###### Abstract  
Smart farming with precise greenhouse monitoring in various scenarios is vital for improved agricultural growth management. The Internet of Things (IoT) leads to a modern age in computer networking that is gaining traction. This paper used a regression-based supervised machine learning approach to demonstrate a precise control of sensing parameters, CO\({}_{2}\), soil moisture, temperature, humidity, and light intensity, in a smart greenhouse agricultural system. The proposed scheme comprised four main components: cloud, fog, edge, and sensor. It was found that the greenhouse could be remotely operated for the control of CO\({}_{2}\), soil moisture, temperature, humidity, and light, resulting in improved management. Overall implementation was remotely monitored via the IoT using Message Query Telemetry Transport (MQTT), and sensor data were analysed for their standard and anomalous behaviours. Then, for practical computation over the cloud layer, an analytics and decision-making system was developed in the fog layer and constructed using supervised machine learning algorithms for precise management using regression modelling methods. The proposed framework improved its presentation and allowed us to properly accomplish the goal of the entire framework. Internet of Things (IoT); machine learning; precise greenhouse management; regression modelling; smart farming +
Footnote †: journal: Applied sciences  
a vast framework that has lately emerged. In the case of a sudden shift in the greenhouse, it entails identifying, estimating, and transmitting nursery data to farmers [4; 5]. A greenhouse is a simple structure that maintains a microclimate favourable to solid plant growth, such as water stream management, temperature control, and other factors. As a result, it offers shielding from, e.g., too much light, extreme temperatures, infections, and creepy crawlies. The four essential benefits of an intelligent greenhouse system, as indicated in Figure 1, are maintaining perfect microclimate conditions, optimizing irrigation and fertilization methods, controlling infection and avoiding disease outbreaks, and reducing theft and enhancing security. By maintaining the proper environmental conditions, farmers could cultivate any plant in all seasons. Greenhouse farming is significant, which illustrates why it has become so popular. Because greenhouses hold moisture, they use far less water than traditional horticulture. This reduces the time spent editing and expands the sorts of harvests available. Temperature and humidity are carefully controlled to meet the demands of the plants. Nurseires can also be used to develop slow-growing crops. Pests may be readily dealt with. Harvesting may be made under various environmental conditions, making them incredibly versatile. The varying climatic conditions in the greenhouse affect plant growth, resulting in a lower yield at the end of cultivation. As a result, greenhouse gases, temperature, soil moisture, and light must all be managed and monitored. This problem could be solved by implementing IoT innovations in precision agriculture using machine learning, which involves the precise application of certain greenhouse factors for optimal plant development, such as temperature management, water flow control, and light radiation, among others. In precision agriculture, agricultural inputs are utilised in precise amounts to obtain increased average yields, compared with traditional cultivation techniques, resulting in an increase in agricultural productivity, which prevents soil degradation; reduces the use of the chemical applications, fertilisers, pesticides, and herbicides in crop production; and promotes the efficient use of water resources. It is also helpful in the dissemination of modern farm practices to improve quality and quantity and reduce the cost of production.