Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9, 1239-1253 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 22 July 2025; Revised: 8 August 2025; Accepted: 12 August 2025; Published: 22 September 2025 * Correspondence: alexander.hernandez@lpu.edu.ph Examining small farm holders’ adoption intention on artificial intelligence of things for sustainable agriculture in developing country: A structural equation modelling assessment Alexander A. Hernandez1*, Suchita Manajit2, Jessa Frida T. Festijo3, Jennifer D. Tucpi4 1Information Technology Department, College of Technology, Lyceum of the Philippines University, Manila, Philippines; alexander.hernandez@lpu.edu.ph (A.A.H.). 2Walailak University International College, Walailak University, Nakhon Si Thammarat, Thailand. 3Research and Innovation Center, Lyceum of the Philippines University, Manila, Philippines. 4Academic Affairs Office, Lyceum of the Philippines University, Manila, Philippines. Abstract: Artificial Intelligence of Things (AIoT) plays a crucial role in promoting sustainable farming and enhancing productivity. This emerging technology is increasingly adopted in developed countries; however, it remains under investigation in developing nations due to its novelty, contextual challenges, and support factors. Consequently, the adoption rate among farmers with small holdings is relatively low. This study addresses this research gap by developing an extended framework based on the Unified Theory of Acceptance and Use of Technology (UTAUT), incorporating perceived gains and sustainability factors to provide a comprehensive understanding of farmers' adoption intentions. The analysis employs measurement and structural models using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results indicate that performance expectancy, effort expectancy, social influence, trust, and government support significantly influence the intention to adopt AIoT. Conversely, facilitating conditions and price value were found to be insignificant. Moreover, perceived gains and sustainability-related factors exert the most substantial impact on the intention to use AIoT among small farm holders, suggesting that these farmers have a positive attitude towards adopting AIoT for smart and sustainable agricultural practices, particularly in the Philippines. Keywords: Artificial intelligence of things, Artificial intelligence, Developing country, Small farm holders, Sustainable agriculture. 1. Introduction Agriculture is a sector in developing countries that contributes to gross domestic product. At present, agriculture output accounts for 50% to 60% in the Philippines, Thailand, and Indonesia, respectively [1]. Although agriculture has overgrown at some time, it still suffers problems due to inaccessibility and expensive integration of emerging technologies such as artificial intelligence and internet of things to enhance throughput and productivity [2, 3]. Also, a decline in the agriculture sector’s performance is affecting the stability of the supply chain in developing countries, such as Thailand, the Philippines, and Indonesia. As a result, the prices of basic commodities are getting more expensive. While there is a decline, farmers’ shortages happen as they experience increasing farming expenses, insufficient farming support, and changes in employment priority from farming to non- farming for better salaries [4]. As these problems continue to exist, at this technologically enabled period, developing countries are trying to cope with agricultural challenges using artificial intelligence, the Internet of Things, and blockchain, among others [5]. More recently, artificial intelligence of things (AIoT) is progressively adopted for smart agriculture to enable technology assisted farming practices in developing countries [6]. First, AIoT is adopted in 1240 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate developed countries as mature infrastructure and availability of devices to build AIoT systems and implement it Sharma, et al. [7]. However, there are limited studies investigating sustainability and climate-change related factors to provide in-depth understanding for its present contextual relevance to developing countries evidently suffering from this climate vulnerability and resource constraints [8]. Second, the Philippines, as a developing country with strong reliance on agriculture, needs to be investigated, as it presents a unique case for studying AIoT adoption as agriculture is one of the most climate sensitive sectors in the Philippines, and other developing countries due to climate change and serious disaster experience. Third, small farm holders' intentions to use AIoT, is not as much explored, making it unclear to determine their reception of this innovative solution to smart farming adoption. This study develops an extended unified theory of acceptance and use of technology (UTAUT) model on the adoption intention-based literature and survey data analyzed through partial least squares and structural equation modeling (PLS-SEM). By considering the research gaps, the study contributes by providing evidence of adoption intention with consideration of sustainability (green) and perceived gain-related factors, to uncover the relationships and significance of the factors under investigation for small farm holders in the Philippines and other developing countries. The paper follows a structured format, comprising literature review, hypothesis development, materials and methods, results discussion, conclusion and future work. 2. Literature Review Artificial Intelligence of Things (AIoT) is an emerging technology for smart agriculture. It combines the capabilities of Artificial Intelligence (AI) with the Internet of Things (IoT) [9]. In relation to agriculture, AIoT uses IoT devices to collect environmental data such as soil moisture, temperature, and humidity, among others, complemented by the AI algorithms as applied to analyzing data and making real-time decisions or predictions [10], (e.g., optimizing fertilizer use, irrigation scheduling, predicting infestation outbreaks, smart harvesting, and supply chain management) [11, 12]. Thus, AIoT is a broader concept that focuses on intelligence in interconnected devices that increasingly plays an important role in smart (sustainable) agriculture. Previous studies were conducted to determine the factors influencing the adoption intention of AIoT for smart farming in developing countries. In a recent study, AIoT was explained by providing evidence of adoption intention for crop management, specifically growth monitoring and yield production [13]. Results show that there is moderate to high acceptance of AIoT based on the performance expectancy, effort expectancy, facilitating conditions, cost, and price value. Similarly, a recent study suggests that usefulness, ease of use, cost, and support from the government are crucial factors