A Survey: Smart agriculture IoT with cloud computing

2017 ◽  
Author(s):  
Mahammad Shareef Mekala ◽  
P. Viswanathan
Keyword(s):  
Cloud Computing ◽  
Smart Agriculture

scholarly journals A survey on agriculture and greenhouse monitoring using IOT and WSN

2018 ◽  
Vol 7 (3.3) ◽  
pp. 673 ◽  
Author(s):  
M Lavanya ◽  
Sujatha Srinivasan
Keyword(s):  
Cloud Computing ◽  
Internet Of Things ◽  
Electronic Device ◽  
Physical Property ◽  
Smart Services ◽  
Physical Objects ◽  
Smart Agriculture

Internet of things (IoT) is connecting physical objects around us; those physical objects can be monitored with the help of sensors. A sensor is a device, which is used to sense physical property of an element, any events or any changes present in the environment and send the in-formation to other electronic device, frequently a computer. Many research are made on those sensor enabled IoT system to provide intelli-gent and smart services, towards smart greenhouse and smart agriculture .This paper will explore various existing IoT based agriculture and greenhouse system according to their deployment with an intension of identifying how it can be improved in future using IoT, WSN and a very recent scenario of using cloud computing.   

Digital Technologies for Smart Agriculture

2021 ◽  
pp. 54-67
Author(s):  
Garima Singh ◽  
Gurjit Kaur
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Digital Technologies ◽  
Agricultural Crop ◽  
Seed Selection ◽  
Crop Selection ◽  
Soil Preparation ◽  
Crop Cycle ◽  
Smart Agriculture

This chapter will provide the reader with an introduction to the modern emerging technologies like cloud computing, machine learning, and artificial intelligence used in agriculture. Then a glimpse of complete crop cycle follows, including seven steps, namely crop selection, soil preparation, seed selection, seed sowing, irrigation, crop growth, fertilizing and harvesting; and how these digital technologies are helpful for the crop cycle is also explained in this chapter. The rest of the chapter will explain the merger of the modern digital technologies with the agricultural crop cycle and how the future farming will work.

scholarly journals Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

2020 ◽  
Vol 10 (4) ◽  
pp. 1544 ◽  
Author(s):  
Kyuchang Lee ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Fog Computing ◽  
Multimedia Data ◽  
Network Congestion ◽  
Service Delay ◽  
Comparison Results ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Computing Capacity

Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

The Application of Big Geospatial Data in Smart Agriculture

2014 ◽  
Vol 955-959 ◽  
pp. 3835-3839
Author(s):  
Jiu Yan Ye ◽  
Bin Chen ◽  
Zun Lin Ke ◽  
Jian Chen ◽  
Yu Fang
Keyword(s):  
Cloud Computing ◽  
Development Trend ◽  
Geospatial Data ◽  
Research Progress ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Agriculture Industry ◽  
Key Technologies ◽  
Fast Development ◽  
Smart Agriculture

With the fast development of data acquisition and web transmission, the age of big geospatial data(BGD) has come. BGD will greatly change peoples’ life-style of and our society’s work mode. In agriculture field, there will also be a lot of changes brought by BGD in future. In this paper, we had a study on the processing of BGD and its application in agriculture. Firstly, we introduced BGD including its definition, value and application areas. Secondly, we summarized the research progress and key technologies of BGD processing in the cloud computing environment. Lastly, we talked about the application of BGD in agriculture industry and looked forward to its development trend.

scholarly journals A Systematic Survey on the Role of Cloud, Fog, and Edge Computing Combination in Smart Agriculture

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5922
Author(s):  
Yogeswaranathan Kalyani ◽  
Rem Collier
Keyword(s):  
Cloud Computing ◽  
Edge Computing ◽  
Future Research ◽  
Relevant Research ◽  
Research Directions ◽  
Architecture Model ◽  
High Bandwidth ◽  
Future Research Directions ◽  
Smart Agriculture

Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades. This article aims to provide a systematic literature review of current works that have been done in Cloud, Fog, and Edge Computing applications in the smart agriculture domain between 2015 and up-to-date. The key objective of this review is to identify all relevant research on new computing paradigms with smart agriculture and propose a new architecture model with the combinations of Cloud–Fog–Edge. Furthermore, it also analyses and examines the agricultural application domains, research approaches, and the application of used combinations. Moreover, this survey discusses the components used in the architecture models and briefly explores the communication protocols used to interact from one layer to another. Finally, the challenges of smart agriculture and future research directions are briefly pointed out in this article.

Cloud Computing Technologies for Smart Agriculture and Healthcare

2021 ◽  
Author(s):  
Urmila Shrawankar ◽  
Latesh Malik ◽  
Sandhya Arora
Keyword(s):  
Cloud Computing ◽  
Computing Technologies ◽  
Smart Agriculture
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