A Survey on IoT Based Agricultural Crop Growth Monitoring and Quality Control

Devare, Jayvant; Hajare, Neha

doi:10.1109/icces45898.2019.9002533

Cited by 9 publications

(2 citation statements)

References 22 publications

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“…The increased volume of research literature has made it difficult for individuals to assess the vast quantity of work to identify the suitable technologies and best practices for a particular technology. To this extent, there have been previous efforts to provide an overview of different technologies in SF such as IoT [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19], data analysis and machine learning techniques [14, Manuscript submitted for review on January 14, 2020. This publication was made possible by grant # AICC02-0505-190022 from the Qatar National Research Fund (a member of Qatar Foundation).…”

Section: Introductionmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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As today's agriculture industry is facing numerous challenges, including climate changes, encroachment of the urban environment and lack of qualified farmers, there is a need for new practices to ensure sustainable agriculture and food supply. Consequently, there is an emphasis on upgrading the farming practices by shifting towards Smart Farming (SF) -utilizing advanced information and communication technologies to improve the quantity and quality of the crop with minimal labor interference. SF has gained lots of interest in recent years utilizing a variety of technological innovations in the field, which imposes a challenge on farmers and technology integrators to identify the suitable technologies and best practices for a particular application. This paper provides a survey of the most recent SF scientific literature to identify common practices toward technology integration, challenges, and solutions. The survey was conducted on 588 papers published on the IEEE database following Cochrane methods to ensure appropriate analysis and interpretation of results. The papers' contributions were analyzed to identify necessary technologies that constitute SF, and consequently, research themes were identified. The identified themes are sensors, communication, big data, actuators and machines, and data analysis. Besides presenting an in-depth analysis of each identified theme, the paper discusses integrating more than one technology in systems to achieve independency. The most common SF systems are remote monitoring, autonomous, and intelligent decision-making systems.

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Section: Introductionmentioning

confidence: 99%

Technologies Driving the Shift to Smart Farming: A Review

Elbeheiry

Balog

2023

IEEE Sensors J.

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“…IoT-enabled technologies signify the future of computing and communication in agriculture. The wireless sensor network is the dynamic strength technology that can transform the agriculture domain area from being fixed and labour-intensive to dynamic and smart and leads to boost the agriculture production with the least human efforts [4] [5] [6].…”

Section: Introductionmentioning

confidence: 99%

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Islam

Jamwal²,

Mir³

et al. 2022

Iraqi Journal of Science

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Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the apple disease prediction system concerning different types of disease found in the apple crop are discussed. In addition, this paper presents the contributions made by numerous researchers over the past few years in the apple disease prediction system. The aim of this research is to support the development of smart agriculture applications that would be helpful in precision farming for the optimization of resources with the help of IoT in agriculture and early disease prediction in apple crops.

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An Automatic Irrigation Based on Control Area Network (CAN) Protocol

Nimmagadda

2023

Natl. Acad. Sci. Lett.

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A Survey on IoT Based Agricultural Crop Growth Monitoring and Quality Control

Cited by 9 publications

References 22 publications

Technologies Driving the Shift to Smart Farming: A Review

Technologies Driving the Shift to Smart Farming: A Review

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

An Automatic Irrigation Based on Control Area Network (CAN) Protocol

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