for its use and continuance, leading to sustainable agricultural conditions. Further, another study found that perceived usefulness, institutions as contextual factors, and social influence play a significant role in the adoption of climate smart agriculture technologies [14]. On the other hand, there are a few studies that examine the influence of cost of investments and targeted price of the crops, sustainability, and climate-related factors when using AIoT for smart agriculture and precision farming practices, in developing countries [15]. In this paper, information systems models and theories were considered such as the unified theory of acceptance and use of technology (UTAUT). The UTAUT focuses on aspects of performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation [16]. The UTAUT was chosen as it provides a comprehensive framework that focuses on use intention and behavior, predictive capability for emerging technologies [17], and relevance to the context of introducing AIoT in agriculture, especially the complex settings and nature of small farm holders in developing countries. Several studies proposed an extended UTAUT model that considers technological and non- technological factors to explain the acceptance or rejection of individuals to new technologies [18]. Another study considered developing an extended UTAUT model by highlighting the crucial role of effort expectancy, performance expectancy, facilitating conditions, hedonic motivation, government 1241 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate support, price value, personal innovativeness, and trust as significant factors to the adoption intention of IoT for agriculture in Bangladesh [19]. Similarly, another study investigated the intention to use ICTs in agriculture’s productivity in Zambia and found that performance expectancy, facilitating conditions, effort expectancy, and social influence had a positive relationship with intention [20]. However, there is a dearth of studies explaining sustainable and perceived gain factors related to AIoT adoption intention and use in developing countries, such as the Philippines. Thus, this research aims to address these research gaps. 2.1. Hypothesis Development Performance expectancy is the extent to which an individual believes that an emerging technology can achieve his or her task or goal and reach productivity. Previous studies found that performance expectancy positively impacts adoption intention in artificial intelligence in smart agriculture [19, 21]. Further, a recent study found that the use of the Internet of Things was found effective and assisted in productive smart farming decisions [22]. In another study, performance expectancy contributes to the use behavior of IoT in agrotourism in Malaysia [23]. When performance expectancy is positively perceived by small farm holders (SFHs) in AIoT for sustainable agriculture, it will more likely be adopted. As a result, this study proposes the hypothesis that: H1: Performance expectancy has a positive significant impact on the AIoT adoption intention in SFHs. Effort expectancy is the level of convenience and user-friendliness that individuals feel when using an information system [24]. Prior studies suggest that effort expectancy contributes to adoption intention and continuance of emerging technologies [25]. In a recent study, it was found that effort expectancy, network prominence, and environmental uncertainty, which led to use and continuance of digital platforms for sustainable and successful agricultural ecosystems [26]. This present study operationalizes effort expectancy as the convenience and user-friendliness that small farm holders perceive about AIoT. As a result, this study proposes the hypothesis that: H2: Effort expectancy has a positive significant impact on the AIoT adoption intention in SFHs. Social influence is the extent to which an individual believes that important others believe that he or she should use a new system [27]. A recent study suggests that peers and friends' experience contribute to the adoption intention and use of intelligent hog farming technology [28]. Besides, peer reviews and satisfaction were noted as influential to convince farmers to try artificial intelligence to enhance farming productivity and reduce monitoring challenges [29]. On the contrary, a study presents the insignificance of social influence on the intention to adopt IoT for farming [30]. This study operationalizes the social influence of farmers related to AIoT [31]. When farmers receive positive reviews from farming peers, AIoT will be more likely adopted. As a result, this study proposes the hypothesis that: H3: Social Influence has a positive significant impact on the AIoT adoption intention in SFHs. Facilitating conditions refer to the extent to which technical structure and organizational resources are available to support the use of an emerging system [27]. Previous studies found that technical infrastructure such as connectivity, weather information stations, cloud computing, and sensors [32]. A recent study found that facilitating conditions contribute to the positive intention, use, and continuance of artificial intelligence and internet of things for smart farming [33]. Similarly, facilitating conditions were a significant contributor [34]. This study operationalizes facilitating conditions as the essential infrastructure such as connectivity, IoT sensors, weather information station, and mobile applications are available to pursue AIoT for smart agriculture. When these facilitating conditions are made 1242 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate available, individuals engaged in smart farming will have positive intention and use behavior. As a result, this study proposes the hypothesis that: H4: Facilitating conditions has a positive significant impact on the AIoT adoption intention in SFHs. Price value refers to the cost of adopting AIoT solutions and the value or return that these AIoT bring to sustainable and smart agriculture [35]. Previous studies found price value’s significance in adoption intention and use behavior [36, 37]. Similarly, another study found that price value contributes to positive acceptance of AI and IoT for smart farming practices [38]. Besides, when benefits derived from AIoT investments justify its costs, SFHs will likely adopt and continue to use AIoT. As a result, this study proposes the hypothesis that: H5: Price value has a positive significant impact on the AIoT adoption intention in SFHs. Trust refers to the confidence or reliance in a specific technology. When AIoT can confidently deliver its promises for green and sustainable agriculture, small farm holders will likely adopt it [39]. Previous studies have established a positive association between trust and behavioral intention in smart agriculture and precision farming [40, 41]. Moreover, a recent study found that trust is