Early-Season Crop Yield Prediction Using Multi-Sensor Data Fusion: A Review of Emerging Techniques

DOI

10.2991/978-94-6239-791-0_20How to use a DOI?

Keywords

Crop Yield Prediction; NDVI; DL; Data Fusion

Abstract

Crop yield prediction contributes significantly in food security and managing agricultural resources. For larger field area it is practically impossible for field based observations for crop yield estimation. From the last few years remote sensing and geographic information system proves to be prominent in crop yield prediction for large scale area. This review article offers systematic review of multi sensor fusion method for crop yield prediction in early season of crop. This review paper is structured according PRISMA framework to study different satellite imagery with different sensors like Sentinel-2A, Landsat etc. Also focuses on climatic measures and soil data for understanding crop dynamics related to crop yield. Diverse fusion level strategies for fusions like data level fusion, feature level fusion and model level fusion are studied. Also, machine learning techniques like random forest, support vector machine, long short term memory (LSTM) techniques are identified which show good results. Temporal analysis with vegetation indices like NDVI, EVI, SAVI etc. highlights the potential crop yield prediction in the early season. Also, future scope highlights the need of fusion approach in crop yield estimation. In summary this systematic review address different data fusion methods and scalable modeling techniques with future scope.

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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TY  - CONF
AU  - Nilesh Khisty
AU  - Mohammad Muqeem
PY  - 2026
DA  - 2026/10/09
TI  - Early-Season Crop Yield Prediction Using Multi-Sensor Data Fusion: A Review of Emerging Techniques
BT  - Proceedings of the International Conference on Artificial Intelligence and Machine Learning for Agro-Ecosystems (ICAIML-AE 2026)
PB  - Atlantis Press
SP  - 250
EP  - 263
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-791-0_20
DO  - 10.2991/978-94-6239-791-0_20
ID  - Khisty2026
ER  -