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Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Author

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  • 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
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    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.
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