a vital factor in the adoption of smart farming technology [42]. In addition, another study suggests that trust has a significant link with use behavior on climate-smart agriculture in most countries investigated [43]. As a result, this study develops the hypothesis that: H6: Trust will have significant positive impact on the AIoT adoption intention of small farm holders. Government Support refers to the government’s climate adaptation programs to support green and lean practices leading to sustainability [44]. Previous studies found that climate adaptive programs for smart agriculture positively affect adoption intention [45]. Previous studies emphasized the government’s significant contribution to a positive acceptance of IoT for smart agriculture [46]. Similarly, individuals who received sufficient support from the government continue to adopt artificial intelligence for smart farming [32]. This study operationalizes support for promotion and training, guidelines, and infrastructure to encourage adoption of AIoT for smart agriculture. When there is government support available to adopt emerging technologies, SFHs will likely adopt AIoT for sustainable agriculture. As a result, this study proposes the hypothesis that: H7: Government support will have a significant positive impact on the AIoT adoption intention of small farm holders. Resource Conservation is the perception of small farm holders that artificial intelligence of things offers financial benefit through using fewer resources. Previous studies found that the internet of things and artificial intelligence allow farmers to conserve water usage [47] through real-time data in scarce areas, and reduce reliance on irrigation facilities, applying fertilizers more efficiently, preventing wastage [48, 49] could lead to resource conservation [50, 51]. When small farm holders perceive that AIoT could result in resource conservation, it will more likely be adopted and considered a gain. Thus, this study proposes the hypothesis that: H8: Resource conservation will have a positive significant impact on the AIoT adoption intention of small farm holders. Operating Cost is the belief that using AIoT for smart agriculture will save operating expenditures. Previous studies found that AIoT can reduce farming operations by automating repetitive tasks, 1243 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate reducing labor costs, and human errors [52, 53]. In a recent study, artificial intelligence and the internet of things were used for real-time monitoring and data-driven decisions [54], resulting in reduced operating costs and downtime of farm operations [55]. When small farm holders perceive that AIoT can result in operating cost savings, it will more likely be adopted for sustainable agriculture and farming activities. As a result, this study proposes the hypothesis that: H9: Operating cost saving will have significant positive impact on the AIoT adoption intention of small farm holders Increased Production is the expectation that using AIoT will increase the productivity of the farm. Previous studies suggest that artificial intelligence and the internet of things play a vital role in sustainable agriculture [56]. In a recent study, it was found that artificial intelligence with IoT can result in crop yield of small farm holders by forty percent (40%) [57]. Similarly, another study suggests that by using AIoT, a projected 30% is expected to generate greater profitability with the same resources for small farm holders’ productivity and growth in business. When small farm holders perceive that AIoT could lead to increased production of farms, it will more likely be adopted. As a result, this study proposes the hypothesis that: H10: Increased production will have significant positive impact on the AIoT adoption intention of small farm holders Eco-Friendly farming refers to the expectation that using AIoT will promote environmentally friendly farming practices and reduce its environmental impact. Previous studies suggested that the internet of things combined with artificial intelligence can help minimize the environmental impact of farming practices [58]. On the other hand, studies found that its combination introduces new approaches to protecting the natural resources used or surrounding farms [59, 60]. As a result, these innovations can make farming sustainable for future generations. Hence, this study proposes the hypothesis that: H11: Eco-Friendly Farming will have significant positive impact on the AIoT adoption intention of small farm holders. Energy Efficiency refers to the expectation that AIoT based equipment and agricultural machinery with optimized algorithms, through solar-based AIoT devices, can reduce energy usage in farming operations [61, 62]. Previous studies found that IoT and AI introduced energy-efficient practices are vital for greening and sustainable agriculture [63]. A recent study found that energy-efficient IoT and AI applied to agriculture can result in cost-effective and eco-friendly farming practices [64]. When small farm holders perceive that AIoT for agriculture promotes energy efficiency, it will be more widely adopted. Thus, this study suggests that hypothesis that: H12: Perceived Energy Efficiency will have significant positive impact on the AIoT adoption intention of small farm holders. Behavioral intention refers to the individual’s relative strength of intention to perform a behavior in the near future. Previous studies found that an individual's motivation can predict acceptance and continuance of use of new technology. In this study, the intention to use AIoT is measured. 1244 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate Figure 1. Conceptual Framework on AIoT adoption intention in SFHs. 3. Materials and Methods The survey instrument was developed based on the conceptual framework developed with prior studies as a source of survey questions. The survey items were modified to consider the current context of small farm holders and smart agriculture. The survey was structured with two parts covering (1) profile of the respondents and (2) adoption intention questions. The survey has 39 items covering adoption intention question items on performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), price value (PV), trust (TR), government support (GS), eco- friendly farming (EF), energy efficiency (ENR), resource conservation (RC), operating costs (OC), increased production (IP), and intention to use (ITU). The survey uses a 7-point Likert’s scale to determine the agreement or disagreement of respondents for survey items, from strongly disagree (1) to strongly agree (7). Initially, the survey was deployed to 50 respondents to check their understanding of the questions. Further, the survey was also reviewed by technology adoption research experts. Comments and suggestions were reflected in the revised survey. The final survey