Farmers’ perception of artificial intelligence and drone technologies for resource-efficient and sustainable agriculture: An empirical study

Introduction

Agriculture is undergoing a rapid technological transformation in response to increasing pressure to produce more food with fewer natural resources while maintaining environmental quality and farm profitability. Population growth, climate variability, water scarcity, soil degradation, increasing input costs and labour shortages have created an urgent need for agricultural production systems that are more precise, adaptive, climate-resilient and resource efficient. Conventional agricultural systems frequently involve uniform application of water, agriculture requires substantial quantities of water, nutrients, energy and plant-protection inputs, and inefficient application can increase production costs and environmental risks. AIenabled systems can estimate crop water requirements, predict nutrient deficiencies and identify pest and disease risks, while UAVs provide the spatial information required for targeted interventions. This supports the precision-agriculture principle of applying the right input, in the right quantity, at the right place and at the right time (Bongiovanni & Lowenberg-Deboer, 2004; Gebbers & Adamchuk, 2010; Balafoutis et al., 2017) [1,2,7] . AI-supported irrigation systems can combine sensor information, weather data and remotely sensed observations to improve irrigation scheduling, while UAV-based thermal and multispectral observations can identify spatial differences in crop water status (Shamshiri et al., 2018; Wolfert et al., 2017) [18, 21] . Similarly, AI-based interpretation of crop and soil information and UAV imagery can support site-specific nutrient management, potentially reducing fertilizer wastage and nutrient losses. In plant protection, UAV-based monitoring can identify areas of crop stress, weed infestation, pests and diseases, while AI algorithms can classify affected areas and support targeted interventions. Recent research increasingly focuses on integrating remote sensing, AI and precision spraying to improve the accuracy and efficiency of agricultural input application (Mogili & Deepak, 2018; Hafeez et al., 2023; Wang et al., 2025) [8,15,20] . Consequently, AI and drone technologies have potential to improve water, nutrient, pesticide, labour and energy-use efficiency while contributing to environmental sustainability and climate-resilient agricultural production. However, technological performance alone does not determine whether an agricultural technology will be accepted and adopted by farmers. Farmers are the ultimate decision-makers at farm level, and their perception of a technology can influence their attitude, intention and subsequent adoption behaviour. Rogers' (2003) [17] Diffusion of Innovations Theory identifies relative advantage, compatibility, complexity, trialability and observability as important characteristics influencing technology diffusion, while the Technology Acceptance Model emphasizes perceived usefulness and perceived ease of use as major determinants of technology acceptance (Davis, 1989) [4] . The Unified Theory of Acceptance and Use of Technology further incorporates performance expectancy, effort expectancy, social influence and facilitating conditions (Venkatesh et al., 2003) [19] . In agriculture, however, farmers evaluate innovations within a complex socio-economic, environmental and institutional context. Their perceptions are influenced not only by usefulness and ease of use but also by expected economic returns, risk, environmental consequences, labour requirements, compatibility with existing practices, access to information, extension support, training and availability of services. Studies of precisionagriculture adoption have demonstrated the influence of education, age, farm size, technical literacy, information availability and perceived profitability on farmers' technology decisions (Finger et al., 2019; Michels et al., 2020; Paustian & Theuvsen, 2017) [6,14,16] . Recent empirical evidence from India provides further support for a multidimensional approach. Barman et al. (2025) [23] , in a study of 320 farmers from Haryana and Uttar Pradesh, reported that promotional efforts, perceived usefulness, peer influence, perceived economic viability, perceived environmental impact and perceived social impact significantly influenced attitudes towards agricultural drones and willingness to adopt them. Similarly, Murugan et al. (2024) [22] examined farmers' awareness and perception of drone technology in Coimbatore district, highlighting the increasing relevance of farmer-level perception research in the Indian context. These findings indicated that farmers' assessment of AI and drone technologies cannot be adequately represented by a single measure of technology acceptance. For small and