Proceedings of the 2nd International Conference on Social Environment Diversity (ICOSEND 2025)

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Transfer Learning in Data-Scarce Agricultural Yield Forecasting: A Bibliometric and Systematic Literature Review

Authors
Khoirudin Khoirudin1, *, Sri Yulianto Joko Prasetyo2, Sutarto Wijono2, Evi Maria2, Untung Rahardja3
1Information Technology, Universitas Semarang, Semarang, Indonesia
2Doctor of Computer Science, Satya Wacana University Salatiga, Salatiga, Indonesia
3Faculty of Science and Technology, Universitas Raharja, Tangerang, Indonesia
*Corresponding author. Email: khoirudin@usm.ac.id
Corresponding Author
Khoirudin Khoirudin
Available Online 30 April 2026.
DOI
10.2991/978-2-38476-565-2_56How to use a DOI?
Keywords
transfer learning; crop yield prediction; precision agriculture; systematic literature review; bibliometric analysis
Abstract

Transfer learning (TL) presents a viable approach to enhance the precision of agricultural yield forecasting in data-scarce settings. This study seeks to analyze the advancements, methodologies, and research deficiencies concerning the utilization of TL in agricultural yield forecasting via a Systematic Literature Review (SLR) and bibliometric analysis of 63 Scopus articles from 2020 to 2025. The study was performed with the PRISMA and PICOC frameworks, aided by Biblioshiny in RStudio. The study findings indicate a rising trend in publications beyond 2021, with the predominant transfer learning methodologies being fine-tuning, feature extraction, and domain adaptation utilizing pretrained convolutional neural networks. Research mostly employs satellite images (Sentinel-2) and focuses on nations including China, India, and the United States. Deficiencies were identified in spatial validation, multimodal data integration, and the examination of model security dimensions. This paper offers a literature review and strategic recommendations for advancing AI-driven precision agriculture in data-scarce environments.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2nd International Conference on Social Environment Diversity (ICOSEND 2025)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
30 April 2026
ISBN
978-2-38476-565-2
ISSN
2352-5398
DOI
10.2991/978-2-38476-565-2_56How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Khoirudin Khoirudin
AU  - Sri Yulianto Joko Prasetyo
AU  - Sutarto Wijono
AU  - Evi Maria
AU  - Untung Rahardja
PY  - 2026
DA  - 2026/04/30
TI  - Transfer Learning in Data-Scarce Agricultural Yield Forecasting: A Bibliometric and Systematic Literature Review
BT  - Proceedings of the 2nd International Conference on Social Environment Diversity (ICOSEND 2025)
PB  - Atlantis Press
SP  - 467
EP  - 477
SN  - 2352-5398
UR  - https://doi.org/10.2991/978-2-38476-565-2_56
DO  - 10.2991/978-2-38476-565-2_56
ID  - Khoirudin2026
ER  -