Intelligent Agricultural Greenhouse Control System Based on Internet of
  Things and Machine Learning
Cangqing Wang
This study endeavors to conceptualize and execute a sophisticated
agricultural greenhouse control system grounded in the amalgamation of the
Internet of Things (IoT) and machine learning. Through meticulous monitoring of
intrinsic environmental parameters within the greenhouse and the integration of
machine learning algorithms, the conditions within the greenhouse are aptly
modulated. The envisaged outcome is an enhancement in crop growth efficiency
and yield, accompanied by a reduction in resource wastage. In the backdrop of
escalating global population figures and the escalating exigencies of climate
change, agriculture confronts unprecedented challenges. Conventional
agricultural paradigms have proven inadequate in addressing the imperatives of
food safety and production efficiency. Against this backdrop, greenhouse
agriculture emerges as a viable solution, proffering a controlled milieu for
crop cultivation to augment yields, refine quality, and diminish reliance on
natural resources [b1]. Nevertheless, greenhouse agriculture contends with a
gamut of challenges. Traditional greenhouse management strategies, often
grounded in experiential knowledge and predefined rules, lack targeted
personalized regulation, thereby resulting in resource inefficiencies. The
exigencies of real-time monitoring and precise control of the greenhouse's
internal environment gain paramount importance with the burgeoning scale of
agriculture. To redress this challenge, the study introduces IoT technology and
machine learning algorithms into greenhouse agriculture, aspiring to institute
an intelligent agricultural greenhouse control system conducive to augmenting
the efficiency and sustainability of agricultural production.