Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

Hongkui Zhou, Fudeng Huang, Weidong Lou, Qing Gu, Ziran Ye, Hao Hu and Xiaobin Zhang

Agricultural Systems, 2025, vol. 223, issue C

Abstract: Predicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability.

Keywords: UAV; Yield prediction; Multispectral imaging; Deep learning; Rice breeding (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0308521X24003640
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:agisys:v:223:y:2025:i:c:s0308521x24003640

DOI: 10.1016/j.agsy.2024.104214

Access Statistics for this article

Agricultural Systems is currently edited by J.W. Hansen, P.K. Thornton and P.B.M. Berentsen

More articles in Agricultural Systems from Elsevier
Bibliographic data for series maintained by Catherine Liu ().