An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

Geng, Lei; Dong, Ting

doi:10.3991/ijoe.v13i12.7885

Cited by 25 publications

(14 citation statements)

References 4 publications

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“…For example, star networks are composed of a central node (coordinator) and several peripheral nodes. In such topology, peripheral nodes send data to the central node [ 93 ]. Therefore, the maximum distance between the peripheral nodes and the central node is limited by the maximum distance allowed by the physical layer communication standard.…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

226

100

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The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of IoT Solutions for Smart Farming

Navarro

Costa

Pereira

2020

Sensors

226

100

View full text Add to dashboard Cite

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“…The increased structure complexity means DL can process large scale and complex data with more learning capacity to characterize input and targeting data. In modern agriculture where the use of a wireless sensor network (WSN) [33] is prevalent, the huge amount of data produced will make the application of DL well suited, and usually leads to better performance.…”

Section: Background On Deep Learning Network Architecturementioning

confidence: 99%

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

2021

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Water stress is one of the major challenges to food security, causing a significant economic loss for the nation as well for growers. Accurate assessment of water stress will enhance agricultural productivity through optimization of plant water usage, maximizing plant breeding strategies, and preventing forest wildfire for better ecosystem management. Recent advancements in sensor technologies have enabled high-throughput, non-contact, and cost-efficient plant water stress assessment through intelligence system modeling. The advanced deep learning sensor fusion technique has been reported to improve the performance of the machine learning application for processing the collected sensory data. This paper extensively reviews the state-of-the-art methods for plant water stress assessment that utilized the deep learning sensor fusion approach in their application, together with future prospects and challenges of the application domain. Notably, 37 deep learning solutions fell under six main areas, namely soil moisture estimation, soil water modelling, evapotranspiration estimation, evapotranspiration forecasting, plant water status estimation and plant water stress identification. Basically, there are eight deep learning solutions compiled for the 3D-dimensional data and plant varieties challenge, including unbalanced data that occurred due to isohydric plants, and the effect of variations that occur within the same species but cultivated from different locations.

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“…Geng and Dong 50 used IoT techniques with deep learning methodology for plant monitoring. To monitor environmental parameters, soil temperature and moisture sensor, humidity sensor, light intensity sensor, and temperature sensor were used.…”

Section: Iot‐based Agricultural Monitoring and Controlling Systemmentioning

confidence: 99%

Systematic review of Internet of Things in smart farming

Terence

Geethanjali

2020

Trans Emerging Tel Tech

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Agriculture unquestionably is one of the traditional occupations, which feeds all mankind in the world. Continuous changes are happening in the agricultural field to increase production. Researchers are applying various techniques to improve farming methods. To monitor plants even from remote places and to improve the yield of plants, Internet of Things (IoT), which is a boon in today's world, is applied in farming, in general, known as smart farming. Smart farming is a way where the farmers can monitor their field and manage farming activities from remote places. This reduces man power and increases resource utilization in farming. In this article, we have studied the architecture of smart farming and studied different smart farming techniques, also we have classified smart farming techniques into three categories, namely, IoT-based agricultural monitoring and controlling system, automatic irrigation system, and plant disease monitoring system. The review for the article is selected based on the systematic literature review method, and articles published from 2011 to 2019 are considered for review. Different IoT technologies such as sensors, gateway, communication system, user interface and experiment nature, plant type, disease type, advantages, and limitations are also reviewed. Future research direction and challenges in smart farming techniques are also discussed. Trans Emerging Tel Tech. 2020;31:e3958. wileyonlinelibrary.com/journal/ett Atzori et al 16 presented a survey on IoT techniques. In this article, the architecture, applications, and challenges of IoT technologies were discussed. Various environmental and agricultural monitoring applications were discussed. Zhang et al 17 projected various agricultural data transmission methods in his review article, namely, voice information transmission, short message service (SMS) information transmission, online information transmission, video conference information transmission, and multichannel-based information transmission techniques. Case studies, features, applications, examples, and limitations of various information transmission techniques were discussed. Kamilaris et al 18 projected survey on big data analysis in agriculture. In this article, 34 agriculture works were considered for the study and different features such as tools applied, big data algorithm used, problem and proposed solution implemented, and data used were discussed. The type of plants and experimental duration and advantages and limitation of algorithms were missing. Talavera et al 14 reviewed IoT applications in environment and agroindustrial system. The authors discussed about various IoT techniques involved in monitoring, communication, logistics, energy and resource management in environment monitoring, and agroindustrial system. In this study, the authors approached all the IoT techniques with following two questions: what is the primary technical solution of IoT in environmental monitoring and agroindustrial system and which methodology and IoT components such as sensors, actuato...

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An Agricultural Monitoring System Based on Wireless Sensor and Depth Learning Algorithm

Cited by 25 publications

References 4 publications

A Systematic Review of IoT Solutions for Smart Farming

A Systematic Review of IoT Solutions for Smart Farming

Deep Learning Sensor Fusion in Plant Water Stress Assessment: A Comprehensive Review

Systematic review of Internet of Things in smart farming

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