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Predicting physiological traits of rice from hyperspectral data under CO2 and drought treatments
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  • To-Chia Ting,
  • Sylvia Park,
  • John J. Couture,
  • Diane R. Wang
To-Chia Ting

Corresponding Author:[email protected]

Author Profile
Sylvia Park
John J. Couture
Diane R. Wang


Using hyperspectral technology for high-throughput plant phenotyping is a potentially useful method in crop sciences. To examine its effectiveness, we collected leaf-level hyperspectral and ground-reference data from rice plants grown in controlled-environments under drought and CO2 treatments at Ag Alumni Seed Phenotyping Facility at Purdue University. By applying RReliefF, we found that short-wave infrared region (SWIR) was important for leaf water potential (LWP), near-infrared region was linked with specific leaf area (SLA) and both red-edge and SWIR regions were related to gas exchange traits (net assimilation [An], stomatal conductance to water vapor [gsw] and transpiration [Emm]). Based on those results, we found that LWP and SLA were moderately predictable and gas exchange traits were predictable (R2 \(\geq\) 0.60 and root mean squared error of prediction for An, gsw and Emmwere 7.706 \(\mu\)molm-2s-1, 0.282 molm-2s-1, and 3.906 mmolm-2s-1 in validation datasets, respectively) by using partial least squares regression. Furthermore, treatment effect on An from cross-validation predictions agreed with ground-reference data. In contrast, photosynthetic parameters (Vcmax and Jmax) could not be estimated from hyperspectral data. Hyperspectral data can provide potential insights about plant growth and water status. When the effect of treatments is pronounced, model predictions are consistent with ground-reference data.
30 Oct 2023Submitted to NAPPN 2024
30 Oct 2023Published in NAPPN 2024