An efficient employment of internet of multimedia things in smart and future agriculture

AlZu’bi, Shadi; Hawashin, Bilal; Mujahed, Muhannad; Jararweh, Yaser; Gupta, Brij B.

doi:10.1007/s11042-019-7367-0

Cited by 109 publications

(31 citation statements)

References 44 publications

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“…Employment of Big Data for smart agriculture is a completely new concept [114]. Although Big Data applications in smart agriculture are not that common, they are meant for cloud computing and IoT-based smart agriculture application [67]. Systems that support reasoning from real-time sensor data have the potential to deliver digital data sources for online services, operations, farmers, and processes by integrating a large number of data sources [131].…”

Section: Discussionmentioning

confidence: 99%

“…For efficient management of the irrigation process, it is important to detect the water status of plants. AlZu'bi et al [67] proposed image processing concepts, where IoT sensors work with machine learning methods to make smart irrigation decisions. Song et al [68] proposed a novel model combining deep belief network with macroscopic cellular automata (MCA) approach to predict the soil moisture content over an irrigated cornfield.…”

Section: G Smart Irrigationmentioning

confidence: 99%

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Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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Smart farming is a new concept that makes agriculture more efficient and effective by using advanced information technologies. The latest advancements in connectivity, automation, and artificial intelligence enable farmers better to monitor all procedures and apply precise treatments determined by machines with superhuman accuracy. Farmers, data scientists and, engineers continue to work on techniques that allow optimizing the human labor required in farming. With valuable information resources improving day by day, smart farming turns into a learning system and becomes even smarter. Deep learning is a type of machine learning method, using artificial neural network principles. The main feature by which deep learning networks are distinguished from neural networks is their depth and that feature makes them capable of discovering latent structures within unlabeled, unstructured data. Deep learning networks that do not need human intervention while performing automatic feature extraction have a significant advantage over previous algorithms. The focus of this study is to explore the advantages of using deep learning in agricultural applications. This bibliography reviews the potential of using deep learning techniques in agricultural industries. The bibliography contains 120 papers from the database of the Science Citation Index on the subject that were published between 2016 and 2019. These studies have been retrieved from 39 scientific journals. The papers are classified into the following categories as disease detection, plant classification, land cover identification, precision livestock farming, pest recognition, object recognition, smart irrigation, phenotyping, and weed detection.

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Section: Discussionmentioning

confidence: 99%

Section: G Smart Irrigationmentioning

confidence: 99%

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Ünal

2020

IEEE Access

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“…The duty cycling methodology is used to minimize energy consumption in special events like climate conditions. Shadi Al Zubi et al 26 have proposed an exact and efficient water management process to exploit irrigation availability because of a shortage of irrigation resources globally. Crops that are parched may have the difficulty in the breathing process, which in turn may give rise to leaves become yellow and sprinkles of leaves fall on the earth.…”

Section: Related Workmentioning

confidence: 99%

Precision agriculture and farming using Internet of Things based on wireless sensor network

Sanjeevi

Prasanna

Kumar

et al. 2020

Trans Emerging Tel Tech

104

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In the recent past, the agriculture and farming industry has become the precision network connectivity of sensors with a new dimension of Internet of Things (IoT) technology. The cloud computing and wireless sensor network-(WSN) based extensive distance network in IoT can be applied to the agriculture and farming industry in a remote area. In this paper, we propose scalable wireless sensor network architecture for monitoring and control using IoT for agriculture and farming in a remote area. One of the significant managements in precision agriculture and farming (PAF) is water resource irrigation and proper utilization of water resources. Appropriate utilization of water irrigation management can be achieved by applying WSN technology using IoT. The efficient communication of various wireless sensors is processed using IoT in PAF to improve the productivity of farmers. We have analyzed the WSN structure based on throughput maximization, latency minimization, high signal-to-noise ratio (SNR), minimum mean square error, and improved coverage area. The experimental results have proved that the proposed methodology provides better performance than conventional IoT-based agriculture and farming.

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“…Hawashin et al, [8], presented the importance of smart agriculture and provided the ideology for managing the water resource management in the agriculture. They adapted the IOt with the vision of multimedia i.e, Internet of multimedia things (IoMT) to optimize the irrigation process with the help of multimedia sensors.…”

Section: Related Workmentioning

confidence: 99%

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

Chandana

Ghantasala²,

Jeny

et al. 2020

IJECE

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The majority of research Study is moving towards cognitive computing, ubiquitous computing, internet of things (IoT) which focus on some of the real time applications like smart cities, smart agriculture, wearable smart devices. The objective of the research in this paper is to integrate the image processing strategies to the smart agriculture techniques to help the farmers to use the latest innovations of technology in order to resolve the issues of crops like infections or diseases to their crops which may be due to bugs or due to climatic conditions or may be due to soil consistency. As IoT is playing a crucial role in smart agriculture, the concept of infection recognition using object recognition the image processing strategy can help out the farmers greatly without making them to learn much about the technology and also helps them to sort out the issues with respect to crop. In this paper, an attempt of integrating kissan application with expert systems and image processing is made in order to help the farmers to have an immediate solution for the problem identified in a crop.

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An efficient employment of internet of multimedia things in smart and future agriculture

Cited by 109 publications

References 44 publications

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Smart Farming Becomes Even Smarter With Deep Learning—A Bibliographical Analysis

Precision agriculture and farming using Internet of Things based on wireless sensor network

An effective identification of crop diseases using faster region based convolutional neural network and expert systems

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