Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

Ayaz, Muhammad; Uddin, Mohammad Ammad; Sharif, Zubair; Mansour, Ali; Aggoune, El‐Hadi M.

doi:10.1109/access.2019.2932609

Cited by 801 publications

(332 citation statements)

References 145 publications

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“…Article on IoT-based smart agriculture [45] by the author Muhammad Ayaz et al identifies the current and future trends of IoT in agriculture. It states that LPWAN technology can be a game changer for smart agriculture and stands as better solution for connectivity because of long-range and affordable price for the farmers.…”

Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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The technology development in wireless sensor network (WSN) offers a sustainable solution towards precision agriculture (PA) in greenhouses. It helps to effectively use the agricultural resources and management tools and monitors different parameters to attain better quality yield and production. WSN makes use of Low-Power Wide-Area Networks (LPWANs), a wireless technology to transmit data over long distances with minimal power consumption. LoRaWAN is one of the most successful LPWAN technologies despite its low data rate and because of its low deployment and management costs. Greenhouses are susceptible to different types of interference and diversification, demanding an improved WSN design scheme. In this paper, we contemplate the viable challenges for PA in greenhouses and propose the successive steps essential for effectual WSN deployment and facilitation. We performed a real-time, end-to-end deployment of a LoRaWAN-based sensor network in a greenhouse of the ’Proefcentrum Hoogstraten’ research center in Belgium. We have designed a dashboard for better visualization and analysis of the data, analyzed the power consumption for the LoRaWAN communication, and tried three different enclosure types (commercial, simple box and airflow box, respectively). We validated the implications of real-word challenges on the end-to-end deployment and air circulation for the correct sensor readings. We found that temperature and humidity have a larger impact on the sensor readings inside the greenhouse than we initially thought, which we successfully solved through the airflow box design.

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Section: Related Workmentioning

confidence: 99%

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Singh

Aernouts

Meyer

et al. 2020

Sensors

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“…IoT has the potential of streamlining procedures, reduce wastage and enhance productivity in the agriculture sector. According to Ayaz et al (2019), IoT can help to improve the solutions of many traditional farming issues, like drought response, yield optimization, land suitability, irrigation and pest control by following the practices of smart agriculture. Further benefits can come from the quantity of fertilizer that has been utilized to the number of journeys the farm vehicles have made or the spray of pesticides (Ayaz et al, 2019).…”

Section: Application Of Iotmentioning

confidence: 99%

“…Major applications, services and transducers for smart agriculture(Ayaz et al, 2019) IoT Architecture (Vermesan et al, 2013). …”

mentioning

confidence: 99%

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

Chiwamba¹,

Phiri²,

Nkunika³

et al. 2019

Journal of Computer Science

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Maize is the main food crop that meets the nutritional needs of both humans and livestock in the sub-Saharan African region. Maize crop has in the recent past been threatened by the fall armyworm (Spodoptera frugiperda, J.E Smith) which has caused considerable maize yield losses in the region. Controlling this pest requires knowledge on the time, location and extent of infestation. In addition, the insect pest's abundance and environmental conditions should be predicted as early as possible for integrated pest management to be effective. Consequently, a fall armyworm pheromone trap was deployed as a monitoring tool in the present study. The trap inspection is currently carried out manually every week. The purpose of this paper is to bring automation to the trap. We modify the trap and integrate Internet of Things technologies which include a Raspberry Pi 3 Model B+ microcomputer , Atmel 8-bit AVR microcontroller, 3G cellular modem and various sensors powered with an off-grid solar photovoltaic system to capture real-time fall armyworm moth images, environmental conditions and provide real-time indications of the pest occurrences. The environmental conditions include Geographical Positioning System coordinates, temperature, humidity, wind speed and direction. The captured images together with environmental conditions are uploaded to the cloud server where the image is classified instantly using Google's pre-trained InceptionV3 Machine Learning model. Intended users view captured data including prediction accuracy via a web application. Once this smart technology is adopted, the labour-intensive task of monitoring will reduce while stakeholders shall be provided with a near real-time insight into the FAW situation in the field therefore enabling pro-activeness in their management of such a devastating pest.

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“…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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Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk

Cited by 801 publications

References 145 publications

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Leveraging LoRaWAN Technology for Precision Agriculture in Greenhouses

Automated Fall Armyworm (Spodoptera frugiperda, J.E. Smith) Pheromone Trap Based on Machine Learning

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

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