Controlled-environment agriculture is rapidly becoming data-intensive and cyber-physical with the rapid digitalization of controlled-environment greenhouses. With artificial intelligence, IoT frameworks, and robotic surveillance systems becoming integrated in greenhouse operations, algorithms are playing a larger role in the managerial decision-making process instead of human supervisors alone. This change opens the idea of algorithmic management to the world of agricultural workforce - a field that has not been sufficiently investigated in the existing studies. This paper constructs a socio-technical system to examine the effect of the algorithm systems on workforce scheduling, performance tracking, and coordination of operations in the greenhouse environment. An optimization model in mathematics is presented to structure task distribution based on efficiency, fairness, and worker fatigue where multi-objective scheduling can be used to achieve productivity and human well-being. The paper offers a proposed structured simulation dataset and a survey instrument to help assess worker perceptions of surveillance and autonomy and fairness to support future empirical research. A comparative analysis of traditional and algorithmic management models indicates that there are trade-offs between agency and precision of operations and labor. The results emphasize that algorithmic management in the agricultural sector is not an issue of technical improvement but a governance problem that needs to be transparent, accountable, and human-centered. This study forms a conceptual and analytical base of ethically responsible AI-based workforce management in smart greenhouse settings and adds to the discussion on the future of human-AI collaboration in industrial systems.
Abedin et al. (Thu,) studied this question.