Hyperspectral multi-trait modeling for rapid assessment of rice photosynthetic light-response traits under field conditions
6 affiliations
- 1Institute of Agricultural Science and Technology Information, Shanghai Academy of Agricultural Sciences
- 2Key Laboratory of Intelligent Agricultural Technology (Yangtze River Delta), Ministry of Agriculture and Rural Affairs
- 3Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences
- 4Shanghai Engineering Research Centre of Low-carbon Agriculture (SERCLA)
- 5Key Laboratory of Low-carbon Green Agriculture in Southeastern China, Ministry of Agriculture and Rural Affairs
- 6Shanghai Spark-X Technology Co., Ltd.
Abstract
Light-response-curve (LRC) derived photosynthetic phenotyping traits, including the maximum net photosynthetic rate (PNmax), light-saturation point (Isat), light-compensation point (Icomp), and dark respiration rate (Rd), are important indicators of photosynthetic performance. However, conventional LI-6400-based measurements require tens of minutes per leaf, severely limiting high-throughput phenotyping and field-based stress monitoring. Here, we developed a hyperspectral approach that simultaneously estimates four key LRC-derived photosynthetic traits (PNmax, Isat, Icomp, and Rd) from leaf hyperspectral data. The framework was evaluated using observations collected under field conditions encompassing three years, three rice varieties, four nitrogen levels, three irrigation treatments, three leaf positions, and multiple measurement dates. Partial Least Squares Regression (PLSR) and machine learning models—including Backpropagation Artificial Neural Network (BP-ANN), Convolutional Neural Networks (CNN), and Support Vector Regression (SVR)—were tested with full spectrum and variable selection methods (Competitive Adaptive Reweighted Sampling, CARS; Successive Projections Algorithm, SPA; and Uninformative Variable Elimination, UVE). Variable selection generally improved model performance, with CARS providing the most stable overall results across the four traits. Multi-trait (MT) models outperformed single-trait (ST) counterparts, particularly on the held-out test set. Among all methods, nonlinear models achieved higher accuracy for individual traits. PLSR2+CARS provided the most consistent overall predictive performance across the four traits, achieving test-set R ² values of 0.84, 0.69, 0.52, and 0.59 and RMSE values of 2.53 μmol (CO ₂ ) m ⁻² s ⁻¹ , 344.08 μmol (photon) m ⁻² s ⁻¹ , 11.48 μmol (photon) m ⁻² s ⁻¹ , and 0.58 μmol (CO ₂ ) m ⁻² s ⁻¹ for PNmax, Isat, Icomp, and Rd, respectively. These results indicate that leaf hyperspectral sensing combined with MT modeling has potential for high-throughput assessment of rice photosynthetic performance at the leaf level under field conditions.
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BibTeX
@article{yao2026hyperspectral,
title = {{Hyperspectral multi-trait modeling for rapid assessment of rice photosynthetic light-response traits under field conditions}},
author = {Xinfeng Yao and Xiuguo Zheng and Huifeng Sun and Sheng Zhou and Wei Wang and Linyi Li},
journal = {Frontiers in Plant Science},
year = {2026},
month = oct,
doi = {10.3389/fpls.2026.1968739},
url = {https://www.frontiersin.org/articles/10.3389/fpls.2026.1968739/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Hyperspectral multi-trait modeling for rapid assessment of rice photosynthetic light-response traits under field conditions
AU - Xinfeng Yao
AU - Xiuguo Zheng
AU - Huifeng Sun
AU - Sheng Zhou
AU - Wei Wang
AU - Linyi Li
PY - 2026
DA - 2026/10/13
JO - Frontiers in Plant Science
DO - 10.3389/fpls.2026.1968739
UR - https://www.frontiersin.org/articles/10.3389/fpls.2026.1968739/full
AB - Light-response-curve (LRC) derived photosynthetic phenotyping traits, including the maximum net photosynthetic rate (PNmax), light-saturation point (Isat), light-compensation point (Icomp), and dark respiration rate (Rd), are important indicators of photosynthetic performance. However, conventional LI-6400-based measurements require tens of minutes per leaf, severely limiting high-throughput phenotyping and field-based stress monitoring. Here, we developed a hyperspectral approach that simultaneously estimates four key LRC-derived photosynthetic traits (PNmax, Isat, Icomp, and Rd) from leaf hyperspectral data. The framework was evaluated using observations collected under field conditions encompassing three years, three rice varieties, four nitrogen levels, three irrigation treatments, three leaf positions, and multiple measurement dates. Partial Least Squares Regression (PLSR) and machine learning models—including Backpropagation Artificial Neural Network (BP-ANN), Convolutional Neural Networks (CNN), and Support Vector Regression (SVR)—were tested with full spectrum and variable selection methods (Competitive Adaptive Reweighted Sampling, CARS; Successive Projections Algorithm, SPA; and Uninformative Variable Elimination, UVE). Variable selection generally improved model performance, with CARS providing the most stable overall results across the four traits. Multi-trait (MT) models outperformed single-trait (ST) counterparts, particularly on the held-out test set. Among all methods, nonlinear models achieved higher accuracy for individual traits. PLSR2+CARS provided the most consistent overall predictive performance across the four traits, achieving test-set R ² values of 0.84, 0.69, 0.52, and 0.59 and RMSE values of 2.53 μmol (CO ₂ ) m ⁻² s ⁻¹ , 344.08 μmol (photon) m ⁻² s ⁻¹ , 11.48 μmol (photon) m ⁻² s ⁻¹ , and 0.58 μmol (CO ₂ ) m ⁻² s ⁻¹ for PNmax, Isat, Icomp, and Rd, respectively. These results indicate that leaf hyperspectral sensing combined with MT modeling has potential for high-throughput assessment of rice photosynthetic performance at the leaf level under field conditions.
SN - 1664-462X
ER -