A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

Farooq, Muhammad Shoaib; Riaz, Shamyla; Abid, Adnan; Abid, Kamran; Naeem, Muhammad

doi:10.1109/access.2019.2949703

Cited by 515 publications

(182 citation statements)

References 96 publications

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“…For the data analytics conponent, various algorithms and models are used for data analytics. Some IoT and data analytics based smart agriculture applications include livestock health monitoring, stress level monitoring, physical gesture recognition, Rumination, heart rate tracking, livestock location monitoring, climate condition monitoring, pest identification, irrigation management, greenhouse gas monitoring, and so on [39].…”

Section: Iot Data Analytics In Agriculturementioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

162

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The Internet of Things (IoT) applications has grown in exorbitant numbers, generating a large amount of data required for intelligent data processing. However, the varying IoT infrastructures (i.e. cloud, edge, fog) and the limitations of the IoT application layer protocols in transmitting/receiving messages become the barriers in creating intelligent IoT applications. These barriers prevent current intelligent IoT applications to adaptively learn from other IoT applications. In this paper, we critically review how IoT-generated data is processed for machine learning analysis, and highlight the current challenges in furthering intelligent solutions in the IoT environment. Furthermore, we propose a framework to enable IoT applications to adaptively learn from other IoT applications, and present a case study in how the framework can be applied to the real studies in the literature. Finally, we discuss the key factors that have an impact on future intelligent applications for the IoT.

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Section: Iot Data Analytics In Agriculturementioning

confidence: 99%

Machine learning and data analytics for the IoT

Adi

Anwar

Baig

et al. 2020

Neural Comput & Applic

162

View full text Add to dashboard Cite

show abstract

“…Current smart agriculture systems consist of various ICT devices such as a variety of sensors, high-definition camera devices like CCTVs, autonomous drones, and voice recognition equipment. Such devices generate colossal volumes of data that include unstructured multimedia data such as videos, images, and sounds as well as structured numerical values such as temperature, humidity, illumination, and air condition [1]. In smart agriculture, the collected data are helpful in making optimal decisions for facility automation, such as optimal temperature and humidity control, and automatic feed supply.…”

Section: Deep Learning In Smart Agriculturementioning

confidence: 99%

“…In rural areas, the increasing age of populations and reduction in quantity will lead to weakness of economic power due to labor shortage. Smart agriculture technologies are already being used in many countries as an approach, using information and communication technology (ICT) to effectively solve rural problems such as the above-mentioned labor shortage [1,2]. Recently, smart agriculture has attempted and implemented full automation incorporating other trending technologies such as big data, Internet of Things (IoT), and artificial intelligence (AI) to improve farmers' quality of life (QoL) [3].…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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“…It is composed of interconnected embedded devices with various sensors and actuators such as temperature, humidity, soil moisture sensors, motors, variable-rate sprayer, etc. and reports time-series data to a remote application that supports in the optimization of agricultural processes [5] [6].…”

Section: Introductionmentioning

confidence: 99%

SGF-MD: Behavior Rule Specification-Based Distributed Misbehavior Detection of Embedded IoT Devices in a Closed-Loop Smart Greenhouse Farming System

et al. 2020

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Smart farming is rapidly revolutionizing the agricultural sector where embedded Internet of Things (IoT) devices are integrated into the field to maintain or improve the quality of products as well as increase food production. Despite the tremendous benefits, various cybersecurity threats of IoT can also be inherited by the sector. In this paper, we propose a lightweight specification-based distributed detection to identify the misbehavior of heterogeneous embedded IoT nodes efficiently and effectively in a closed-loop smart greenhouse farming system. To expand the monitoring space of a node, we exploited the Kalman-filter algorithm and simple statistical operations to obtain estimates of data. Accordingly, this enables a monitoring node to assess a target node that has distinct physical characteristics and access to natural phenomena. Along with this, we derive the behavior-rules that are specific to the target system and carefully translate these rules into a state machine diagram. Besides, we formally verify the functional correctness of the monitoring processes as well as ensure that the behavior specifications are completely covered by using the model checker tool UPPAAL. Through extensive experimental simulation using Proteus, we verify its applicability to resource-constrained embedded devices, e.g., Arduino-Uno, as well as show high accuracy in detecting misbehaving nodes while having low false alarms.

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A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

Cited by 515 publications

References 96 publications

Machine learning and data analytics for the IoT

Machine learning and data analytics for the IoT

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

SGF-MD: Behavior Rule Specification-Based Distributed Misbehavior Detection of Embedded IoT Devices in a Closed-Loop Smart Greenhouse Farming System

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