Drone-Based Crop Monitoring and Pest Detection: The Moderating Role of Technology Adoption Readiness among Farmers

Abstract

Agricultural productivity is increasingly threatened by pest infestations, diseases, and inefficient crop management practices. Drone-based crop monitoring systems provide a technological solution, enabling high-resolution aerial imaging, real-time data collection, and early pest detection. By integrating remote sensing, multispectral imaging, and artificial intelligence, drones allow precise assessment of crop health, facilitating timely interventions and resource optimization. This study investigates the impact of drone-based crop monitoring on pest detection and overall crop management, with a focus on the moderating role of farmers’ technology adoption readiness (TAR). TAR encompasses farmers’ willingness, skills, and preparedness to adopt and effectively utilize innovative technologies. High TAR is expected to enhance the effectiveness of drone-based monitoring by enabling accurate interpretation of data and timely implementation of pest management strategies. A quantitative research design was adopted, targeting farmers, agronomists, and agricultural extension officers using or familiar with drone technologies. Structured questionnaires assessed drone usage, pest detection efficiency, crop monitoring practices, and farmers’ technology adoption readiness. Data were analyzed using Smart PLS structural equation modeling to evaluate direct effects of drone-based crop monitoring and the moderating role of TAR on pest detection and crop management outcomes. Results indicate that drone-based crop monitoring significantly improves pest detection and crop management. Technology adoption readiness positively moderates this relationship, emphasizing that farmers who are more prepared and skilled in technology adoption derive greater benefits from drone-based systems. These findings highlight the importance of combining innovative technologies with human readiness and capacity-building initiatives to optimize agricultural productivity and pest management. Policymakers, extension services, and technology developers can leverage these insights to design effective adoption strategies and enhance sustainable farming practices.