marginal farmers, the issue is also not necessarily individual ownership of sophisticated equipment but access to technologyenabled services through Custom Hiring Centres, Farmer Producer Organizations, cooperatives, agricultural extension institutions and private drone service providers. Agricultural extension therefore has an important role in awareness creation, technology demonstration, training, interpretation of digital advisories, linkage with service providers and continued technical support. The uploaded manuscript similarly emphasizes that cloud-based platforms and mobile applications can provide timely, location-specific agricultural advisories and strengthen farm-level decision-making and extension services. Thus, social and institutional support should be considered alongside technological, economic and environmental dimensions when measuring farmers' perception. Despite the increasing literature on the technical applications and adoption of AI and drone technologies, there remains a need for a context-specific, multidimensional and psychometrically validated perception scale that simultaneously captures farmers' views regarding resource efficiency, environmental sustainability, economic benefits, technological suitability and institutional support. Existing studies have examined individual constructs such as perceived usefulness, perceived ease of use, economic viability, environmental impact, social influence and adoption intention (Davis, 1989; Venkatesh et al., 2003; Caffaro et al., 2020; Barman et al., 2025) [3,4,19,23] , but the rapidly evolving nature of integrated AI-drone systems require a broader farmer-oriented measurement framework. A scientifically developed perception scale can provide a systematic method of measuring farmers' evaluations rather than relying on isolated questions or general opinions. Scale development requires conceptualization of the construct, systematic item generation, expert assessment, pre-testing, item analysis, reliability assessment and construct validation (Lawshe, 1975; DeVellis & Thorpe, 2021) [5,11] . Exploratory Factor Analysis (EFA) can identify the underlying factor structure, while Confirmatory Factor Analysis (CFA) can subsequently establish the adequacy of the proposed structure. Reliability could be assessed through Cronbach's alpha and composite reliability, while convergent and discriminant validity can establish the psychometric soundness of the final instrument. Accordingly, the present study conceptualizes farmers' perception as a multidimensional construct comprising perceived usefulness, resource-use efficiency, environmental sustainability, economic benefits, technological suitability and social/institutional support. Perceived usefulness represents farmers' beliefs regarding the ability of AI and drones to improve agricultural decision-making and farm performance; resource-use efficiency reflects perceived conservation and efficient utilization of water, fertilizers, pesticides, labour and energy; environmental sustainability represents perceived contributions to resource conservation, pollution reduction and climate-resilient farming; economic benefits encompass cost reduction, profitability, labour savings and economic viability; technological suitability includes ease of use, compatibility, accessibility and practical applicability; and social/institutional support encompasses extension services, training, demonstrations, government support, FPOs, Custom Hiring Centres and availability of service providers. This conceptualization is supported by recent evidence demonstrating that farmers' attitudes towards drones are shaped by technological, economic, environmental and social considerations. Therefore, the present study aims to develop and standardize a multidimensional perception scale for measuring farmers' perception towards Artificial Intelligence and drone technologies for resource-efficient and sustainable agriculture. The study will systematically generate and validate perception items, identify the underlying dimensions through factor analysis, establish the reliability and validity of the scale, and provide a standardized instrument for future research on AI-and drone-enabled agricultural transformation. Such a scale can subsequently be used to compare farmers' perceptions across crops, regions and socio-economic groups, identify determinants of perception and adoption intention, and assist agricultural extension organizations in designing targeted awareness programmes, demonstrations, training interventions and institutional support mechanisms. Ultimately, the development of a validated farmer-centred perception scale can contribute to ensuring that AI and drone technologies are not only technologically advanced but also economically viable, socially acceptable, environmentally sustainable and practically relevant to farming communities.

