Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis

Abstract

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000–2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000–2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI’s role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

Thematic analysis of articles on artificial intelligence with spine trauma, vertebral metastasis, and osteoporosis using chord diagrams: A systematic review and meta-analysis

“…As part of bibliometric analyses, documents can be categorized based on a theme (e.g., osteoporosis, fracture, and deformity), a topical entity (e.g., author or affiliated country), and a characteristic (e.g., citations and publications). Although numerous AI-related bibliographical studies have been conducted, [1,2,[7][8][9][10][11][12] cluster classifications based on themes (or topics) are still lacking.…”

“…Artificial intelligence (AI) refers to computer algorithms designed to mimic and augment human behavior and thought patterns. [ 1 ] Over the past several years, artificial intelligence has been highlighted across a variety of technical industries. Currently, technical terms such as artificial neural networks (ANNs), convolutional neural networks, deep learning, machine learning, and big data are commonly used in everyday conversation.…”

Crop Monitoring in Smallholder Farms Using Unmanned Aerial Vehicles to Facilitate Precision Agriculture Practices: A Scoping Review and Bibliometric Analysis

“…The techniques involved in bibliometric and scientometric research, such as mapping, clustering, cocitation, and co-occurrence analyses, are commonly utilized to gain a greater perspective on a particular research topic [6,31]. The use of tools such as Biblioshiny and VOSviewer has become a common practice among researchers to facilitate this process [32][33][34] as they allow for trends to be more easily identified and visualized by (i) highlighting the research that has shaped our understanding of a particular research focus area, (ii) identifying key themes within the literature, (iii) illuminating the links between these themes, and (iv) exploring their evolution over time [6,30,35,36].…”

Learning from global research to analyze contributing factors in rice yield gap: Bibliometric approach towards Indonesia’s self-sufficiency

“…Several studies have applied this method to investigate a diverse range of topics, such as a bibliometric approach to solid waste in Iran (Mesdaghinia et al, 2015), global research trends in spinal ultrasound (Zhai et al, 2017), research spotlights in China (Hu et al, 2017), nanotechnology (Muñoz-Écija et al, 2017, human health through remote sensing applications (Viana et al, 2017), Covid-19 research (Hamidah et al, 2020) and the bioavailability of pesticide (Andreo-Martínez et al, 2020). In agriculture, bibliometrics has been successful in identifying key areas of interest in research of artificial intelligence and sustainable agriculture (Bhagat et al, 2022), in providing study recommendations for practitioners and scientists in the implementation of deep learning approach (Coulibaly et al, 2022) and in summarizing drone research in agriculture and suggesting future research directions (Rejeb et al, 2022). Using bibliometric analysis, these studies have provided valuable insights into the current state of research in their respective fields and identified future research opportunities.…”