The study and application of the IOT technology in agriculture

Zhao, Jichang; Zhang, Junfeng; Yu, Feng; Guo, Jianxin

doi:10.1109/iccsit.2010.5565120

Cited by 115 publications

(24 citation statements)

References 4 publications

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“…The innovation of IoT solutions in agriculture is continuously increasing. IoT devices are collecting vast amounts of data, which could help to improve the soil quality, water management, crop health, and production strength, and thus the quality of agriculture in general (Gómez et al, 2019; Mekala and and Viswanathan, 2017; Mohanraj et al, 2016; Patil and Kale, 2017; Shifeng et al, 2011; Zhao et al, 2010)”. “A NABARD (National Bank of Agriculture and Rural Development, 2017) survey in 2013 said that the world agriculture production is affected by 20%–30% by the annual loss due to plant disease (‘NABARD-2016’; Ayaz et al, 2019).…”

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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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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“…The system measures and records critical temperature, humidity and soil signals which is then transmitted through the network to the platform for processing. Another application [8] uses a ZigBee WSN to monitor physiological data of patients that automatically generates electronic medical records.…”

Section: Information Aggregation Servicesmentioning

confidence: 99%

Internet of Things: Services and Applications Categorization

Gigli¹,

Koo²

2011

AIT

164

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In this paper we attempt to categorize the services provided by the Internet of Things (IoT) in order to help application developers build upon a base service. First we introduce the four main categories of services, and then follow by providing a number of examples of each of the service categories so as to provide an example of how each type of service might be implemented, and how it can be used to build an IoT application.

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“…In recent decades, miniaturized humidity sensors have been realized using various transduction methods [1,2,3,4,5,6,7] for a wide range of applications such as improving indoor comfort in homes and automobiles, humidity monitoring in semiconductor processing facilities [8], food processing industries [9], medical facilities [10] and, Internet-of-Things (IoT) based frameworks [11]. The push for the need for low-power chemical sensors [12,13,14] has been quite strong due to the growing importance of IoT sensor nodes across the world.…”

Section: Introductionmentioning

confidence: 99%

Parametrically Amplified Low-Power MEMS Capacitive Humidity Sensor

Likhite

Banerjee

Majumder

et al. 2019

Sensors

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We present the design, fabrication, and response of a polymer-based Laterally Amplified Chemo-Mechanical (LACM) humidity sensor based on mechanical leveraging and parametric amplification. The device consists of a sense cantilever asymmetrically patterned with a polymer and flanked by two stationary electrodes on the sides. When exposed to a humidity change, the polymer swells after absorbing the analyte and causes the central cantilever to bend laterally towards one side, causing a change in the measured capacitance. The device features an intrinsic gain due to parametric amplification resulting in an enhanced signal-to-noise ratio (SNR). Eleven-fold magnification in sensor response was observed via voltage biasing of the side electrodes without the use of conventional electronic amplifiers. The sensor showed a repeatable and recoverable capacitance change of 11% when exposed to a change in relative humidity from 25–85%. The dynamic characterization of the device also revealed a response time of ~1 s and demonstrated a competitive response with respect to a commercially available reference chip.

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The study and application of the IOT technology in agriculture

Cited by 115 publications

References 4 publications

Smart paddy field monitoring system using deep learning and IoT

Smart paddy field monitoring system using deep learning and IoT

Internet of Things: Services and Applications Categorization

Parametrically Amplified Low-Power MEMS Capacitive Humidity Sensor

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