Materials and Methods

Research design

The study would adopt an ex-post facto, descriptive and exploratory research design to assess farmers' perceptions regarding the role of Artificial Intelligence (AI) and drone technologies in promoting resource-efficient and sustainable agricultural systems. The study would focus on farmers' perceptions of the usefulness, resource-use efficiency, environmental sustainability, technological applicability, economic feasibility and institutional dimensions of AI-and drone-based agricultural technologies. The conceptual basis of the study was supported by the role of AI, machine learning, remote sensing, IoT and UAV technologies in crop monitoring, precision input application, predictive analytics and farm-level decision-making of farmers.

Study area

The study would be conducted in Telangana, India, considering the increasing importance of digital agriculture, precision farming and technology-enabled agricultural extension. Telangana provides an appropriate for assessing farmers' perceptions because agriculture was increasingly supported by mechanization, digital advisory services, remote sensing and emerging drone applications. A multistage sampling procedure would be adopted for selecting the study area. Districts would be selected purposively based on the prevalence of major agricultural crops and the availability or exposure of farmers to digital agricultural technologies. Subsequently, mandals, villages and farmers would be selected using appropriate sampling procedures. The target population would comprise farmers engaged in crop production in the selected study area. Farmers having direct or indirect exposure to AI-based agricultural advisory services, digital applications, precision agriculture, remote sensing or drone technologies would be considered for the study.

Scale development of the perception on Artificial Intelligence and drone technologies for resource-efficient and sustainable agriculture

A systematic scale-development procedure was followed to develop a standardized scale for measuring farmers' perception of Artificial Intelligence and drone technologies for resourceefficient and sustainable agriculture. The construct was operationalized into six dimensions, namely perceived usefulness and productivity, resource-use efficiency, environmental sustainability, technological usability and compatibility, economic feasibility, and institutional support, trust and perceived risks. An initial pool of statements was generated through an extensive review of literature and expert consultations. The statements were subjected to expert scrutiny for relevance, clarity and representativeness, followed by assessment of content validity using Content Validity Ratio and Content Validity Index. The refined scale was pre-tested and pilot tested among farmers. Item analysis, item-total correlation and Cronbach's alpha were used to assess item quality and internal consistency. Exploratory factor analysis/Principal Component Analysis was subsequently employed to establish the underlying factor structure and construct validity. Based on the psychometric properties, the final scale comprising 60 statements, with 10 statements under each of the six dimensions, was retained. The final scale was administered using a five-point Likert continuum ranging from strongly disagree (1) to strongly agree (5), with higher scores indicating a more favourable perception towards AI and drone technologies for resourceefficient and sustainable agriculture. Since each item has a score ranging from 1 to 5 with maximum score is 300 and minimum score is 60. A higher score will indicate a more favourable perception towards AI and drone technologies for resourceefficient and sustainable agriculture.

Content Validity Ratio (CVR)

The Content Validity Ratio (CVR) could be calculated using Lawshe's method to determine whether each statement was considered essential by the experts.

Where: • = number of experts indicating that the item is essential • = total number of experts Items with inadequate CVR values should be deleted, modified or reconsidered. Items with satisfactory CVR values will be retained for further testing.

Calculation of perception scores

Dimension-wise scores will also be calculated: The results presented in Table 1 revealed that an overview of the farmers' socio-economic status, resource availability, digital preparedness and institutional exposure. These characteristics are particularly relevant to AI-and drone-based agriculture because the effective use of such technologies requires not only awareness but also adequate digital skills, connectivity, financial capacity, technical training, extension support and access to service providers. The profile of the respondents therefore provides the contextual basis for examining the multidimensional perception of AI and drone technologies and their subsequent influence on farmers' adoption intention and willingness to use these technologies.

Age

The findings revealed that majority (48.33%) of the respondents belonged to the young age group (≤35 years), followed by (26.25%) who belonged to the old age group (>50 years), while (25.42%) were in the middle-age group (36-50 years). The predominance of young farmers about AI and drone technologies. Younger farmers were generally expected to have greater exposure to smartphones, digital applications and online agricultural information. Their relatively greater familiarity with digital technologies may facilitate the understanding and acceptance of AI-enabled advisory services and drone-based agricultural applications.

