Research Article | Open Access

Volume 2025 |Article ID 100045 | https://doi.org/10.1016/j.plaphe.2025.100045

High-Throughput Field Phenotyping Using Unmanned Aerial Vehicles (UAVs) for Rapid Estimation of Photosynthetic Traits

Jingshan Lu,^1,4 Qimo Qi,^1,4 Gangjun Zheng,^1,4 Jan U.H. Eitel,^2,3 Qiuyan Zhang,¹ Jiuyuan Zhang,¹ Sumei Chen,¹ Fei Zhang,¹ Weimin Fang,¹ Zhiyong Guan,¹ and Fadi Chen ¹

¹State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Landscaping, Ministry of Agriculture and Rural Affairs, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture, Nanjing Agricultural University, Nanjing, Jiangsu, 210095, China
²Department of Natural Resources and Society, College of Natural Resources, University of Idaho (UI), 875 Perimeter Drive, Moscow, ID, 83843, USA
³McCall Outdoor Science School, College of Natural Resources, University of Idaho, 1800 University Lane, McCall, ID, 83638, USA
⁴These authors contributed equally to this work

Abstract

Efficient measurement of photosynthetic traits, such as the maximum carboxylation rate of Rubisco (Vcmax) and electron transport rate (Jmax), is essential for advancing research and breeding aimed at enhancing crop productivity. Traditional methods are time-intensive, which limits their scalability. Remote sensing presents an opportunity for estimating these traits; however, it often lacks an affordable platform for effective spatial mapping, a critical aspect of phenotyping. This study explored the use of unmanned aerial vehicle (UAV) multispectral data to estimate and spatially map photosynthetic traits in tea chrysanthemums during the branching and budding stages under an open canopy. Over six field experiments across varieties conducted in 2022–2023, we captured canopy reflectance using UAV-mounted multispectral sensors, calculated spectral indices, and measured the photosynthetic traits of the upper leaves using a portable photosynthesis system. The results indicated that certain indices, particularly those incorporating green and red-edge bands, effectively estimated photosynthetic traits, with the simplified canopy chlorophyll content index (SCCCI) yielding the most accurate Vcmax estimates (R2 = 0.52) and the chlorophyll vegetation index (CVI) providing the best estimates for Jmax (R2 = 0.38). The integration of variable selection with partial least squares regression (PLSR) modeling further enhanced the precision of the model (Vcmax: R2 = 0.70; Jmax: R2 = 0.63). Our findings demonstrate that UAV-acquired multispectral data can effectively map photosynthetic traits with high spatial resolution, establishing it as a valuable tool for rapid phenotyping and spatial assessment of photosynthetic capacity in crop fields.