version was created in Google Forms, to provide ease of access and deployment to the respondents’ email, text messages, and social media accounts. This study applies purposive sampling of small farm holders in the Philippines. The farmers were from different provinces such as Regions 2, Region 3, Region 4A-4B, Region 5, and Region 6. Purposive 1245 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate sampling is applicable as these SMFs have agricultural contributions, understand resource constraints and efficiency, need support for agriculture as they are vulnerable to climate change and environmental risks, localized farming practices, and can drive crop productivity and reduce poverty. Local government units and agriculture offices were contacted to determine the small farm holders in these regions. After such, the SFH were contacted through their registered mobile numbers via call or text messages. To comply with ethics in research, an invitation to participate in the survey was sent to the respondents, and confirmed participants were stored in a secured Google Sheet as a reference for the deployment of the survey. Also, informed consent was provided to the participants to ensure adherence to ethics in research. The survey was deployed through the SFHs’ contact number, email, and social media accounts in October - November 2024. The survey turnout was 78% (n = 446) from the confirmed respondents. From this number, all 446 completed responses were used for modeling and analysis. For this study, Smart PLS 4.1 was used to examine the measurement and structural models. The respondents were distributed into male (n=218, 48%) and female (n=228, 52%) groups. The respondents have considerable years of small farming experience, entailing 1-3 years (n=35, 7.8%), 4-6 years (n=121, 27.13%), and 7-9 years (n=123, 27.63%), and over 10 years (n=167, 37.44%). The respondents have experience with mobile apps and devices, as considerable in the study in which AIoT applications could be made accessible for use. Table 1. Demographic profile of the respondents. Attributes Frequency (%) Gender ● Male 218 (48%) ● Female 228 (52%) Education Level ● High School 138 (31%) ● Bachelor’s 246 (55%) ● Master’s or Doctorate 62 (14%) Years of Farming Experience ● 1 - 3 years 35 (7.8%) ● 4-6 years 121 (27.13%) ● 7-9 years 123 (27.63%) ● 10 and over 167 (37.44%) Income ● Below $300 (USD) 64 (14.35%) ● $300 - $600 (USD) 163 (36.55%) ● $600 - $900 (USD) 183 (41.03%) ● Above $900 (USD) 36 (8.07%) 4. Results 4.1. Measurement Model The study assesses the measurement model by examining the reliability and validity aspects. Cronbach’s alpha test was assessed, resulting in a range of 0.6 - 0.9 for the items (acceptable to highly acceptable). Further, composite reliability (CR) resulted in 0.6 - 0.9 for all items. Average variance extracted (AVE) resulted in greater than 0.5 (>0.50). Factor loading confirms that each variable (indicator) investigated is correlated with its associated construct. Results show that the indicators meet the validity requirement, ranging from 0.700 to 0.798. Based on the measurement model results, all constructs fall within the acceptable benchmark values required before proceeding to the analysis of the structural model. Also, the model fit is 0.859. 1246 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate Table 2. Convergent validity. Construct Cronbach’s Alpha (0.6-0.95) AVE (>0.50) CR (rho_c) (0.6-0.95) Factor Loading (>0.7) Convergent Validity PE 0.816 0.556 0.847 0.715 - 0.725 Established EE 0.807 0.644 0.842 0.701 - 0.733 Established SI 0.837 0.688 0.857 0.735 - 0.740 Established FC 0.884 0.699 0.774 0.700 - 0.760 Established PV 0.811 0.652 0.844 0.702 - 0.748 Established TR 0.814 0.753 0.846 0.714 - 0.738 Established GS 0.828 0.775 0.854 0.730 - 0.730 Established RC 0.819 0.761 0.849 0.721 - 0.798 Established OC 0.836 0.739 0.854 0.730 - 0.738 Established IP 0.849 0.707 0.867 0.749 - 0.756 Established EF 0.840 0.762 0.861 0.701 - 0.733 Established ENR 0.838 0.720 0.861 0.723 - 0.754 Established In this study, the heterotrait monotrait ratio (HTMT) was also examined to determine discriminant validity of the constructs. Based on the results, all constructs fall under the acceptable threshold of <0.85, which confirms the discriminant aspect, as presented in Table 2. Similarly, the descriptive results were noted for the constructs such as performance expectancy (x̄ = 5.854, σ = 0.653), effort expectancy (x̄ = 5.763, σ = 0.633), social influence (x̄ = 5.743, σ = 0.671), facilitating conditions (x̄ = 5.451, σ = 0.673), government support (x̄ = 5.932, σ = 0.684), trust (x̄ = 5.984, σ = 0.649) price value (x̄ = 5.362, σ = 0.6832), eco-friendly (x̄ = 5.8522, σ = 0.672), energy-efficiency (x̄ = 5.77, σ = 0.681), resource conservation (x̄ = 5.914, σ = 0.674) operating cost saving (x̄ = 5.853, σ = 5.853), increased production (x̄ = 5.857, σ = 0.635), and intention (x̄ = 6.473, σ = 0.691). Overall, the results show moderate to strong agreement. Table 3. Heterotrait-monotrait ratio (<0.85). x ̄ σ OCS EE EF ENR FC GS IP ITU PE PV RC SI TR OCS 5.853 0.646 EE 5.763 0.633 0.548 EF 5.852 0.672 0.605 0.741 ENR 5.770 0.681 0.532 0.650 0.629 FC 5.451 0.673 0.501 0.627 0.733 0.638 GS 5.932 0.684 0.651 0.650 0.618 0.736 0.799 IP 5.857 0.635 0.633 0.646 0.734 0.616 0.761 0.742 ITU 6.473 0.691 0.653 0.639 0.739 0.713 0.666 0.728 0.791 PE 5.854 0.653 0.645 0.628 0.648 0.747 0.743 0.617 0.727 0.727 PV 5.362 0.6832 0.625 0.745 0.712 0.703 0.633 0.711 0.621 0.649 0.738 RC 5.914 0.674 0.632 0.653 0.603 0.634 0.701 0.734 0.712 0.721 0.716 0.719 SI 5.743 0.671 0.629 0.647 0.636 0.731 0.721 0.734 0.716 0.639 0.633 0.728 0.737 TR 5.984 0.649 0.627 0.626 0.724 0.728 0.717 0.778 0.714 0.732 0.703 0.717 0.706 0.742 4.2. Structural Model This study assessed the structural model by examining the r2, Q2 and coefficient values. The r2 is a number between 0 and 1 that measures how well a statistical model predicts an outcome. Based on the results, intention to use (r2 = 0.689) obtained 68.9% of the variance explaining its ability to explain the adoption intention, considering a benchmark of weak (0.25), moderate (0.50), and substantial (0.75). In terms of the predictive accuracy using Q2 values with benchmarks (higher than 0: small, 0.25: 0.25: medium, 0.50: large), intention to use AIoT results in Q2 = 0.521 value, demonstrating considerable predictive relevance among the endogenous variables. Based on the results, the model was able to 1247 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate establish its predictive relevance on AIoT adoption intention. Lastly, the coefficient values were assessed for all constructs and confirm the relationships, as presented in Table 3. Figure 2. Structural model on AIoT adoption intention in SFHs. Table 4. Hypothesis Testing. Hypothesis Path Coefficient (β) t-value p-value Result H1 PE → ITU 0.124 2.501 0.012 Supported H2 EE → ITU 0.182 2.415 0.000 Supported H3 SI → ITU 0.147 2.085 0.037 Supported H4 FC → ITU -0.245 1.431 0.153 Not Supported H5 PV → ITU -0.491 0.894 0.210 Not Supported H6 TR → ITU 0.131 1.753 0.023 Supported H7 GS → ITU 0.259 3.501 0.000 Supported H8 RC → ITU 0.241 3.613 0.000 Supported H9 OC → ITU 0.239 3.642 0.000 Supported H10 IP → ITU 0.276 2.864 0.000 Supported H11 EF → ITU 0.278 3.517 0.000 Supported H12 ENR → ITU 0.247 2.623 0.009 Supported Note: Significant at p-value < 0.05. 