Education

The results showed that (31.67%) of respondents had completed higher secondary education, followed by (30.00%) with secondary education. About (23.75%) were graduates and above, while (14.58%) had education up to illiterate/primary level.

The relatively higher educational attainment of the respondents indicates that a considerable proportion of farmers may have the basic capacity to understand digital agricultural information. Education can facilitate comprehension of AI-generated recommendations, drone imagery, mobile applications and technology-based farm advisories.

Farm size

The results revealed that majority (42.50%) of the respondents belonged to the small farm-size category, followed by (37.92%) in the medium and (19.58%) in the large farm category. The predominance of small and medium farmers has important implications for technology adoption. Individual ownership of drones and sophisticated AI-based equipment might not be economically viable for smallholders. Consequently, custom hiring, FPO-based services and drone-as-a-service models may be more appropriate for improving access.

Annual income

More than half of the respondents (51.67%) were categorized under medium annual income, while (25.00%) belonged to the high-income group and (23.33%) to the low-income group. The predominance of medium-income farmers suggested moderate economic capacity for investing in or accessing emerging agricultural technologies. However, the cost of drone services and AI-enabled equipment may remain an important consideration, particularly among low-income farmers.

Farming experience

Nearly half of the respondents (49.58%) had medium farming experience, followed by (27.92%) with high experience and (22.50%) with low experience. Farmers with longer farming experience possess substantial indigenous and experiential knowledge of crop production. However, experienced farmers may differ in their willingness to replace or supplement traditional decision-making with technology-based recommendations.

Operational landholding

The findings showed that less than half (47.08%) of farmers had medium operational landholdings, followed by (37.92%) with small operational landholdings and (15.00%) with large holdings.

The predominance of small and medium holdings highlights the importance of collective technology access. Shared drone services can make advanced technologies economically feasible even when individual farmers cannot afford to purchase the equipment.

Access to irrigation

A substantial majority of respondents (85.42%) had medium access to irrigation, while (9.17%) had low access and (5.42%) had high access.

The predominance of medium irrigation access indicates that most farmers have some degree of irrigation availability but may still face resource constraints. AI-based irrigation scheduling and drone-based crop stress detection could potentially improve water-use efficiency.

Access to credit

More than half (57.08%) of the respondents had medium access to credit, followed by (22.92%) with high access and (20.00%) with low access. Credit availability could influence farmers' ability to invest in digital technologies and pay for technology-based services. Hence, financial institutions and agricultural development programmes can play an important role in supporting technology adoption.

Ownership of farm literacy

The results indicated that (49.17%) of respondents had medium machinery ownership, while (35.00%) had low ownership and (15.83%) had high ownership.

The predominance of medium and low machinery ownership suggested that direct ownership of advanced technologies might be difficult for many farmers. This reinforces the relevance of custom hiring centres and FPO-managed machinery and drone services.

Digital literacy

More than half of the farmers (53.33%) had medium digital literacy, followed by (25.42%) with high digital literacy and (21.25%) with low digital literacy.

The finding indicated that a substantial proportion of farmers possess at least moderate digital skills. This creates a favourable foundation for introducing smartphone-based AI advisory systems and digital farm-management platforms.

3.1.11Smart phone and internet access

The results showed that (52.92%) of respondents had medium access to smartphones/internet, while (30.83%) had high access and (16.25%) had low access. The relatively widespread access to smartphones and internet connectivity provides an important opportunity for delivering AI-enabled agricultural advisory services. Smartphones can serve as the interface through which farmers receive crop recommendations, weather information, pest alerts and dronegenerated information.

Extension contact

The majority of respondents (53.75%) had medium extension contact, followed by (25.83%) with high extension contact and (20.42%) with low extension contact.

The findings indicate that extension agencies remain an important channel for communicating information about emerging agricultural technologies. Demonstrations organized through extension systems can reduce uncertainty and improve farmers' confidence in AI and drone technologies.

