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The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture

Author

Listed:
  • E. M. B. M. Karunathilake

    (Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea)

  • Anh Tuan Le

    (Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea)

  • Seong Heo

    (Department of Horticulture, Kongju National University, Yesan 32439, Republic of Korea)

  • Yong Suk Chung

    (Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea)

  • Sheikh Mansoor

    (Department of Plant Resources and Environment, Jeju National University, Jeju 63243, Republic of Korea)

Abstract

Precision agriculture employs cutting-edge technologies to increase agricultural productivity while reducing adverse impacts on the environment. Precision agriculture is a farming approach that uses advanced technology and data analysis to maximize crop yields, cut waste, and increase productivity. It is a potential strategy for tackling some of the major issues confronting contemporary agriculture, such as feeding a growing world population while reducing environmental effects. This review article examines some of the latest recent advances in precision agriculture, including the Internet of Things (IoT) and how to make use of big data. This review article aims to provide an overview of the recent innovations, challenges, and future prospects of precision agriculture and smart farming. It presents an analysis of the current state of precision agriculture, including the most recent innovations in technology, such as drones, sensors, and machine learning. The article also discusses some of the main challenges faced by precision agriculture, including data management, technology adoption, and cost-effectiveness.

Suggested Citation

  • E. M. B. M. Karunathilake & Anh Tuan Le & Seong Heo & Yong Suk Chung & Sheikh Mansoor, 2023. "The Path to Smart Farming: Innovations and Opportunities in Precision Agriculture," Agriculture, MDPI, vol. 13(8), pages 1-26, August.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:8:p:1593-:d:1215239
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    References listed on IDEAS

    as
    1. JaeYoung Kim & Chaewon Lee & Ji-Eun Park & Sheikh Mansoor & Yong Suk Chung & Kyunghwan Kim, 2023. "Drought Stress Restoration Frequencies of Phenotypic Indicators in Early Vegetative Stages of Soybean ( Glycine max L.)," Sustainability, MDPI, vol. 15(6), pages 1-17, March.
    2. Khanna, Abhishek & Kaur, Sanmeet, 2023. "An empirical analysis on adoption of precision agricultural techniques among farmers of Punjab for efficient land administration," Land Use Policy, Elsevier, vol. 126(C).
    3. Gaganpreet Singh Hundal & Chad Matthew Laux & Dennis Buckmaster & Mathias J Sutton & Michael Langemeier, 2023. "Exploring Barriers to the Adoption of Internet of Things-Based Precision Agriculture Practices," Agriculture, MDPI, vol. 13(1), pages 1-16, January.
    4. Guido S. Berger & Marco Teixeira & Alvaro Cantieri & José Lima & Ana I. Pereira & António Valente & Gabriel G. R. de Castro & Milena F. Pinto, 2023. "Cooperative Heterogeneous Robots for Autonomous Insects Trap Monitoring System in a Precision Agriculture Scenario," Agriculture, MDPI, vol. 13(2), pages 1-26, January.
    5. McFadden, Jonathan & Njuki, Eric & Griffin, Terry, 2023. "Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms," USDA Miscellaneous 333550, United States Department of Agriculture.
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    Cited by:

    1. Anastasios Michailidis & Chrysanthi Charatsari & Thomas Bournaris & Efstratios Loizou & Aikaterini Paltaki & Dimitra Lazaridou & Evagelos D. Lioutas, 2024. "A First View on the Competencies and Training Needs of Farmers Working with and Researchers Working on Precision Agriculture Technologies," Agriculture, MDPI, vol. 14(1), pages 1-12, January.
    2. Deniz Uztürk & Gülçin Büyüközkan, 2023. "Strategic Analysis for Advancing Smart Agriculture with the Analytic SWOT/PESTLE Framework: A Case for Turkey," Agriculture, MDPI, vol. 13(12), pages 1-25, December.
    3. Sergio Monteleone & Edmilson Alves de Moraes & Roberto Max Protil & Brenno Tondato de Faria & Rodrigo Filev Maia, 2024. "Proposal of a Model of Irrigation Operations Management for Exploring the Factors That Can Affect the Adoption of Precision Agriculture in the Context of Agriculture 4.0," Agriculture, MDPI, vol. 14(1), pages 1-33, January.
    4. Chrysanthi Charatsari & Anastasios Michailidis & Evagelos D. Lioutas & Thomas Bournaris & Efstratios Loizou & Aikaterini Paltaki & Dimitra Lazaridou, 2023. "Competencies Needed for Guiding the Digital Transition of Agriculture: Are Future Advisors Well-Equipped?," Sustainability, MDPI, vol. 15(22), pages 1-15, November.

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