ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Technical Advances in Plant Science
This article is part of the Research TopicPlant Phenotyping for AgricultureView all 33 articles
Biomass prediction and shoot growth characterization of single-staked yam plants using UAV imagery
Provisionally accepted
- 1Japan International Research Center for Agricultural Sciences (JIRCAS), Tsukuba, Japan
- 2International Institute of Tropical Agriculture, Ibadan, Nigeria
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
This study presents an unmanned aerial vehicle (UAV)-based approach for estimating shoot biomass and characterizing growth patterns in single-staked white Guinea yams (Dioscorea rotundata). Multi-angle aerial images from nadir and oblique views were used to extract vegetation-and height-related indices that served as predictors in machine learning models. Support vector regression using combined-view imagery provided the highest prediction accuracy (R² = 0.79) and remained robust across growth stages, years, fertilizer treatments, and genotypes. Notably, the combined-view configuration outperformed single-view imaging, demonstrating the advantage of capturing complementary canopy-structure information in complex staked-vine canopies. Time-series biomass estimates enabled the fitting of genotype-specific Richards growth curves using Bayesian inference. Significant genotypic variations were observed in parameters associated with maximum biomass and early growth rate, whereas phenology-related parameters showed comparatively minimal differences. These parameter differences may reflect variation in canopy architecture and growth allocation among genotypes. Overall, this integrated workflow provides a scalable tool for nondestructive monitoring of yam growth dynamics and for summarizing biomass trajectories with interpretable parameters, supporting breeding efforts aimed at improving yam productivity and yield stability across diverse cultivation conditions.
Keywords: canopy structure, multi-angle UAV imagery, Richards growth model, Shoot biomass, Support vector regression, Yam
Received: 27 Dec 2025; Accepted: 10 Feb 2026.
Copyright: © 2026 ISEKI, Matsumoto and Asfaw. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Kohtaro ISEKI
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.