Training exposure

A substantial majority of farmers (82.50%) had received training exposure, whereas (17.50%) had not received training. This was an important finding because training can improve farmers' knowledge, perceived ease of use and confidence in using emerging technologies. The high level of training exposure creates a favourable environment for technology promotion. However, training should increasingly focus on hands-on demonstrations, interpretation of drone imagery, AI-based decision support and practical application, rather than merely providing theoretical information.

Awareness of AI and drone technologies

The results showed that (50.42%) of respondents had medium awareness of AI and drone technologies, while (27.92%) had high awareness and (21.67%) had low awareness. This finding supports the need for awareness campaigns, result demonstrations, farmer field schools and exposure visits.

FPO/SHG membership

The findings indicated that (39.17%) of farmers were members of an FPO/SHG, while (60.83%) were not members.

Although membership was lower than non-membership, FPOs and SHGs can potentially serve as important institutional mechanisms for collective access to expensive technologies. Drone services can be organized through such groups, thereby reducing individual costs.

Market access

A substantial majority of respondents (84.17%) had medium market access, while (10.42%) had low access and (5.42%) had high market access. Medium market access among most farmers suggests moderate integration with agricultural markets. Better market access may increase farmers' willingness to invest in technologies when they can realize improved productivity and profitability through better market participation. The socio-economic and technological profile of the respondents revealed a predominantly young and moderately educated farming population with medium levels of income, farming experience, operational landholding, digital literacy, smartphone/internet access and extension contact. A substantial majority (82.50%) had exposure to training, while half of the respondents (50.42%) possessed medium awareness of AI and drone technologies. However, the predominance of small and medium farmers and medium levels of machinery ownership indicates the importance of collective and service-based access models. The profile therefore provides a suitable basis for examining the multidimensional perception of AI and drone technologies and their subsequent influence on farmers' adoption intention and willingness to use these technologies.

Perceived Usefulness and Agricultural Productivity

The first perception dimension consisted of 10 statements designed to measure farmers' perceptions regarding the usefulness of AI and drones for improving agricultural productivity.

Resource-Use Efficiency

The second dimension assessed farmers' perceptions of the ability of AI and drones to improve the efficiency of agricultural inputs.

Environment sustainability

The third dimension measured farmers' perceptions concerning the environmental contribution of AI and drone technologies. and "Technology reduces chemical wastage" (Mean = 4.18). This indicated that farmers particularly recognized the potential of precision technologies to target farm inputs more efficiently and minimize unnecessary chemical use. The lowest mean was recorded for "AI and drones contribute to long-term sustainability" (Mean = 3.94), followed by "Technology promotes sustainable production" (Mean = 3.98). Although these indicators ranked lower, their scores remained close to 4.00, suggesting a generally favourable perception of the long-term environmental benefits of AI and drone technologies.

Technological Usability and Compatibility

This dimension examined whether farmers perceived AI and drone technologies as easy to understand, operate and integrate into existing farming systems. The results in the table 5 indicated that technological usability and compatibility dimension recorded an overall mean score of 3.91, indicating a highly favourable perception among the 240 farmers regarding the usability and compatibility of AI and drone technologies. The highest-ranked indicator was "Drone operations can be managed by trained personnel" (Mean = 4.08), followed by "Farmers can learn to use AI applications" (Mean = 4.02) and "Technology is convenient to use" (Mean = 3.97). The lowest mean was observed for "Technical assistance is available when required" (Mean = 3.72), followed by "Drone services are easy to access" (Mean = 3.78). This suggests that although farmers generally perceive the technologies as understandable and usable, access to technical support and drone services may remain important areas for improvement.

Economic Feasibility

Economic feasibility represents farmers' perceptions regarding the affordability, profitability and economic benefits of AI and drone technologies. that farmers particularly perceive custom hiring and labour-cost savings as important mechanisms for improving the economic viability of these technologies. The lowest mean score was recorded for "AI and drones are economically feasible for farmers" (Mean = 3.82), followed by "Technology provides benefits greater than its cost" (Mean = 3.88). This indicates that, while farmers recognize several economic benefits, overall affordability and the cost-benefit relationship may remain concerns.

