Open Access

ARTICLE

Limitation of RGB-Derived Vegetation Indices Using UAV Imagery for Biomass Estimation during Buckwheat Flowering

E. M. B. M. Karunathilake^1,#, Thanh Tuan Thai^1,2,3,#, Sheikh Mansoor¹, Anh Tuan Le^3,4, Faheem Shehzad Baloch^1,5, Yong Suk Chung^1,*, Dong-Wook Kim^6,*

1 Department of Plant Resources and Environment, Jeju National University, Jeju, 63243, Republic of Korea
2 Multimedia Communications Laboratory, University of Information Technology, Ho Chi Minh City, 700000, Vietnam
3 Vietnam National University, Ho Chi Minh City, 700000, Vietnam
4 Faculty of Biology—Biotechnology, University of Science, Ho Chi Minh City, 700000, Vietnam
5 Department of Biotechnology, Faculty of Science, Mersin University, Yenişehir, Mersin, 33343, Türkiye
6 Department of Smart Farm Engineering, College of Industrial Sciences, Kongju National University, Yesan-gun, 32439, Republic of Korea

* Corresponding Authors: Yong Suk Chung. Email: ; Dong-Wook Kim. Email:
# These authors contributed equally to this work

(This article belongs to the Special Issue: Application of Digital Agriculture and Machine Learning Technologies in Crop Production)

Phyton-International Journal of Experimental Botany 2025, 94(7), 2215-2228. https://doi.org/10.32604/phyton.2025.067439

Received 03 May 2025; Accepted 30 June 2025; Issue published 31 July 2025

Abstract

Accurate and timely estimation of above-ground biomass is crucial for understanding crop growth dynamics, optimizing agricultural input management, and assessing productivity in sustainable farming practices. However, conventional biomass assessments are destructive and resource-intensive. In contrast, remote sensing techniques, particularly those utilizing low-altitude unmanned aerial vehicles, provide a non-destructive approach to collect imagery data on plant canopy features, including spectral reflectance and structural details at any stage of the crop life cycle. This study explores the potential visible-light-derived vegetative indices to improve biomass prediction during the flowering period of buckwheat (Fagopyrum tataricum). Red, green, and blue (RGB) images of buckwheat were acquired during peak flowering, using a DJI P4 multispectral Drone. From the analysis of those images, four vegetative indices were calculated. Aboveground fresh biomass was harvested and measured on 14 September 2024. The results showed negative correlations between the green-band based excess green (ExG), excess green minus excess red (ExGR), and green leaf index (GLI) indices and the fresh above-ground biomass of buckwheat, while the red band-based excess red (ExR) index showed an insignificant positive correlation at p < 0.10. An investigation into green-band-based vegetation indices (VIs) for estimating fresh biomass revealed significant negative correlations during the experimental period. This unexpected inverse relationship is attributed to spectral interference from abundant white flowers during the flowering stage, where the high reflectance of white petals masked the green vegetation signal. Consequently, these green-band VIs demonstrated limited predictive power for biomass under such conditions, indicating that their utility is compromised when floral reflectance is dominant. Therefore, we suggest that further experiments are required to validate this relationship and improve the estimation of fresh above-ground biomass in white-flowered buckwheat plants.

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