1248 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate This study confirms that performance expectancy has a significant positive relationship with intention to use (p = 0.124, β = 0.012). Thus, H1 is supported. Similarly, the effort expectancy is positively influential to intention (p = 0.182, β = 0.000). Thus, H2 is supported. While social influence is significantly impacting adoption intention (p = 0.147, β = 0.037). Hence, H3 is supported. Further, this study confirms the negative impact of facilitating conditions with intention to use (p = 0.153, β = - 0.245). Therefore, H4 is not supported. With regard to price value with adoption intention (p = 0.210, β = -0.491), this study found a negative impact. Hence, H5 is not supported. The results show that trust of AIoT with existing setup has a significant positive relationship with adoption intention (p = 0.023, β = 0.131). Hence, H6 is supported. With regard to government support, results show that it significantly affects the intention to use AIoT of SFHs (p = 0.000, β = 0.131). H7 is supported. In relation to sustainability-related factors, this study found that resource conservation has a significant positive impact on intention to use AIoT (p = 0.000, β = 0.241). Thus, H8 is supported. Similarly, operating cost saving as a factor shows a significant positive relationship with intention on AIoT (p = 0.000, β = 0.239) for smart agriculture, thus H9 is supported. Likewise, results show that increased production has a significant effect on intention to use AIoT (p = 0.000, β = 0.276). H10 is supported. When it comes to climate change-related factors, this study found that eco-friendly as a factor has a significant positive impact on intention to use (p = 0.000, β = 0.278). Thus, H11 is supported. While energy-efficiency construct is significant to adoption intention (p = 0.000, β = 0.247). Thus, H12 is supported. Similarly, the energy-efficiency dimension positively affects intention to adopt AIoT. 5. Discussion The results on performance expectancy are similar with prior studies, confirming its impact on adoption intention [65, 66]. This result indicates that SFHs consider significant importance on the aspect of usefulness and efficiency enhancements in adopting emerging technologies. Similarly, in the study of Miah, et al. [23] and Ena and Siewa [34], effort expectancy is found positively correlated with intention to use and use behavior, which indicates that effort expectancy can reduce perceived complexity and increase acceptance, among SFHs. While this study provides additional evidence that social influence significantly affects intention to use, similar to prior research, which indicates that social influence plays a crucial role to drive technology adoption decisions for SFHs. Also, facilitating conditions are incomparable with the study of Anubhav, et al. [32], which indicates that infrastructure, connectivity, data, and mobile apps can contribute to SFHs’ decision to adopt AIoT. While price value is found insignificant, this result is inconsistent with prior work of Bahari, et al. [38], which indicates that value that can be derived from AIoT investment is imperative to pursue it for smart agriculture. In terms of trust factor, the present study is closely similar with prior studies [67, 68], which indicates that SFHs perceive trust as critical to the adoption and sustained use of AIoT technologies for agriculture. Similarly, government support is a crucial element of emerging technologies in agricultural countries, which indicates that SFHs have considerable positive attention to government support to pursue AIoT for smart agriculture. In terms of the perceived gains, this study confirms resource conservation as essential to SFH intention to use, which indicates that SFHs' focus on efficient utilization of resources can lead to positive economic and environmental gains. Similarly, this study confirms that operating costs that SFHs expected returns on AIoT’s for smart agriculture, which indicates it could lead to sustainable agriculture operations. Also, the result on increased production is similar to prior work of Piancharoenwong and Badir [69], which indicates that the SFHs positively perceive that AIoT can lead to anticipated crop yield production gains. Also, the results show that AIoT can be seen as advantageous in attaining eco-friendly agriculture and farming practices, which indicates that it can contribute to greening and sustainable agriculture. Besides, the ability of AIoT to introduce energy efficiency for agriculture is projected to bring balance to farm operations and environmental benefits. 1249 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate This study contributes to theory by providing evidence of perceived gains (resource conservation, cost savings, increased production), and sustainability factors (eco-friendly, energy-efficient), to understand adoption intention of AIoT from a developing country, the Philippines. Second, AIoT is not yet in full diffusion and implementation in the Philippines due to facilitating factors such as the internet and data, and prices to set up AIoT. Third, this research provides evidence of factors influential to its future adoption. Moreso, the result of this work confirms the role of government support and trust, which were underexplored in past studies of AIoT adoption intention, among small farm holders, both in developed and developing countries. In practice, this study contributes by providing guidance to the development of policies to further enable the diffusion of AIoT in the Philippines, as a developing country. Small farm holders need government support for awareness and capability building to develop an understanding of its benefits and limitations. Moreso, the government can provide measures to institutionalize programs that enable technology-driven farming such as AIoT while climate change affects SFMs’ productivity. Further, the results could provide foundational support to aid small farm holders with necessary tools and technologies to enhance agricultural productivity, resource management, and promote sustainable farming practices. In this way, small farm holders can be assisted in how to scale AIoT in the Philippines and adapt to other emerging economies with similar socio-economic and environmental issues. 