Institutional Support, Trust and Perceived Risk

The sixth dimension assessed institutional confidence and concerns related to the adoption of AI and drone technologies. The highest mean score was observed for "Farmers are concerned about high service costs" (Mean = 4.15; Rank I), indicating that cost-related risk is a major concern associated with AI and drone technologies. This was followed by "Institutional support can reduce technology-related risk" (Mean = 4.12; Rank II) and "Government subsidies/incentives encourage adoption" (Mean = 4.08; Rank III). These findings highlighted the importance of institutional mechanisms, subsidies and support services in reducing farmers' perceived risks.

Overall Perception of AI and Drone Technologies

The overall perception index was developed by combining the scores of all six dimensions.

Psychological and Cognitive Mediating Variables

The conceptual framework proposes five mediating variables about the farmer perception of AI and drone technologies in sustainable agriculture. The results indicated in the table 9 revealed that the farmers had an overall favourable psychological and cognitive orientation towards AI and drone technologies. Among the five mediating variables, perceived benefits recorded the highest mean score (4.18; SD = 0.68) and ranked first, indicating that farmers strongly recognized the potential advantages of AI and drones in agricultural production. Attitude towards technology ranked second with a mean of 4.05, followed by Technology awareness (3.86). Perceived ease of use recorded a mean of 3.81, while Knowledge about AI and drones had the lowest mean (3.72). Although knowledge ranked lowest, its score remained within the high category, suggesting that farmers generally possessed a favourable understanding of these technologies but may require additional training and practical exposure.

Overall mean across the five variables = 3.92, indicating a high level of psychological and cognitive readiness towards AI and drone technologies. The results indicated in table 10 revealed that AI/drone awareness (r = 0.681) had the strongest positive and significant association with farmers' overall perception of AI and drone technologies, followed by digital literacy (r = 0.617) and smartphone/internet access (r = 0.583). This suggested that farmers with greater awareness and digital capability were more likely to perceive AI and drone technologies favourably. Annual income (r = 0.521), training exposure (r = 0.492), education (r = 0.462), extension contact (r = 0.451) and access to credit (r = 0.438) also showed moderate positive and significant relationships. These findings suggest that better economic capacity, educational exposure, institutional interaction and access to financial resources may facilitate favourable perceptions. The relatively weaker but significant relationships were observed for access to irrigation (r = 0.214), farming experience (r = 0.246) and FPO/SHG membership (r = 0.276). Overall, the correlation pattern suggests that knowledge, digital readiness, awareness, economic capacity and institutional exposure are important factors associated with farmers' perceptions of AI and drone technologies.

Correlation

Adoption Intention/Willingness to Use AI and Drone Technologies

Adoption intention was considered the principal outcome variable of the study. The results in table 11 revealed that overall adoption intention recorded a mean score of 3.99 (SD = 0.80), indicating a high level of willingness among farmers to adopt AI and drone technologies.

The highest-ranked statement was "I would use these technologies if services were locally available" (Mean = 4.18), followed by "I am willing to use drone-based services" (Mean = 4.12) and "I intend to use AI-based agricultural services" (Mean = 4.08). This indicates that local availability of services and access to drone-based applications could substantially encourage farmers' adoption intentions. The lowest mean was observed for "I am willing to pay reasonable charges for useful services" (Mean = 3.82), suggesting that although farmers have a favourable intention to use these technologies, cost and affordability may influence their willingness to pay. Participation in demonstrations and access to training also received favourable scores, indicating the potential importance of demonstrations, capacity building and customhiring services in promoting adoption.

