Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture

Shaikh, Faisal Karim; Karim, Sarang; Zeadally, Sherali; Nebhen, Jamel

doi:10.1109/jiot.2022.3210154

Cited by 49 publications

(26 citation statements)

References 123 publications

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“…We can implement different sensor network platforms, such as, Wasp mote Platform [19], Libelium Platform [19], Fleck Platform [5] and other platforms [5], [20], [21] for future correspondences.…”

Section: Wireless Platformsmentioning

confidence: 99%

“…Exhaust research work on WSN-based precision agriculture has been carried out by different researchers. Most of the research work on precision agriculture is based on various application scenarios like agricultural monitoring, data monitoring, greenhouses, pest detection, soil monitoring and irrigation management [5], [8], [12]- [18] by incorporating different methodologies, techniques, tools and platforms [19]- [21] along with basic guidelines [22]. In [23], a WSN-based system was developed for monitoring the fluoride-affected area.…”

mentioning

confidence: 99%

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Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Rahu

Karim

Shams

et al. 2023

SJET

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With the advent of the latest sensing technologies, agricultural tasks can be performed so quickly andadequately and termed Smart agriculture. In this paper, a system based on sensor networks has been designed tomonitor agricultural parameters wirelessly. The proposed system has been deployed in a Wheat field. The aim of this workis to increase the quality and productivity of the Wheat crops and minimize the extensive field visits of the farmers. Thissystem enables precision agriculture by periodically measuring the three most key parameters (temperature, light, andwater level) for achieving a remarkable increase in quality, productivity and growth of the Wheat crops. Thus, this systemhelps the agriculturists, landowners and research experts to monitor these parameters at the base station without goingto the field site. A GUI tool is also designed to display the measured data and stored it in the database accordingly. Whiledesigning this system; IRIS mote, MDA100 data acquisition board, and MIB520 USB interface board are employed. Weuse TinyOS operating system for the development of codes for wireless nodes and the GUI tool is designed in MicrosoftVisual Studio. ZigBee IEEE 802.15.4 protocol and direct topology are used for the communication of nodes with the basestation.

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Section: Wireless Platformsmentioning

confidence: 99%

mentioning

confidence: 99%

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Rahu

Karim

Shams

et al. 2023

SJET

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Section: Introductionmentioning

confidence: 99%

“…The maize crop's production ratio has been severely effected by the occurrence of various diseases, such as bacterial stalk root, which, if not controlled in its earliest stages, could possibly damage an entire field or farm. 2,4 Thus, technology-supported infrastructures, that is, those particularly designed for the agriculture domain (the maize crop), which must be cheaper, are required to be developed and implemented in a real agricultural environment.…”

Section: Introductionmentioning

confidence: 99%

“…The development of a technologically advanced agricultural domain has a significant contribution to the enhancement of the gross domestic product (GDP) of a country, preferably with similar resources such as farms, water, and pesticides. 1,2 In the agriculture sector, the adoption of basic machinery leads to increased yield, which is desperately required if the growing population of the world and its food requirements are considered. Yields of these crops have been further increased with the advent of the Internet of Things (IoT) and artificial intelligence (AI), which have the capacity to provide 24/7 monitoring, prediction of diseases, efficient utilization of resources such as water and pesticides (need-based methodology), and water deficit locations in the field.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

Al‐Otaibi,

Khan,

Ali

et al. 2024

Computational Intelligence

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Although the Internet of Things (IoT) has been considered one of the most promising technologies to automate various daily life activities, that is, monitoring and prediction, it has become extremely useful for problem solving with the introduction and integration of artificial intelligence (AI)‐enabled smart learning methodologies. Therefore, due to their overwhelming characteristics, AI‐enabled IoTs have been used in different application environments, such as agriculture, where detection, prevention (if possible), and prediction of crop diseases, especially at the earliest possible stage, are desperately required. Bacterial stalk root is a common disease of tomatoes that severely affects its production and yield if necessary measures are not taken. In this article, AI and an IoT‐enabled decision support system (DSS) have been developed to predict the possible occurrence of bacterial stalk root diseases through a sophisticated technological infrastructure. For this purpose, Arduino agricultural boards, preferably with necessary embedded sensors, are deployed in the agricultural field of maize crops to capture valuable data at a certain time interval and send it to a centralized module where AI‐based DSS, which is trained on an equally similar data set, is implemented to thoroughly examine captured data values for the possible occurrence of the disease. Additionally, the proposed AI‐ and IoT‐enabled DSS has been tested on benchmark data sets, that is, freely available online, along with real‐time captured data sets. Both experimental and simulation results show that the proposed scheme has achieved the highest accuracy level in timely prediction of the underlined disease. Finally, maize crop plots with the proposed system have significantly increased the yield (production) ratio of crops.

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Crop Yield Prediction Using Optimized Convolutional Neural Network Model Based on Environmental and Phenological Data

Haldorai,

Murugan

et al. 2024

Artificial Intelligence for Sustainable Development

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Recent Trends in Internet-of-Things-Enabled Sensor Technologies for Smart Agriculture

Cited by 49 publications

References 123 publications

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Wireless Sensor Networks-based Smart Agriculture: Sensing Technologies, Application and Future Directions

Artificial intelligence and Internet of Things‐enabled decision support system for the prediction of bacterial stalk root disease in maize crop

Crop Yield Prediction Using Optimized Convolutional Neural Network Model Based on Environmental and Phenological Data

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