplift farmers rather than quietly disempower them. These findings exemplify the hybrid intersection’s defining characteristic: the co-existence of genuine social benefit with structural constraint reproduction. Unlike the traditional intersection (which subordinates farmer welfare to economic growth) or the transformative intersection (which would challenge underlying power structures), the hybrid intersection delivers measurable improvements while maintaining the systemic conditions that necessitate external intervention. ATPs genuinely address farmers’ pain points—yield optimisation, market access, financial inclusion—yet the institutional mechanisms required for commercial sustainability necessarily embed farmers more deeply within platform-controlled ecosystems.

This pattern reflects what Schiff [21] identifies as “transformative objectives negotiated within traditional policy and incentive structures”. Agricultural AI providers operate with genuine social good intentions, but market-driven innovation systems require revenue models that extract value from user interactions. The result is not exploitation but rather a form of “constrained transformation” where beneficial outcomes coexist with deepened dependencies.

10.2. Market Optimisation over Structural Reform

The second tension emerges from the institutional constraints that channel AI applications toward market optimisation rather than structural transformation. This reflects not a failure of vision but the practical limitations of market-driven innovation systems when addressing systemic challenges. Agricultural AI effectively tackles productivity and efficiency gaps—optimising inputs, improving market access, enhancing financial inclusion—yet these interventions operate within existing power structures rather than challenging them.

While these interventions are undoubtedly valuable, they reflect a form of incrementalism that dominates the framing of social good contributions in agricultural AI. The government’s approach to agricultural AI likewise mirrors incrementalism stance, primarily providing cost subsidies to farmers so they can adopt PA solutions or use niche, state-developed AI tools. This incremental approach often prioritises immediate, measurable improvements while leaving deeper systemic issues—such as inadequate irrigation infrastructure—largely unaddressed. Creating a climate-resilient infrastructure is critical for agricultural production as Thailand will face prolonged periods of drought punctuated by sudden and severe floods [114]. Addressing such structural challenges, which demand transformative rather than incremental change, falls beyond the purview of private, profit-oriented initiatives. Instead, these challenges require substantial public investment and long-term policy commitments to facilitate systemic reform.

While AI alone cannot resolve water scarcity, it has significant potential to enhance decision-making in irrigation infrastructure planning. Limited irrigation access often stems from a lack of nearby water reservoirs or inadequate pipeline networks to transport water from reservoirs to farmland. AI, embedded in a decision support system, can support government agencies in identifying and prioritising regions most in need of infrastructure development by integrating diverse datasets—such as drought severity, water availability, soil conditions, and farmer demographics. These AI-driven insights can enable more strategic resource allocation, which is particularly crucial given budget constraints and regional disparities in infrastructure development.

However, the primary barrier to systemic reform is not technological availability but political will and policy direction. This challenge is especially evident given the existence of well-developed agricultural data platforms like Agri-Map (https://agri-map-online.moac.go.th, accessed on 5 May 2025) and THAGRI (https://www.thagri.in.th/about, accessed on 5 May 2025), which already provide valuable datasets for agricultural policy planning. The tools and knowledge are readily available—what remains is the political commitment to harness AI for systemic reform, ensuring its use contributes not only to efficiency but also to structural transformation and long-term resilience. This incrementalist approach, which lacks a strategic plan for structural reform, aligns with the hybrid intersection hypothesis. AI is leveraged as a technofix to optimise market efficiency and address immediate societal challenges, yet it does not fundamentally reform the structures that perpetuate deprivation.

As Inglis [115] explains, empowerment enables individuals to develop capacities to act successfully within existing systems and power structures, whereas emancipation entails critically analysing, resisting, and challenging these structures. To achieve both economic betterment and environmental resilience, AI must be both emancipatory and empowering. For AI to be emancipatory, it must support authorities in strategically planning nationwide infrastructure development with minimal capital investment, reducing disparities in access to reliable water sources. For AI to be empowering, once irrigation systems are in place, it must enable farmers to optimise water usage—preventing over-irrigation, which can be counterproductive to yields and exacerbate GHG emissions. By balancing these two dimensions, AI can transcend its current role as a market optimiser and contribute to a more just and sustainable agricultural system.

11. Conclusions

Placing the hybrid intersection at the centre of the analysis clarifies how Agricultural AI in Thailand simultaneously delivers tangible benefits to smallholder farmers and reproduces structural constraints that limit transformative change. The concept captures an uneasy balance: AI services are deployed to relieve immediate economic pressures—raising yields, lowering costs, widening credit access—yet they are framed and assessed almost exclusively through a productivist lens. As a result, notions of social good become narrowly equated with short-term income gains, while environmental stewardship, gender equity and other longer-horizon objectives remain peripheral.

Because many of AI services are offered “free”, their business architecture relies on monetising granular farm and personal data. This model intensifies a first tension within the hybrid intersection: the dependence fosters data commodification. This does not imply that poverty-stricken farmers should not seek farm improvement through free-of-charge AI services or that commodifying data is inherently unethical. Instead, it highlights the need for critical awareness from farmers when engaging with AI services and active oversight from regulatory bodies and watchdogs. Currently, the sector is primarily underpinned by a governance norm that privileges functional over procedural trust. Farmers can readily observe that models “work” in the field, but the algorithms remain opaque, contractual clauses disclaim liability for error, and statutory protections to contest automated decisions or share in data value are embryonic. The resulting asymmetry leaves smallholders as passive data suppliers and risk bearers, even as they benefit from immediate efficiency gains.

Thailand’s Agricultural AI landscape demonstrates both the possibilities and limitations of market-driven approaches to social good. ATPs have created genuine value for smallholder farmers while operating within structural constraints that shape how that value can be created and distributed. The hybrid intersection thus represents not a failure of intention but a reflection of broader challenges in aligning commercial viability with transformative social outcomes. Recognising the hybrid intersection’s constraints is a prerequisite for moving beyond incrementalism. Policymakers should pair AI adoption incentives with (i) statutory rights to contest automated decisions and share in data value, (ii) independent audits that verify both accuracy and fairness, and (iii) public investment that channels AI analytics towards long-horizon, climate-resilient infrastructure choices (e.g., strategic irrigation). For practitioners, embedding gender-responsive and environmental metrics from the outset would broaden the notion of social good and mitigate over-reliance on a single revenue–data trade-off.

Finally, future research should test whether analogous patterns emerge in other Global South contexts, examine how different institutional arrangements might enable more transformative outcomes, and develop frameworks for evaluating when hybrid intersection approaches serve as stepping stones toward deeper change versus when they entrench existing limitations. The goal is not to eliminate the hybrid intersection—which delivers genuine benefits to marginalised populations—but to understand how its constraints might be progressively relaxed while preserving its capacity for immediate social good delivery.