Optimization and Prediction of Crop Yields with Machine Learning: A Bibliometric Analysis

Publication Type

Conference Proceeding

Name of Author

• Daniel Dzarma Ezra, Computer Science and Operations Research Department, IMSP Dangbo, University of Abomey, Calavi (UAC), Dangbo, Benin
• Theophile Komlan Dagba, National School of Applied Economics and Management, University of Abomey-Calavi (UAC), Cotonou, Benin
• Guy Degla, Operations Research Department, IMSP Dangbo, University of Abomey-Calavi, Dangbo, Benin

Publication Date (Issue Year)

2025

Journal Name

Emerging Technologies for Developing Countries

Abstract

Agriculture is a critical sector in Africa, significantly contributing to the continent’s economy, livelihoods, and food security. This study aims to determine the impact that different countries, publishing houses, and authors have made thus far, as well as the present level of research in crop yields prediction and optimization using machine learning. Meanwhile, Bibliometric data were gathered from the Dimension’s database by the filtered search for the study “Optimization and prediction of crop yields with machine learning: a bibliometric analysis”. The search field included “sustainable development objectives” “applied economics”, “agriculture”, “artificial intelligence”, and “machine learning” between the years 2003 and 2022. The publication types taken into consideration were articles, book chapters, and conference proceedings. The data were examined using the software VOSviewer and Bibliometrix. The analysis performed by the bibliometrix software showed that 2382 bibliometric documents were downloaded, the average age of the documents was 2.62 years, the average number of citations per document was 20, the total number of authors was 8714, the average number of single-authored documents was 138, and the average number of co-authors per document was 7, Wang Y. who had 9 h-index journal is most globally relevant author while China who had 801 and 5 total citation and average citation was the most relevant country. The VOSviewer results revealed that “Computers and electronics in agriculture” was the most referenced Journal Production organization with 5448 total citations.

Keywords

Optimization, Prediction, Crop Yields, Machine Learning, Bibliometric, Analysis

Rsif Scholar Name

Daniel Dzarma Ezra

Rsif Scholar Nationality

Nigeria

Cohort

Cohort 4

Thematic Area

ICTs Including Big Data and Artificial Intelligence

Africa Host University (AHU)

Université d'Abomey-Calavi, Benin

Funding Statement

We wish to acknowledge the management of Université d’Abomey Calavi for giving us the platform to carry out research and Regional Scholarship and Innovation Fund (RSIF) for sponsoring the publication

Recommended Citation

Ezra, D. D., Dagba, T. K., & Degla, G. (2025). Optimization and Prediction of Crop Yields with Machine Learning: A Bibliometric Analysis. Emerging Technologies for Developing Countries https://doi.org/10.1007/978-3-031-93557-2_15