6. Conclusion In this study, adoption intention of AIoT among small farm holders in a developing country is highlighted. A survey was deployed, and partial least squares and structural equation modeling are used to analyze the factors in the conceptual framework. Based on the results, performance expectancy, effort expectancy, social influence, trust, and government support factors were significant in the adoption intention of AIoT, while facilitating conditions and price value did not impact intention to use AIoT for SMFs. Moreso, perceived gain (resource conservation, cost saving, increased production), and sustainability (eco-friendly and energy efficiency) related factors have the most significant impact on intention to use, which indicates the future positive role of AIoT adoption among small farm holders in the Philippines. This study presents some limitations that future studies may address. Firstly, this study investigated AIoT in a developing country, the Philippines. Thus, the results may not reflect other developing countries and could not be used for generalization. Future studies may consider expanding the contribution of the study and understanding in developing countries. Secondly, this study did not cover issues and challenges faced by small farm holders to pursue AIoT for sustainable and smart agriculture. Lastly, the study did not explore the influence of innovation hubs and institutions as contextual factors in the adoption intention of AIoT. Future research may explore this area to clarify its impact on adoption intention in AIoT. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgments: The authors are grateful to the small farm holders who participated in the study. Further, the authors are thankful for the constructive comments from reviewers of this article. 1250 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate Copyright: © 2025 by the authors. This open-access article is distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] R. Tansuchat, S. Suriyankietkaew, P. Petison, K. Punjaisri, and S. Nimsai, "Impacts of COVID-19 on sustainable agriculture value chain development in Thailand and asean," Sustainability, vol. 14, no. 20, p. 12985, 2022. https://doi.org/10.3390/su142012985 [2] V. V. Hoang, "Investigating the agricultural competitiveness of ASEAN countries," Journal of Economic Studies, vol. 47, no. 2, pp. 307-332, 2020. [3] C. Balingbing et al., "Precision land leveling for sustainable rice production: Case studies in Cambodia, Thailand, Philippines, Vietnam, and India," Precision Agriculture, vol. 23, no. 5, pp. 1633-1652, 2022. [4] J. Liu, M. Wang, L. Yang, S. Rahman, and S. Sriboonchitta, "Agricultural productivity growth and its determinants in south and southeast asian countries," Sustainability, vol. 12, no. 12, p. 4981, 2020. https://doi.org/10.3390/su12124981 [5] A. T. Shaikh, T. Rasool, and F. Rasheed Lone, "Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming," Computers and Electronics in Agriculture, vol. 198, p. 107119, 2022. https://doi.org/10.1016/j.compag.2022.107119 [6] F. K. Shaikh, S. Karim, S. Zeadally, and J. Nebhen, "Recent trends in internet-of-things-enabled sensor technologies for smart agriculture," IEEE Internet of Things Journal, vol. 9, no. 23, pp. 23583-23598, 2022. [7] A. Sharma et al., "Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture," Open Life Sciences, vol. 18, no. 1, 2023. https://doi.org/10.1515/biol-2022-0713 [8] R. K. Goel, C. S. Yadav, S. Vishnoi, and R. Rastogi, "Smart agriculture – Urgent need of the day in developing countries," Sustainable Computing: Informatics and Systems, vol. 30, p. 100512, 2021. https://doi.org/10.1016/j.suscom.2021.100512 [9] H. K. Adli et al., "Recent advancements and challenges of aiot application in smart agriculture: A review," Sensors, vol. 23, no. 7, p. 3752, 2023. https://doi.org/10.3390/s23073752 [10] S. Mandal, A. Yadav, F. A. Panme, K. M. Devi, and S. Kumar S.M, "Adaption of smart applications in agriculture to enhance production," Smart Agricultural Technology, vol. 7, p. 100431, 2024. https://doi.org/10.1016/j.atech.2024.100431 [11] A. Y. Ismail et al., "Optimization of smart farming irrigation using IoT-based artificial neural network," in 2024 IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS), 2024. [12] A. Sharma, M. Georgi, M. Tregubenko, A. Tselykh, and A. Tselykh, "Enabling smart agriculture by implementing artificial intelligence and embedded sensing," Computers & Industrial Engineering, vol. 165, p. 107936, 2022. https://doi.org/10.1016/j.cie.2022.107936 [13] M. Jain, G. Soni, S. K. Mangla, D. Verma, V. P. Toshniwal, and B. Ramtiyal, "Mediating and moderating role of socioeconomic and technological factors in assessing farmers attitude towards adoption of Industry 4.0 technology," British Food Journal, vol. 127, no. 5, pp. 1810-1830, 2025. [14] M. A. Aziz, N. H. Ayob, N. A. Ayob, Y. Ahmad, and K. Abdulsomad, "Factors influencing farmer adoption of climate- smart agriculture technologies: Evidence from Malaysia," Human Technology, vol. 20, no. 1, pp. 70-92, 2024. [15] J. Zhao, D. Liu, and R. Huang, "A review of climate-smart agriculture: Recent advancements, challenges, and future directions," Sustainability, vol. 15, no. 4, p. 3404, 2023. https://doi.org/10.3390/su15043404 [16] V. Venkatesh, "Adoption and use of AI tools: A research agenda grounded in UTAUT," Annals of Operations Research, vol. 308, no. 1, pp. 641-652, 2022. [17] V. Venkatesh, R. Raman, and F. Cruz-Jesus, "AI and emerging technology adoption: A research agenda for operations management," International Journal of Production Research, vol. 62, no. 15, pp. 5367-5377, 2024. [18] L. H. L. Nguyen, A. Halibas, and T. Quang Nguyen, "Determinants of precision agriculture technology adoption in developing countries: A review," Journal of Crop Improvement, vol. 37, no. 1, pp. 1-24, 2023. [19] Y. Shi, A. B. Siddik, M. Masukujjaman, G. Zheng, M. Hamayun, and A. M. Ibrahim, "The antecedents of willingness to adopt and pay for the iot in the agricultural industry: An application of the UTAUT 2 theory," Sustainability, vol. 14, no. 11, p. 6640, 2022. https://doi.org/10.3390/su14116640 [20] P. Nkandu and J. Phiri, "Assessing the effect of ICTs on agriculture productivity based on the UTAUT model in developing countries. Case study of southern province in Zambia," Open Journal of Business and Management, vol. 10, no. 6, pp. 3436-3454, 2022. [21] W. Li et al., "A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems," Computers and Electronics in Agriculture, vol. 172, p. 105305, 2020. https://doi.org/10.1016/j.compag.2020.105305 https://creativecommons.org/licenses/by/4.0/ https://doi.org/10.3390/su142012985 https://doi.org/10.3390/su12124981 https://doi.org/10.1016/j.compag.2022.107119 https://doi.org/10.1515/biol-2022-0713 https://doi.org/10.1016/j.suscom.2021.100512 https://doi.org/10.3390/s23073752 https://doi.org/10.1016/j.atech.2024.100431 https://doi.org/10.1016/j.cie.2022.107936 https://doi.org/10.3390/su15043404 