Environmental monitoring and disease detection of plants in smart greenhouse using internet of things

Khan, Fazeel Ahmed; Ibrahim, Adamu Abubakar; Zeki, Akram M.

doi:10.1088/2399-6528/ab90c1

Cited by 36 publications

(18 citation statements)

References 14 publications

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“…In [9], the paper presents an on field study in a tomato greenhouse located in Michurinsk, Russia's Tambov region with deployment of the Internet of Things (IoT) to identify the monitoring aspect of greenhouse and the growth rate of tomatoes. Similar application of the Internet of Things (IoT) in automation of greenhouse environment has been performed in the paper [10]. The paper [11] demonstrated an IoT system with a bot notification on tomato growing stages to provide smart farming solutions.…”

Section: Review Of Literaturementioning

confidence: 81%

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

Fatima¹,

Siddiqui²,

Ahmad³

2021

IJACSA

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Rapid industrialization and urbanization has led to decrease in agricultural land and productivity worldwide. This is combined with increasing demand of chemical free organic vegetables by the educated urban households, and thus, greenhouses are quickly catching trend for their specialized advantages especially in extreme weather countries. They provide an ideal environment for longer and efficient growing seasons and ensure profitable harvests. The present paper designs and demonstrates a comprehensive IoT based Smart Greenhouse system that implements a novel combination of monitoring, alerting, cloud storage, automation and disease prediction, viz. a readily deployable complete package. It continuously keeps track of ambient conditions like temperature, humidity and soil moisture conditions to ensure a higher yield of crop and immediate redressal in case of abnormal conditions. It also has a built-in automatic irrigation management system. Finally, it employs the most efficient deep learning model for disease identification with leaf images. Furthermore, with memory and storage optimization through cloud storage, an individual living in the city can also build a greenhouse and can monitor it from his home and take redressal methods as and when desired.

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Section: Review Of Literaturementioning

confidence: 81%

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

Fatima¹,

Siddiqui²,

Ahmad³

2021

IJACSA

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“…At the same time, cloud computing is scalable, processes big data, and has unlimited computational processing abilities. In contrast, edge computing is storage-limited, requires interconnection through proprietary networks, and is highly power-consuming [50,51,59,60]. Moreover, edge computing can be combined with cloud computing to enhance the efficiency of both approaches yielding a hybrid edgecloud computing model [58].…”

Section: Main Differences Between Cloud Computing and Edgementioning

confidence: 99%

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Molo

Badejo

Adetiba

et al. 2021

Applied Computational Intelligence and Soft Computing

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Cloud computing is a technology that allows dynamic and flexible computing capability and storage through on-demand delivery and pay-as-you-go services over the Internet. This technology has brought significant advances in the Information Technology (IT) domain. In the last few years, the evolution of cloud computing has led to the development of new technologies such as cloud federation, edge computing, and fog computing. However, with the development of Internet of Things (IoT), several challenges have emerged with these new technologies. Therefore, this paper discusses each of the emerging cloud-based technologies, as well as their architectures, opportunities, and challenges. We present how cloud computing evolved from one paradigm to another through the interplay of benefits such as improvement in computational resources through the combination of the strengths of various Cloud Service Providers (CSPs), decrease in latency, improvement in bandwidth, and so on. Furthermore, the paper highlights the application of different cloud paradigms in the healthcare ecosystem.

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“…With the advent of the Internet of Things (IoT), an agricultural monitoring system for smart farming has been developed and utilized, enabling remote monitoring and control of agricultural environmental conditions such as temperature, humidity, light, and water level. − Accordingly, the agricultural monitoring system s for smart farming comprise various sensors, such as temperature and humidity sensors, light detectors, pH sensors, etc. − The accurate detection of water level is crucial for automatically and optimally watering agricultural crops. Resistive type water level sensors are suitable for agricultural monitoring systems owing to their simplicity, ease of operation, and low manufacturing cost compared with those of other types of water level sensors, such as ultrasonic, magnetic float, and radar level sensors. − When a plant is watered, the water comes in contact with the resistive water level sensor next to the plant, resulting in the detection of water level by the reduced the resistance of the water level sensor.…”

Section: Introductionmentioning

confidence: 99%

Resistive Water Level Sensors Based on AgNWs/PEDOT:PSS-g-PEGME Hybrid Film for Agricultural Monitoring Systems

et al. 2022

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Recently, an agricultural monitoring system using the Internet of Things has been developed to realize smart farming. The high performance of various sensors in agricultural monitoring systems is essential for smart farming to automatically monitor and control agricultural environmental conditions such as temperature and water level. In this study, we propose resistive water level sensors based on an AgNWs/PEDOT:PSS- g -PEGME hybrid structure to improve the already high conductivity and water stability of PEDOT:PSS. After spin-coating the AgNWs/PEDOT:PSS- g -PEGME hybrid film, a laser treatment method successfully patterns the resistive water level sensor with areas of higher resistance. When water contacts the sensor, the variation in resistance caused by the water level changes the current flow of the sensor, allowing it to be used to detect the water level. Finally, we develop a water level sensor module as a component of the agricultural monitoring system by connecting the sensor to a microcontroller for water level monitoring in real time. The proposed water level sensors may be a new solution for detecting water levels in agricultural monitoring systems.

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Environmental monitoring and disease detection of plants in smart greenhouse using internet of things

Cited by 36 publications

References 14 publications

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

IoT-based Smart Greenhouse with Disease Prediction using Deep Learning

A Review of Evolutionary Trends in Cloud Computing and Applications to the Healthcare Ecosystem

Resistive Water Level Sensors Based on AgNWs/PEDOT:PSS-g-PEGME Hybrid Film for Agricultural Monitoring Systems

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