From Innovation to Implementation: Factors Driving Chinese Farmers' Adoption Intentions for Laser Weeding Robots

Abstract

Laser weeding robots represent a transformative agricultural technology that overcomes the limitations of traditional weeding methods through precision weed recognition, reduced chemical pollution, and enhanced operational efficiency. However, their adoption among Chinese farmers faces critical barriers, including high implementation costs and insufficient technical awareness. This study employs the Unified Theory of Acceptance and Use of Technology (UTAUT) framework to investigate the determinants and mechanisms influencing farmers' behavioral intentions to adopt laser weeding robots. Through structural equation modeling analysis of 284 valid questionnaires, we identify performance expectancy, effort expectancy, social influence, and facilitating conditions as four core drivers, with effort expectancy (β=0.369) and social influence (β=0.361) demonstrating the strongest predictive jjjjpower. The findings reveal that farmers prioritize operational simplicity and peer recommendations over pure technical performance, while infrastructure compatibility exerts moderate influence (β=0.227). These insights provide empirical evidence for optimizing technology diffusion strategies in aging rural populations, suggesting multi-stakeholder interventions: (1) policy incentives for cost-sharing mechanisms and digital literacy programs targeting smallholders; (2) industry-driven interface simplification and terrain-adaptive designs; and (3) community-level demonstration farms leveraging opinion leaders. This research extends UTAUT applications to emerging agricultural robotics while offering actionable pathways for sustainable intensification in China's agricultural modernization.