Artificial intelligence in postharvest agriculture: mapping a research agenda

Abstract

IntroductionThe implementation of artificial intelligence (AI) in postharvest agriculture has significantly improved in recent decades, thanks to extensive scientific research. The study aimed to identify research gaps and hotspots for future research based on keyword co-occurrence and clustering analyses, as well as to discuss the results and highlight the research trends.MethodsThis study analyses research trends in AI application in postharvest agriculture using novel scientometric tools such as the Bibliometrix R package, biblioshiny, and VosViewer. The research analysed 586 published papers on AI application in postharvest agriculture research between 1994 and September 2022, retrieved from the Scopus database.Results and discussionThe results showed that publications on AI applications in postharvest agriculture research have been increasing for almost 30 years, with significant growth in the subject area in the last decade. China, the USA, and India were found to be the top three most productive countries, accounting for 52.4%, 22%, and 18.6% of the total selected publications, respectively. The analysis also revealed that topics such as the Internet of Things, cold chain logistics, big data, decision-making, and real-time monitoring have low development degrees in the knowledge domain. This study demonstrated increased research on AI applications in postharvest agriculture, aiming to reduce postharvest losses, enhance food nutrition and quality, and mitigate food insecurity. It also provides valuable scientific references on AI applications in postharvest agriculture research for researchers and scholars. By identifying research gaps and hotspots, this study can guide future research in AI applications in postharvest agriculture to further improve the industry.