Modeling determinants of farmers’ attitude and adoption willingness toward agricultural drones: a PLS-SEM study in India

Agricultural drones represent a rapidly advancing innovation in modern farming, offering significant potential to enhance productivity, optimize input use, reduce labor dependency, and support real-time data-based decision-making. However, despite their proven advantages and increasing market availability, adoption among Indian farmers remains limited. Understanding the factors that shape farmers’ attitudes and willingness to adopt drone technology is essential for overcoming barriers and promoting large-scale adoption. This study examines the determinants influencing farmers’ attitudes toward agricultural drone technology and their willingness to adopt. A quantitative research approach was adopted, using a structured questionnaire administered to 320 farmers from selected districts in Haryana and Uttar Pradesh, India. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the influence of key predictors on Attitude Toward Technology (ATT). The structural model results indicated that six of the eight hypothesized predictors, Promotional Efforts, Perceived Usefulness, Peer Pressure, Perceived Economic Viability, Perceived Environmental Impact, and Perceived Social Impact, had a statistically significant positive effect on ATT and contributed substantially to explaining variance in adoption willingness. These results reveal that farmer decisions are shaped by psychological, economic, environmental, and social dimensions, rather than purely technical considerations. The study emphasizes that effective adoption strategies must prioritize awareness enhancement, hands-on training, field demonstrations, and financial support mechanisms to improve accessibility and trust. Strengthening institutional support and fostering positive peer experiences could further accelerate technology acceptance among less innovative farmers and later adopters. The findings provide valuable insights for policymakers, extension agencies, and agri-tech developers seeking to scale precision agriculture solutions, positioning agricultural drones as a critical tool for advancing sustainable and technologically enabled farming in India.

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Publication Details

Journal
Frontiers in Sustainable Food Systems
Published
2025-11-28
DOI
https://doi.org/10.3389/fsufs.2025.1695231
Primary Topic
Smart Agriculture and AI
Type
article
Field-Weighted Citation Impact
0.00

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article

Modeling determinants of farmers’ attitude and adoption willingness toward agricultural drones: a PLS-SEM study in India

Sk Wasaful Quader, Manjeet Singh Nain, R.N. Padaria, Bikram Barman et al.
Frontiers in Sustainable Food Systems
Smart Agriculture and AI
article

Modeling determinants of farmers’ attitude and adoption willingness toward agricultural drones: a PLS-SEM study in India

Sk Wasaful Quader, Manjeet Singh Nain, R.N. Padaria, Bikram Barman, Rashmi Singh
article en

Abstract

Agricultural drones represent a rapidly advancing innovation in modern farming, offering significant potential to enhance productivity, optimize input use, reduce labor dependency, and support real-time data-based decision-making. However, despite their proven advantages and increasing market availability, adoption among Indian farmers remains limited. Understanding the factors that shape farmers’ attitudes and willingness to adopt drone technology is essential for overcoming barriers and promoting large-scale adoption. This study examines the determinants influencing farmers’ attitudes toward agricultural drone technology and their willingness to adopt. A quantitative research approach was adopted, using a structured questionnaire administered to 320 farmers from selected districts in Haryana and Uttar Pradesh, India. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess the influence of key predictors on Attitude Toward Technology (ATT). The structural model results indicated that six of the eight hypothesized predictors, Promotional Efforts, Perceived Usefulness, Peer Pressure, Perceived Economic Viability, Perceived Environmental Impact, and Perceived Social Impact, had a statistically significant positive effect on ATT and contributed substantially to explaining variance in adoption willingness. These results reveal that farmer decisions are shaped by psychological, economic, environmental, and social dimensions, rather than purely technical considerations. The study emphasizes that effective adoption strategies must prioritize awareness enhancement, hands-on training, field demonstrations, and financial support mechanisms to improve accessibility and trust. Strengthening institutional support and fostering positive peer experiences could further accelerate technology acceptance among less innovative farmers and later adopters. The findings provide valuable insights for policymakers, extension agencies, and agri-tech developers seeking to scale precision agriculture solutions, positioning agricultural drones as a critical tool for advancing sustainable and technologically enabled farming in India.

Frontiers in Sustainable Food SystemsVol. 9
Indian Agricultural Research Institute (IN)
Indian Council of Agricultural Research, Indian Agricultural Research Institute
Openalex Percentile: Top 73%
Smart Agriculture and AI
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