https://doi.org/10.3390/su14116640 https://doi.org/10.1016/j.compag.2020.105305 1251 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate [22] R. Sun, S. Zhang, T. Wang, J. Hu, J. Ruan, and J. Ruan, "Willingness andinfluencing factors of pig farmers to adopt internet of things technology in food traceability," Sustainability, vol. 13, no. 16, p. 8861, 2021. https://doi.org/10.3390/su13168861 [23] M. S. Miah, W. Ahmed, and C. C. Seng, "Iot in agrotourism: A sem-neural analysis of smart farming adoption and impacts," Cham, 2024: Springer Nature Switzerland, pp. 501-521. https://doi.org/10.1007/978-3-031-66428-1_32 [24] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, "User acceptance of information technology: Toward a unified view," MIS Quarterly, vol. 27, no. 3, pp. 425-478, 2003. https://doi.org/10.2307/30036540 [25] N. Hayat, A. Al Mamun, N. A. M. Nasir, G. Selvachandran, N. B. C. Nawi, and Q. S. Gai, "Predicting sustainable farm performance—using hybrid structural equation modelling with an artificial neural network approach," Land, vol. 9, no. 9, p. 289, 2020. https://doi.org/10.3390/land9090289 [26] A. Cimino, I. M. Coniglio, V. Corvello, F. Longo, J. K. Sagawa, and V. Solina, "Exploring small farmers behavioral intention to adopt digital platforms for sustainable and successful agricultural ecosystems," Technological Forecasting and Social Change, vol. 204, p. 123436, 2024. https://doi.org/10.1016/j.techfore.2024.123436 [27] V. Venkatesh, F. D. Davis, and Y. Zhu, "Competing roles of intention and habit in predicting behavior: A comprehensive literature review, synthesis, and longitudinal field study," International Journal of Information Management, vol. 71, p. 102644, 2023. https://doi.org/10.1016/j.ijinfomgt.2023.102644 [28] J. Wang, S. Zhang, and L. Zhang, "Intelligent hog farming adoption choices using the unified theory of acceptance and use of technology model: Perspectives from China’s new agricultural managers," Agriculture, vol. 13, no. 11, p. 2067, 2023. https://doi.org/10.3390/agriculture13112067 [29] G. Scur, A. V. D. da Silva, C. A. Mattos, and R. F. Gonçalves, "Analysis of IoT adoption for vegetable crop cultivation: Multiple case studies," Technological Forecasting and Social Change, vol. 191, p. 122452, 2023. https://doi.org/10.1016/j.techfore.2023.122452 [30] A. Al Mamun, F. Naznen, G. Jingzu, and Q. Yang, "Predicting the intention and adoption of hydroponic farming among Chinese urbanites," Heliyon, vol. 9, no. 3, p. e13635, 2023. [31] N. Mishra et al., "Technology in farming: Unleashing farmers’ behavioral intention for the adoption of agriculture 5.0," PLOS ONE, vol. 19, no. 8, p. e0308883, 2024. https://doi.org/10.1371/journal.pone.0308883 [32] K. Anubhav, M. Agarwal, and K. Aashish, "Smart farming for future: A structural relation analysis of attitude, facilitating condition, economic benefit and government support," Technology Analysis & Strategic Management, vol. 37, no. 2, pp. 187-201, 2025. [33] M. H. Ronaghi and A. Forouharfar, "A contextualized study of the usage of the internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of unified theory of acceptance and Use of technology model (UTAUT)," Technology in Society, vol. 63, p. 101415, 2020. https://doi.org/10.1016/j.techsoc.2020.101415 [34] G. W. W. Ena and A. L. S. Siewa, "Factorsinfluencing the behavioural intention for smart farming in sarawak, Malaysia," Journal of Agribusiness, vol. 9, no. 1, pp. 37-56, 2022. [35] S. Schukat and H. Heise, "Towards an understanding of the behavioral intentions and actual use of smart products among german farmers," Sustainability, vol. 13, no. 12, p. 6666, 2021. https://doi.org/10.3390/su13126666 [36] A. A. Alalwan, "Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse," International Journal of Information Management, vol. 50, pp. 28-44, 2020. [37] N. Shaw and K. Sergueeva, "The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value," International Journal of Information Management, vol. 45, pp. 44-55, 2019. [38] M. Bahari, I. Arpaci, O. Der, F. Akkoyun, and A. Ercetin, "Driving agricultural transformation: Unraveling key factors shaping iot adoption in smart farming with empirical insights," Sustainability, vol. 16, no. 5, p. 2129, 2024. https://doi.org/10.3390/su16052129 [39] Q. Yang, A. Al Mamun, M. Masukujjaman, Z. K. M. Makhbul, and X. Zhong, "Adoption of internet of things-enabled agricultural systems among Chinese agro-entreprises," Precision Agriculture, vol. 25, no. 5, pp. 2477-2504, 2024. [40] P. Jayashankar, S. Nilakanta, W. J. Johnston, P. Gill, and R. Burres, "IoT adoption in agriculture: The role of trust, perceived value and risk," Journal of Business & Industrial Marketing, vol. 33, no. 6, pp. 804-821, 2018. https://doi.org/10.1108/JBIM-01-2018-0023 [41] D.-C. Toader, C. M. Rădulescu, and C. Toader, "Investigating the adoption of blockchain technology in agri-food supply chains: Analysis of an extended utaut model," Agriculture, vol. 14, no. 4, p. 614, 2024. https://doi.org/10.3390/agriculture14040614 [42] R. Pillai and B. Sivathanu, "Adoption of internet of things (IoT) in the agriculture industry deploying the BRT framework," Benchmarking: An International Journal, vol. 27, no. 4, pp. 1341-1368, 2020. https://doi.org/10.1108/BIJ- 08-2019-0361 [43] M. Gemtou et al., "Farmers’transition to climate-smart agriculture: A systematic review of the decision-making factors affecting adoption," Sustainability, vol. 16, no. 7, p. 2828, 2024. https://doi.org/10.3390/su16072828 [44] M. Savastano, A. H. Samo, U. Abdullah, and N. Cucari, "Toward sustainable smart agriculture in a developing country: An empirical analysis of green firms determinants," Business Ethics, the Environment & Responsibility, vol. 34, no. 4, pp. 1942-1965, 2025. https://doi.org/10.3390/su13168861 https://doi.org/10.1007/978-3-031-66428-1_32 https://doi.org/10.2307/30036540 https://doi.org/10.3390/land9090289 https://doi.org/10.1016/j.techfore.2024.123436 https://doi.org/10.1016/j.ijinfomgt.2023.102644 https://doi.org/10.3390/agriculture13112067 https://doi.org/10.1016/j.techfore.2023.122452 https://doi.org/10.1371/journal.pone.0308883 https://doi.org/10.1016/j.techsoc.2020.101415 https://doi.org/10.3390/su13126666 https://doi.org/10.3390/su16052129 https://doi.org/10.1108/JBIM-01-2018-0023 https://doi.org/10.3390/agriculture14040614 https://doi.org/10.1108/BIJ-08-2019-0361 https://doi.org/10.1108/BIJ-08-2019-0361 https://doi.org/10.3390/su16072828 1252 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate [45] A. M. Zamil, H. M. U. Javed, and S. Ali, "Internet of things platforms adoption in agriculture: Comparative theoretical models," International Journal of Retail & Distribution Management, vol. 52, no. 9, pp. 965-981, 2024. [46] L. Li, X. Min, J. Guo, and F. Wu, "The influence mechanism analysis on the farmers’ intention to adopt Internet of things based on UTAUT-TOE model," Scientific