1University Research Scholar, Bidhan Chandra Krishi Viswavidyalaya (Agril. University), Nadia, West Bengal, India
2Vice Chancellor, Uttar Banga Krishi Viswavidyalaya (Agril. University), Cooch Behar, West Bengal, India
The study undertook a comprehensive analysis of stakeholder feedback regarding agricultural drones, employing sophisticated machine-learning algorithms to elucidate the diverse perceptions surrounding this emerging technology. A multi-pronged data collection strategy was developed to generate feedback. Utilizing advanced machine-learning algorithms, the data were analyzed using TF-IDF, topic modeling, core idea extraction, complementary connotation-wise categorization, co-occurrence with likelihood ratio, hierarchical cluster analysis, silhouette scoring, multi-dimensional scaling techniques, and sentiment analysis. The findings were presented with tables and figures highlighting various dimensions of the data. The implications of this study are manifold, offering critical insights for drone companies, policymakers, and stakeholders while delineating pathways for future research aimed at fostering user trust and integrating drone technology with existing precision farming frameworks.
Agricultural drones, Stakeholder feedback, Machine learning, Topic modeling, Hierarchical cluster analysis, Multi-dimensional scaling, Sentiment analysis
