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, CO2, 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 CO2, 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.

In the farming industry, the Internet of Things (IoT) is crucial for boosting utility. Innovative agriculture practices and medical informatics have the potential to increase crop yield while using the same amount of input. Individuals can benefit from the Internet of Things in various ways. The intelligent farms require the creation of an IoT-based infrastructure based on sensors, actuators, embedded systems, and a network connection. The agriculture sector will gain new advantages from machine learning and IoT data analytics in terms of improving crop output quantity and quality to fulfill rising food demand. This paper described an intelligent medical informatics farming system with predictive data analytics on sensing parameters, utilizing a supervised machine learning approach in an intelligent agricultural system. The four essential components of the proposed approach are the cloud layer, fog layer, edge layer, and sensor layer. The primary goal is to enhance production and provide organic farming by adjusting farming conditions as per plant needs that are considered in experimentation. The use of machine learning on acquired sensor data from a prototype embedded model is investigated for regulating the actuators in the system. Then, an analytics and decision-making system was built at the fog layer, employing two supervised machine learning approaches including classification and regression algorithms using a support vector machine (SVM) and artificial neural network (ANN) for effective computation over the cloud layer. The experimental results are evaluated and analyzed in MATLAB software, and it is found that the classification accuracy using SVM is much better as compared to ANN and other state of art methods.

No abstract

No abstract

For improved agricultural growth control, smart farming with precise greenhouses is essential, as is precision agriculture monitoring in a variety of situations. The Internet of Things (IoT) is a new era in computer communication that is gaining pace as a result of its wide variety of project development applications. Individuals may benefit from the IoT through smart and remote ways such as smart agriculture, smart environment, smart security, and smart cities. These are the latest technologies that are making life simpler in today’s world. The IoT has significantly increased remote control and the variety of networked things or devices, which is a fascinating aspect. The hardware and internet connectivity to the real-time application make up the Internet of Things (IoT). The Internet of Things is made up of sensors, actuators, embedded systems, and a network connection. As a result, we’d want to develop an IoT application for smart farms. This paper demonstrated a remote parameter sensing system in smart greenhouse agriculture. The goal is to monitor greenhouse parameters like CO2, soil moisture, temperature, humidity, and light, with adjusting actions for greenhouse windows/doors based on crops. In this experimentation, Gerbera and Broccoli is considered. The primary purpose is to adjust greenhouse conditions in line with plant needs in order to increase production and provide organic farming. As a result of the findings, it appears that the greenhouse might be operated remotely for CO2, soil moisture, temperature, humidity and light, resulting in improved management. Overall implementation is remotely monitored via IoT using MQTT on Adafruit IO Cloud Platform and sensor data is analyzed for its normal and anomaly behavior.