Bibliometric Analysis of Trends in Smart Irrigation for Smart Agriculture

Yiyuan Pang, Francesco Marinello (), Pan Tang, Hong Li and Qi Liang
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Yiyuan Pang: Research Centre of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang 212013, China
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

Sustainability, 2023, vol. 15, issue 23, 1-23

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.

Keywords: cluster analysis; high-frequency phrase; Scopus; Internet of Things; irrigation system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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