WSN Based Smart Control and Remote Field Monitoring of Pakistan’s Irrigation System Using SCADA Applications

Katyara, Sunny; Shah, Madad Ali; Zardari, Shehnila; Chowdhry, Bhawani Shankar; Kumar, Wanod

doi:10.1007/s11277-016-3905-5

Cited by 27 publications

(8 citation statements)

References 4 publications

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“…Recently multiple IoT irrigation platforms have been developed to control the water consumption in the field. In [73] a simple irrigation system has been developed by using WSN. In more advance system users can control the process of irrigation through cellular technologies.…”

Section: D: Irrigation Monitoring Systemmentioning

confidence: 99%

“…This whole information is stored in the computer in standard format and can be accessible via cell phone or internet for further processing. To optimize the use of water resources an automated irrigation and monitoring system is used [73]. Apart from irrigation system farm is also protected from pest and animal intrusion [66], [67].…”

Section: G: Farm Management Systemmentioning

confidence: 99%

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

et al. 2019

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Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented.

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Section: D: Irrigation Monitoring Systemmentioning

confidence: 99%

Section: G: Farm Management Systemmentioning

confidence: 99%

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

et al. 2019

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“…In the last decade, a vast number of research work have been reported on different aspects related to smart field monitoring in the agriculture sector with the support of deep learning (Mehra et al, 2018; Sa et al, 2016; Sethy et al, 2020a, 2020b; Su et al, 2020), IoT (Doshi et al, 2019; Mehra et al, 2018; Mekala and Viswanathan, 2019; Nageswara Rao and Sridhar, 2018; Prathibha et al, 2017; Rajalakshmi and Devi Mahalakshmi, 2016; Rezk et al, 2020) and WSN (Katyara et al, 2017; Khan and Kumar, 2020). Table 1 shows the most popular methods and models for smart field monitoring.…”

Section: Background Studymentioning

confidence: 99%

Smart paddy field monitoring system using deep learning and IoT

Sethy

Behera

Kannan

et al. 2021

Concurrent Engineering

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Paddy is an essential nutrient worldwide. Rice gives 21% of worldwide human per capita energy and 15% of per capita protein. Asia represented 60% of the worldwide populace, about 92% of the world’s rice creation, and 90% of worldwide rice utilization. With the increase in population, the demand for rice is increased. So, the productivity of farming is needed to be enhanced by introducing new technology. Deep learning and IoT are hot topics for research in various fields. This paper suggested a setup comprising deep learning and IoT for monitoring of paddy field remotely. The vgg16 pre-trained network is considered for the identification of paddy leaf diseases and nitrogen status estimation. Here, two strategies are carried out to identify images: transfer learning and deep feature extraction. The deep feature extraction approach is combined with a support vector machine (SVM) to classify images. The transfer learning approach of vgg16 for identifying four types of leaf diseases and prediction of nitrogen status results in 79.86% and 84.88% accuracy. Again, the deep features of Vgg16 and SVM results for identifying four types of leaf diseases and prediction of nitrogen status have achieved an accuracy of 97.31% and 99.02%, respectively. Besides, a framework is suggested for monitoring of paddy field remotely based on IoT and deep learning. The suggested prototype’s superiority is that it controls temperature and humidity like the state-of-the-art and can monitor the additional two aspects, such as detecting nitrogen status and diseases.

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“…Warning alarms are present in proposals [25,27]. These alarms are focused on the monitored parameters.…”

Section: System Characteristics Comparisonmentioning

confidence: 99%

Cluster-Based Communication Protocol and Architecture for a Wastewater Purification System Intended for Irrigation

Lloret¹,

García

Jiménez

et al. 2021

IEEE Access

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WSN Based Smart Control and Remote Field Monitoring of Pakistan’s Irrigation System Using SCADA Applications

Cited by 27 publications

References 4 publications

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

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

Smart paddy field monitoring system using deep learning and IoT

Cluster-Based Communication Protocol and Architecture for a Wastewater Purification System Intended for Irrigation

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