By integrating spectral indices with machine learning, the approach achieved strong predictive power while enabling spatial mapping across multiple varieties.
Photosynthesis underpins plant growth and crop productivity, making it a central focus for agricultural improvement. Two parameters—Vcmax and Jmax—define a plant’s photosynthetic capacity by regulating CO₂ fixation and electron transport during the Calvin cycle. Conventional measurements, based on gas-exchange curves, require 15–30 minutes per sample and are unsuitable for large breeding populations. Remote sensing has been used to monitor photosynthetic traits, but existing platforms—whether leaf-level hyperspectral sensors, airborne surveys, or satellites—are either costly, low-resolution, or inflexible. UAVs offer a unique opportunity, combining affordability, centimeter-scale spatial resolution, and operational efficiency. Based on these challenges, researchers sought to determine whether UAV multispectral data could provide a practical solution for real-world crop phenotyping.
A study (DOI: 10.1016/j.plaphe.2025.100045) published in Plant Phenomics on 26 April 2025 by Fadi Chen’s team, Nanjing Agricultural University, offers a powerful tool for crop phenotyping, paving the way for large-scale screening of photosynthetic efficiency and accelerating the development of high-yielding cultivars.
In this study, researchers employed UAV-mounted multispectral imaging to evaluate photosynthetic traits of tea chrysanthemums under varying nitrogen (N) levels and across multiple varieties. Using reflectance data collected from five spectral bands, they first characterized vegetation reflection patterns, which displayed the expected near-infrared dominance, followed by red-edge, green, red, and blue bands. Spectral indices known to be sensitive to chlorophyll and nitrogen, such as CIGreen, CIRE, and NDRE, increased consistently with higher N application, confirming their responsiveness. To refine accuracy, canopy spectra were analyzed with and without soil and shadow removal, with the latter showing stronger correlations with measured photosynthetic traits. Among 63 indices tested, the simplified canopy chlorophyll content index (SCCCI) was most strongly correlated with maximum carboxylation rate (Vcmax, r = 0.75), while the chlorophyll vegetation index (CVI) was best for maximum electron transport rate (Jmax, r = 0.66). Linear models achieved moderate predictive accuracy (R² = 0.52 for Vcmax; R² = 0.38 for Jmax), which improved substantially when integrating variable selection with partial least squares regression (PLSR). Using LASSO to filter redundant variables, nine and eighteen significant indices were identified for Vcmax and Jmax, respectively. The combined LASSO–PLSR model achieved higher validation accuracies (R² = 0.70 for Vcmax, R² = 0.63 for Jmax), with green and red-edge indices contributing most strongly. Importantly, both spectral index-based and PLSR-based models produced spatial maps of photosynthetic traits, displaying consistent patterns across nitrogen treatments and chrysanthemum varieties, though PLSR models provided more stable value ranges with lower error margins. Overall, UAV multispectral imaging, combined with machine learning, proved effective for high-resolution, cost-efficient estimation and mapping of photosynthetic traits in tea chrysanthemums, offering strong potential for phenotyping and crop improvement programs.
This UAV-based approach provides plant breeders and crop scientists with a practical tool for evaluating photosynthetic efficiency across large populations and diverse field conditions. By enabling rapid, spatially resolved assessments of Vcmax and Jmax, the platform accelerates the identification of high-efficiency cultivars and supports precision agriculture strategies. The method is cost-effective, flexible, and scalable, making it suitable not only for research but also for deployment in breeding stations and production fields. Beyond chrysanthemums, the framework can be applied to other crops, offering a pathway to improve photosynthetic capacity—a key driver of yield potential—while reducing reliance on time-consuming, manual gas-exchange measurements.
###
References
DOI
10.1016/j.plaphe.2025.100045
Original URL
https://doi.org/10.1016/j.plaphe.2025.100045
Funding information
This work was supported by the National Natural Science Foundation of China (32302593), Natural Science Fund of Jiangsu Province (BK20230996), the Jiangsu Funding Program for Excellent Postdoctoral Talent (2022ZB339), the Fellowship of China Postdoctoral Science Foundation (2022M721638), The“JBGS”Project of Seed Industry Revitalization in Jiangsu Province (JBGS (2021) 020), the Program for Key Research and Development, Jiangsu, China (Modern Agriculture) (BE2023367), the earmarked fund for Jiangsu Agricultural Industry Technology System.
About Plant Phenomics
Plant Phenomics is dedicated to publishing novel research that will advance all aspects of plant phenotyping from the cell to the plant population levels using innovative combinations of sensor systems and data analytics. Plant Phenomics aims also to connect phenomics to other science domains, such as genomics, genetics, physiology, molecular biology, bioinformatics, statistics, mathematics, and computer sciences. Plant Phenomics should thus contribute to advance plant sciences and agriculture/forestry/horticulture by addressing key scientific challenges in the area of plant phenomics.