Reports, vol. 14, no. 1, p. 15016, 2024. [47] V. Erokhin, K. Mouloudj, A. C. Bouarar, S. Mouloudj, and T. Gao, "Investigatingfarmers’ intentions to reduce water waste through water-smart farming technologies," Sustainability, vol. 16, no. 11, p. 4638, 2024. https://doi.org/10.3390/su16114638 [48] M. H. Islam, M. Z. Anam, M. R. Hoque, M. Nishat, and A. B. M. M. Bari, "Agriculture 4.0 adoption challenges in the emerging economies: Implications for smart farming and sustainability," Journal of Economy and Technology, vol. 2, pp. 278-295, 2024. https://doi.org/10.1016/j.ject.2024.09.002 [49] T. Saranya, C. Deisy, S. Sridevi, and K. S. M. Anbananthen, "A comparative study of deep learning and Internet of things for precision agriculture," Engineering Applications of Artificial Intelligence, vol. 122, p. 106034, 2023. https://doi.org/10.1016/j.engappai.2023.106034 [50] F. Fuentes-Peñailillo, K. Gutter, R. Vega, and G. C. Silva, "Transformative technologies in digital agriculture: Leveraging internet of things, remote sensing, and artificial intelligence for smart crop management," Journal of Sensor and Actuator Networks, vol. 13, no. 4, p. 39, 2024. https://doi.org/10.3390/jsan13040039 [51] A. Subeesh and C. Mehta, "Automation and digitization of agriculture using artificial intelligence and internet of things," Artificial Intelligence in Agriculture, vol. 5, pp. 278-291, 2021. [52] V. Patel, S. Gautam, V. Chaurasia, S. Kureel, A. Kumar, and R. K. Gupta, "IoT-enabled machine learning-based smart and sustainable agriculture," in Reshaping Environmental Science Through Machine Learning and IoT: IGI Global Scientific Publishing, 2024, pp. 176-200. [53] C. De Abreu and J. P. van Deventer, "The application of artificial intelligence (AI) and internet of things (IoT) in agriculture: A systematic literature review," in Southern African Conference for Artificial Intelligence Research, 2022: Springer. [54] S. G. Eladl, A. Y. Haikal, M. M. Saafan, and H. Y. ZainEldin, "A proposed plant classification framework for smart agricultural applications using UAV images and artificial intelligence techniques," Alexandria Engineering Journal, vol. 109, pp. 466-481, 2024. [55] J. Rane, Ö. Kaya, S. K. Mallick, and N. L. Rane, "Smart farming using artificial intelligence, machine learning, deep learning, and ChatGPT: Applications, opportunities, challenges, and future directions," in Generative Artificial Intelligence in Agriculture, Education, and Business, 2024, pp. 218-272. https://doi.org/10.70593/978-81-981271-7- 4_6 [56] M. Dhanaraju, P. Chenniappan, K. Ramalingam, S. Pazhanivelan, and R. Kaliaperumal, "Smartfarming: Internet of things (IoT)-based sustainable agriculture," Agriculture, vol. 12, no. 10, p. 1745, 2022. https://doi.org/10.3390/agriculture12101745 [57] E. S. Mohamed, A. Belal, S. K. Abd-Elmabod, M. A. El-Shirbeny, A. Gad, and M. B. Zahran, "Smart farming for improving agricultural management," The Egyptian Journal of Remote Sensing and Space Science, vol. 24, no. 3, pp. 971- 981, 2021. [58] S. Kadam, A. S. Shinde, A. M. Bari, and J. Gujar, "The role of artificial intelligence and the internet of things in smart agriculture towards green engineering," in Applied Computer Vision and Soft Computing with Interpretable AI: Chapman and Hall/CRC, 2023, pp. 135-152. [59] F. A. Alaba, A. Jegede, U. Sani, and E. G. Dada, "Artificial intelligence of things (AIoT) solutions for sustainable agriculture and food security," 2024, pp. 123-142. https://doi.org/10.1007/978-3-031-53433-1_7 [60] S. Tiwari, B. Bhardwaj, D. Arora, and S. Khatri, "Challenges and barriers to smart farming adaptation: A technical, economic, and social perspective," Smart Agritech: Robotics, AI, and Internet of Things (IoT) in Agriculture, pp. 75-111, 2024. https://doi.org/10.1002/9781394302994.ch4 [61] A. Kumar et al., "Role of artificial intelligence in vegetable production: A review," Journal of Scientific Research and Reports, vol. 30, no. 9, pp. 950–963, 2024. https://doi.org/10.9734/jsrr/2024/v30i92423. [62] H. El Bilali, C. Strassner, and T. Ben Hassen, "Sustainable agri-food systems: Environment, economy, society, and policy," Sustainability, vol. 13, no. 11, p. 6260, 2021. https://doi.org/10.3390/su13116260 [63] M. Aldossary, H. A. Alharbi, and C. Anwar Ul Hassan, "Internet of Things (IoT)-Enabled Machine Learning Models for Efficient Monitoring of Smart Agriculture," IEEE Access, vol. 12, pp. 75718-75734, 2024. https://doi.org/10.1109/ACCESS.2024.3404651 [64] K. Aggarwal, G. Sreenivasula Reddy, R. Makala, T. Srihari, N. Sharma, and C. Singh, "Studies on energy efficient techniques for agricultural monitoring by wireless sensor networks," Computers and Electrical Engineering, vol. 113, p. 109052, 2024. https://doi.org/10.1016/j.compeleceng.2023.109052 [65] G. Fox, J. Mooney, P. Rosati, and T. Lynn, "AgriTechinnovators: A study of initial adoption and continued use of a mobile digital platform by family-operated farming enterprises," Agriculture, vol. 11, no. 12, p. 1283, 2021. https://doi.org/10.3390/agriculture11121283 [66] K. Xie, Y. Zhu, Y. Ma, Y. Chen, S. Chen, and Z. Chen, "Willingness oftea farmers to adopt ecological agriculture techniques based on the UTAUT extended model," International Journal of Environmental Research and Public Health, vol. 19, no. 22, p. 15351, 2022. https://doi.org/10.3390/ijerph192215351 https://doi.org/10.3390/su16114638 https://doi.org/10.1016/j.ject.2024.09.002 https://doi.org/10.1016/j.engappai.2023.106034 https://doi.org/10.3390/jsan13040039 https://doi.org/10.70593/978-81-981271-7-4_6 https://doi.org/10.70593/978-81-981271-7-4_6 https://doi.org/10.3390/agriculture12101745 https://doi.org/10.1007/978-3-031-53433-1_7 https://doi.org/10.1002/9781394302994.ch4 https://doi.org/10.9734/jsrr/2024/v30i92423 https://doi.org/10.3390/su13116260 https://doi.org/10.1109/ACCESS.2024.3404651 https://doi.org/10.1016/j.compeleceng.2023.109052 https://doi.org/10.3390/agriculture11121283 https://doi.org/10.3390/ijerph192215351 1253 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 9: 1239-1253, 2025 DOI: 10.55214/2576-8484.v9i9.10125 © 2025 by the authors; licensee Learning Gate [67] S. Singha and R. Singha, "Theories andmodels in AIoT: Exploring economic, behavioral, technological, psychological, and organizational perspectives," in Artificial Intelligence of Things (AIoT) for Productivity and Organizational Transition, 2024, pp. 214-239. [68] A. Sood, A. K. Bhardwaj, and R. K. Sharma, "Towards sustainable agriculture: Key determinants of adopting artificial intelligence in agriculture," Journal of Decision Systems, vol. 33, no. 4, pp. 833-877, 2024. [69] A. Piancharoenwong and Y. F. Badir, "IoT smart farming adoption intention under climate change: The gain and loss perspective," Technological Forecasting and Social Change, vol. 200, p. 123192, 2024. https://doi.org/10.1016/j.techfore.2023.123192 https://doi.org/10.1016/j.techfore.2023.123192