The evolution of Artificial Intelligence in agriculture: A biblio-metric analysis
DOI:
https://doi.org/10.14719/pst.5990Keywords:
computer vision, crop management, deep learning, sustainability, systematic reviewAbstract
Artificial Intelligence (AI) has emerged as a revolutionary force, fundamentally reshaping conventional practices and opening new avenues for growth across various sectors. In agriculture, AI is transforming practices by addressing key challenges such as soil health, maximising production and alleviating labour shortages. AI helps farmers gain insights into crop management, optimise resources and improve efficiency. However, high initial costs and the need for specialised knowledge pose barriers, particularly for small-scale farmers. Despite these challenges, the future of AI in agriculture appears promising, with advancements in autonomous systems and AI-driven precision farming poised to boost productivity and sustainability. This systematic review evaluates AI implementation in agriculture over the past decade through a bibliometric analysis of 70 research papers from the Scopus database. It highlights contributions such as computer vision and deep learning, which enhance crop management by enabling real-time health monitoring, early disease detection and data-driven decisions that boost yields. The bibliometric analysis also explores co-authorship networks, illustrating collaborative efforts among researchers and institutions in the agricultural domain. The analysis of annual research patterns reveals a steady increase in AI-related publications, reflecting a growing interest and investment in this field. Furthermore, the assessment of global scientific outputs underscores the widespread adoption of AI technologies, highlighting their potential to revolutionise agriculture and contribute to food security in an era of increasing demand. Overall, this review illustrates the dynamic nature of AI in agriculture and its promising future.
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