Analysis of Precise Green House Management System using Machine Learning based Internet of Things (IoT) for Smart Farming

Rokade, Ashay; Singh, Manwinder

doi:10.1109/icosec51865.2021.9591962

Cited by 20 publications

(5 citation statements)

References 39 publications

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“…In their study, Rokade and Singh (2021) analyzed a greenhouse management system that employed ML-based IoT technology to enhance efficiency and productivity in the field of SA. The methodology examines a greenhouse management system that utilizes IoT and ML techniques [15]. The execution showcases the efficacy of the system within greenhouse environments.…”

Section: Related Workmentioning

confidence: 99%

IoT Integration for Enhanced Turmeric Cultivation: A Case Study in Smart Agriculture

Pandey,

Mishra

2024

BIO Web Conf.

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The agricultural sector serves as a fundamental cornerstone of the economies of numerous countries, necessitating technological advancements despite limited financial resources. The Internet of Things (IoT) presents a novel aspect within the field of soil health monitoring, which has significant implications for advancing smart agriculture and farming practices. Integrating conventional agricultural practices with cutting-edge technologies, such as the IoT and Wireless Sensor Networks (WSN), can foster Smart Agriculture (SA). This paper presents IoT Integration for Enhanced Turmeric Cultivation (IoT-ETmC) in the context of SA. The TurmFox IoT and Edge-to-Cloud (ETC) technology can analyze gathered data and send it to the user through internet connectivity. The work involves the implementation of TurmFox in experiments focused on turmeric cultivation. The results demonstrate a notable improvement in the quality of turmeric as a direct outcome of this intervention. The curcumin levels in the given product are notably higher, ranging from 4450 to 5450 mg per 120g. This paper also aims to demonstrate the intuitive configuration of sensor-to-actuator connections for implementing desired SA. The real-time data obtained from Turmfox provides information on the pH values, moisture levels, and temperature, allowing for observing dynamic variations in environmental conditions within the specified period. The pH level was 6.5 at 09:00, with a moisture content of 51 g/m3 and a temperature of 293 K.

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Section: Related Workmentioning

confidence: 99%

IoT Integration for Enhanced Turmeric Cultivation: A Case Study in Smart Agriculture

Pandey,

Mishra

2024

BIO Web Conf.

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“…Hassan et al [ 34 ] proposed a survey for data sharing techniques for 5G. A. Rokade and Singh [ 35 ] give a brief analysis of intelligent farming techniques which helps us to propose a sustainable intelligent farming model. A. Kadu and Singh [ 36 ] emphasize on distinguish data analysis for telemedicine systems which describes the uses of IoT and machine learning.…”

Section: Literature Reviewmentioning

confidence: 99%

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

Rokade

Singh

Arora

et al. 2022

Computational and Mathematical Methods in Medicine

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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.

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“…To find the best places for these mobile nodes, a new soft computing approach called Adaptive Plant Propagation Algorithm (APPA) is used [12]. The system presents an intelligent greenhouse system for adopting monitoring and controlling in the precision agricultural domain, based on an examination of current and prior related research that examine the greenhouse system's primary aspects, benefits, and limitations [15]. Examines how artificial intelligence (AI) and telemedicine have been employed to improve continuous monitoring [16].…”

Section: Related Workmentioning

confidence: 99%

An Autonomous Smart Farming System for Computational Data Analytics using IoT

Rokade

Kadu²,

Belsare³

2022

J. Phys.: Conf. Ser.

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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.

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Analysis of Precise Green House Management System using Machine Learning based Internet of Things (IoT) for Smart Farming

Cited by 20 publications

References 39 publications

IoT Integration for Enhanced Turmeric Cultivation: A Case Study in Smart Agriculture

IoT Integration for Enhanced Turmeric Cultivation: A Case Study in Smart Agriculture

IOT-Based Medical Informatics Farming System with Predictive Data Analytics Using Supervised Machine Learning Algorithms

An Autonomous Smart Farming System for Computational Data Analytics using IoT

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