Effect of Overall Perception on Adoption Intention

The influence of farmers' overall perception on their intention to adopt Artificial Intelligence (AI) and drone technologies was examined to determine whether favourable perceptions translate into a greater willingness to use these technologies in agricultural activities. Overall perception represents farmers' collective assessment of the usefulness, resource-use efficiency, environmental sustainability, technological usability, economic feasibility, and institutional support associated with AI and drone technologies. Adoption intention reflects the farmers' willingness to use, recommend, and continue using these technologies when appropriate services, training, and support are available. Regression/Structural Equation Modelling (SEM) was employed to estimate the strength and significance of the relationship between overall perception and adoption intention.

The results are presented in Table 12.

Perceived Sustainable Agricultural Outcomes

The adoption of Artificial Intelligence (AI) and drone technologies was expected to generate a range of agricultural, economic and environmental benefits. In the present study, farmers' perceptions regarding the sustainable outcomes of these technologies were assessed across five major dimensions: improved productivity, efficient use of resources, reduced production costs and higher profitability, environmental protection and climate resilience and long-term sustainability. The items were measured using a five-point Likert scale, where higher scores represented stronger agreement regarding the potential contribution of AI and drone technologies to sustainable agriculture. The strongest effect was observed for improved productivity (β = 0.681), followed by resource-use efficiency (β = 0.642) and reduced cost and profitability (β = 0.617). The comparatively lower effect on climate resilience (β = 0.529) suggested that farmers' adoption intentions may be more strongly driven by immediate and observable farm benefits than by long-term climate adaptation outcomes. Overall, the findings indicated in table 18 revealed that farmers perceive AI and drone technologies as promising instruments for achieving sustainable agricultural development. The strongest perceived benefits were related to productivity enhancement and resource-use efficiency, while climate resilience and long-term sustainability received comparatively lower scores. The positive and significant relationship between adoption intention and perceived sustainable agricultural outcomes indicated that farmers' expectations of tangible benefits were an important factor underlying their willingness to use these technologies. Hence, technology promotion programmes should emphasize evidence-based demonstrations of productivity gains, input savings, profitability and environmental benefits, while simultaneously creating greater awareness about the long-term contribution of AI and drones to climate resilience and sustainable agricultural development.

Conclusion

The present study was undertaken to assess farmers' perception of Artificial Intelligence (AI) and drone technologies for resource-efficient and sustainable agriculture, with particular emphasis on their perceived usefulness, resource-use efficiency, environmental sustainability, technological usability, economic feasibility, institutional support and their influence on adoption intention. The study provided an integrated understanding of how farmers perceive emerging digital technologies and the extent to which such perceptions may contribute to their willingness to use them in agricultural production. The findings indicated that farmers generally perceive AI and drone technologies as promising tools for improving agricultural productivity and promoting sustainable resource management. Among the perceived sustainable agricultural outcomes, improved agricultural productivity emerged as the most prominent benefit, followed by efficient use of agricultural resources and environmental protection. Farmers particularly recognized the potential of drone-based crop monitoring, precision application of agricultural inputs, early identification of pests and diseases, and AI-supported farm decision-making. These findings indicate that farmers are able to recognize the practical benefits of digital technologies when their applications are directly linked to day-to-day farm management.

The study further demonstrates that AI and drone technologies have the potential to contribute to efficient utilization of water, fertilizers, pesticides, labour and other farm resources. Precision-based interventions can help minimize input wastage while improving the timeliness and accuracy of farm operations. At the same time, farmers perceived environmental benefits through reduced excessive use of agrochemicals, improved resource conservation and promotion of sustainable production practices. However, perceptions regarding climate resilience and long-term sustainability were comparatively less pronounced, suggesting that farmers may have greater awareness of immediate productivity and economic benefits than the longerterm climate adaptation potential of these technologies.

The economic dimension also emerged as an important consideration. Although farmers recognized the possibility of reduced input expenditure, labour savings and increased profitability, the affordability of AI and drone technologies remains an important issue, particularly for small and medium farmers. Therefore, custom hiring services, FPO-based technology services, cooperative approaches and Drone-as-a-Service models could provide more equitable access than individual ownership. Similarly, strengthening government incentives and institutional support can reduce the financial and technological barriers associated with adoption.

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