IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i23p16420-d1290743.html
   My bibliography  Save this article

Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Author

Listed:
  • Yiyuan Pang

    (Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
    Department of Land, Environment, Agriculture and Forestry, University of Padua, 35020 Legnaro, Italy)

  • Francesco Marinello

    (Department of Land, Environment, Agriculture and Forestry, University of Padua, 35020 Legnaro, Italy)

  • Pan Tang

    (Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China)

  • Hong Li

    (Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China)

  • Qi Liang

    (Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China)

Abstract

Agriculture is considered one of the most critical sectors that play a strategic role in ensuring food security. It is directly related to human development and social stability. The agricultural sector is currently incorporating new technologies from other areas. These phenomena are smart agriculture and smart irrigation. However, a challenge to research is the integration of technologies from different knowledge fields, which has caused theoretical and practical difficulties. Thus, our purpose in this study has been to understand the core of these two themes. We extracted publications in Scopus and used bibliometric methods for high-frequency word and phrase analysis. Research shows that current research on smart agriculture mainly focuses on the Internet of Things, climate change, machine learning, precision agriculture and wireless sensor networks. Simultaneously, the Internet of Things, irrigation systems, soil moisture, wireless sensor networks and climate change have received the most scholarly attention in smart irrigation. This study used cluster analysis to find that the IoT has the most apparent growth rate in smart agriculture and smart irrigation, with five-year growth rates of 1617% and 2285%, respectively. In addition, machine learning, deep learning and neural networks have enormous potential in smart irrigation compared with smart agriculture.

Suggested Citation

  • Yiyuan Pang & Francesco Marinello & Pan Tang & Hong Li & Qi Liang, 2023. "Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture," Sustainability, MDPI, vol. 15(23), pages 1-23, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16420-:d:1290743
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/23/16420/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/23/16420/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liu, Shouying & Ma, Sen & Yin, Lijuan & Zhu, Jiong, 2023. "Land titling, human capital misallocation, and agricultural productivity in China," Journal of Development Economics, Elsevier, vol. 165(C).
    2. Domínguez-Niño, Jesús María & Oliver-Manera, Jordi & Girona, Joan & Casadesús, Jaume, 2020. "Differential irrigation scheduling by an automated algorithm of water balance tuned by capacitance-type soil moisture sensors," Agricultural Water Management, Elsevier, vol. 228(C).
    3. Stambouli, T. & Faci, J.M. & Zapata, N., 2014. "Water and energy management in an automated irrigation district," Agricultural Water Management, Elsevier, vol. 142(C), pages 66-76.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Conesa, María R. & Conejero, Wenceslao & Vera, Juan & Agulló, Vicente & García-Viguera, Cristina & Ruiz-Sánchez, M. Carmen, 2021. "Irrigation management practices in nectarine fruit quality at harvest and after cold storage," Agricultural Water Management, Elsevier, vol. 243(C).
    2. Xueqin Jiang & Shanjun Luo & Qin Ye & Xican Li & Weihua Jiao, 2022. "Hyperspectral Estimates of Soil Moisture Content Incorporating Harmonic Indicators and Machine Learning," Agriculture, MDPI, vol. 12(8), pages 1-17, August.
    3. Jiménez-Aguirre, M.T. & Isidoro, D., 2018. "Hydrosaline Balance in and Nitrogen Loads from an irrigation district before and after modernization," Agricultural Water Management, Elsevier, vol. 208(C), pages 163-175.
    4. Tarjuelo, José M. & Rodriguez-Diaz, Juan A. & Abadía, Ricardo & Camacho, Emilio & Rocamora, Carmen & Moreno, Miguel A., 2015. "Efficient water and energy use in irrigation modernization: Lessons from Spanish case studies," Agricultural Water Management, Elsevier, vol. 162(C), pages 67-77.
    5. Li, Shengping & Tan, Deshui & Wu, Xueping & Degré, Aurore & Long, Huaiyu & Zhang, Shuxiang & Lu, Jinjing & Gao, Lili & Zheng, Fengjun & Liu, Xiaotong & Liang, Guopeng, 2021. "Negative pressure irrigation increases vegetable water productivity and nitrogen use efficiency by improving soil water and NO3–-N distributions," Agricultural Water Management, Elsevier, vol. 251(C).
    6. Sheng Dai & Timo Kuosmanen & Zhiqiang Liao, 2024. "Economic growth of cities: Does resource allocation matter?," Papers 2410.04918, arXiv.org.
    7. Playán, Enrique & Salvador, Raquel & Bonet, Luis & Camacho, Emilio & Intrigliolo, Diego S. & Moreno, Miguel A. & Rodríguez-Díaz, Juan A. & Tarjuelo, José M. & Madurga, Cristina & Zazo, Teresa & Sánche, 2018. "Assessing telemetry and remote control systems for water users associations in Spain," Agricultural Water Management, Elsevier, vol. 202(C), pages 89-98.
    8. Martínez-Romero, A. & López-Urrea, R. & Montoya, F. & Pardo, J.J. & Domínguez, A., 2021. "Optimization of irrigation scheduling for barley crop, combining AquaCrop and MOPECO models to simulate various water-deficit regimes," Agricultural Water Management, Elsevier, vol. 258(C).
    9. Zapata, N. & Playán, E. & Castillo, R. & Gimeno, Y. & Oliván, I. & Jiménez, A. & Carbonell, X. & Fábregas, M. & López-Pardo, J.R. & Vicente, L.M. & Millán, J. & Solano, D. & Lorenzo, M.A., 2020. "A methodology to classify irrigated areas: Application to the central Ebro River Basin in Aragón (Spain)," Agricultural Water Management, Elsevier, vol. 241(C).
    10. Giacomo Ferrarese & Alessandro Pagano & Umberto Fratino & Stefano Malavasi, 2021. "Improving Operation of Pressurized Irrigation Systems by an Off-grid Control Devices Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(9), pages 2813-2827, July.
    11. Eltarabily, Mohamed Galal & Mohamed, Abdelmoneim Zakaria & Begna, Sultan & Wang, Dong & Putnam, Daniel H. & Scudiero, Elia & Bali, Khaled M., 2024. "Simulated soil water distribution patterns and water use of Alfalfa under different subsurface drip irrigation depths," Agricultural Water Management, Elsevier, vol. 293(C).
    12. Puig-Sirera, Àngela & Provenzano, Giuseppe & González-Altozano, Pablo & Intrigliolo, Diego S. & Rallo, Giovanni, 2021. "Irrigation water saving strategies in Citrus orchards: Analysis of the combined effects of timing and severity of soil water deficit," Agricultural Water Management, Elsevier, vol. 248(C).
    13. Lulin Shen & Fang Wang, 2024. "Can Migrant Workers Returning Home for Entrepreneurship Increase Agricultural Labor Productivity: Evidence from a Quasi-Natural Experiment in China," Agriculture, MDPI, vol. 14(6), pages 1-17, June.
    14. Bwambale, Erion & Abagale, Felix K. & Anornu, Geophrey K., 2022. "Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review," Agricultural Water Management, Elsevier, vol. 260(C).
    15. Tuanbiao Jiang & Min Zhong & Anrong Gao & Guoqun Ma, 2024. "Do Factor Misallocations Affect Food Security? Evidence from China," Agriculture, MDPI, vol. 14(5), pages 1-17, May.
    16. Saman Rabiei & Ehsan Jalilvand & Massoud Tajrishy, 2021. "A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data," Sustainability, MDPI, vol. 13(20), pages 1-17, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16420-:d:1290743. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.