Farmers' intention to adopt drone technology: A structural equation modelling approach

Drones have recently become a part of the precision agriculture (PA) technology toolkit. Drones are transforming agricultural methods by improving crop management's accuracy and efficiency. However, despite their potential benefits, adoption rates remain low, and research on drone adoption in agriculture is limited. Thus, this paper aims to assess factors influencing farmers' perceptions of adopting drone technology in agriculture and identify the challenges farmers face when incorporating drone technology into farming practices in the Western Zone of Tamil Nadu. A total of 228 farmers were personally interviewed in 2024 as part of the survey. The Structural Equation Modelling indicated that perceived importance of drones (β = 0.908, p < 0.01), job relevance (β = 0.603, p < 0.01), and farmers' attitude (β = 0.187, p < 0.01) were significant predictors of adoption intention. The model showed strong internal consistency and validity, with Cronbach's Alpha (CA) values between 0.79 and 0.92 and AVE values above 0.70. The findings suggest that increasing farmers' awareness of drone applications specific to their farms and boosting their confidence in using drones can enhance adoption rates. High cost, poor connectivity, limited awareness about the benefits, lack of timely availability of drones during peak seasons, fear of unemployment, and limited availability of training programs emerged as key barriers to drone adoption among farmers. Addressing these challenges is crucial for promoting effective and widespread use of drone technology in agriculture. These insights are valuable for agribusinesses involved in drone development, sales, and services and for research on PA technologies.