Precision Farming: Sensor Analytics

Ivanov, Stepan; Bhargava, Kriti; Donnelly, William J.

doi:10.1109/mis.2015.67

Cited by 84 publications

(26 citation statements)

References 8 publications

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“…In the recent past, the prospects, opportunities, and feasibility of Wireless Sensor Networks (WSN) in agriculture have been widely studied [76]. The application of WSN in agriculture includes environmental monitoring, precision, agriculture, machine and process control automation and traceability [58,36]. Recently the importance of IoT has been highlighted because of its versatility of the adoption of different wired and wireless technologies as well as the capability to integrate with advanced data analysis mechanisms.…”

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

192

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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

192

View full text Add to dashboard Cite

show abstract

“…The weed detection is employed in [3] to reduce harm to existing surrounding plants where the images are gathered by a micro aerial vehicle (MAV). It uses the encoderdecoder cascaded convolutional neural network, SegNet for implementation, In [4] the Wireless sensor networks is used to explore possible WSN-based analytics towards precision farming. Precision farming practices used in [5] drastically progress farm production and improves the overall profit and minimizes the ecological pollution .…”

Section: Related Workmentioning

confidence: 99%

Smart Precision Agriculture using LORA and WeIO Interface

Thilagavathy*¹,

Ramesh²,

Selvi³

et al. 2019

IJRTE

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The capability to exploit smart technologies possibly will formulate today’s farmers need to direct their farming practice in a better efficient way. To ensue this, technology advancement towards the data access gathered by satellites and drones are vital. The skill of executing these requires real time data transfer with precise and cost-efficient method to foresee and protect the development of farming crops. In this paper, we propose a LORA based smart farming where WeIO interface module is implemented for accurate response to the farmer. Here different parameters pertaining to the growth of crops in agriculture like temperature, light, PH, humidity, soil moisture etc are considered. The method is tested and found to be less cost effective and accurate than existing methods.

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“…During the last few years, WSNs (wireless sensor network) have been widely studied and utilised in many industrial applications, related to forest monitoring [28], agriculture monitoring [29], and healthcare [30][31]. Compared to the practical working environment of above applications, marine environment systems are quite sensitive to the effects of human activities.…”

Section: Literature Reviewmentioning

confidence: 99%

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Cao

Zhao

Yang

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time.Index Terms-wireless sensor network deployment, 3D engine room, Very large crude-oil carrier (VLCC), Coverage, Lifetime, Reliability, Parallel, Multi-objective evolutionary algorithm.

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Precision Farming: Sensor Analytics

Cited by 84 publications

References 8 publications

Machine learning and data analytics for the IoT

Machine learning and data analytics for the IoT

Smart Precision Agriculture using LORA and WeIO Interface

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

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