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A Survey on Intelligent Agricultural Information Handling Methodologies

Author

Listed:
  • Yorghos Voutos

    (Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • Phivos Mylonas

    (Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • John Katheniotis

    (Department of Informatics, Ionian University, 49132 Corfu, Greece)

  • Anastasia Sofou

    (Department of Informatics, Ionian University, 49132 Corfu, Greece
    School of Electrical & Computer Engineering, National Technical University of Athens, 15773 Athens, Greece)

Abstract

The term intelligent agriculture , or smart farming , typically involves the incorporation of computer science and information technologies into the traditional notion of farming. The latter utilizes plain machinery and equipment used for many decades and the only significant improvement made over the years has been the introduction of automation in the process. Still, at the beginning of the new century, there are ways and room for further vast improvements. More specifically, the low cost of rather advanced sensors and small-scale devices, now even connected to the Internet of Things (IoT), allowed them to be introduced in the process and used within agricultural production systems. New and emerging technologies and methodologies, like the utilization of cheap network storage, are expected to advance this development. In this sense, the main goals of this paper may be summarized as follows: (a) To identify, group, and acknowledge the current state-of-the-art research knowledge about intelligent agriculture approaches, (b) to categorize them according to meaningful data sources categories, and (c) to describe current efficient data processing and utilization aspects from the perspective of the main trends in the field.

Suggested Citation

  • Yorghos Voutos & Phivos Mylonas & John Katheniotis & Anastasia Sofou, 2019. "A Survey on Intelligent Agricultural Information Handling Methodologies," Sustainability, MDPI, vol. 11(12), pages 1-23, June.
  • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3278-:d:239672
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    References listed on IDEAS

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    1. Evangelos Anastasiou & Athanasios Balafoutis & Nikoleta Darra & Vasileios Psiroukis & Aikaterini Biniari & George Xanthopoulos & Spyros Fountas, 2018. "Satellite and Proximal Sensing to Estimate the Yield and Quality of Table Grapes," Agriculture, MDPI, vol. 8(7), pages 1-17, June.
    2. Alessandro Matese & Salvatore Filippo Di Gennaro, 2018. "Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture," Agriculture, MDPI, vol. 8(7), pages 1-13, July.
    3. Dong Tian & Min Zhang & Xuejian Wei & Jing Wang & Weisong Mu & Jianying Feng, 2018. "GIS-Based Energy Consumption and Spatial Variation of Protected Grape Cultivation in China," Sustainability, MDPI, vol. 10(9), pages 1-21, September.
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    Cited by:

    1. Salinee Santiteerakul & Apichat Sopadang & Korrakot Yaibuathet Tippayawong & Krisana Tamvimol, 2020. "The Role of Smart Technology in Sustainable Agriculture: A Case Study of Wangree Plant Factory," Sustainability, MDPI, vol. 12(11), pages 1-13, June.
    2. Michał Cupiał & Zbigniew Kowalczyk, 2020. "Optimization of Selection of the Machinery Park in Sustainable Agriculture," Sustainability, MDPI, vol. 12(4), pages 1-15, February.

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